Learning Machine-Learning & deep learning
This repository containes my paper reading notes on machine learning and deep learning.
Paper notes
This repository contains my paper reading notes on AI and computer vision. It is inspired by Denny Britz and Daniel Takeshi. A minimalistic webpage generated with Github io can be found here.
What to read
If you are new to deep learning in computer vision and don’t know where to start, I suggest you spend your first month or so dive deep into this list of papers. I did so (see my notes) and it served me well.
Here is a list of trustworthy sources of papers in case I ran out of papers to read.
My review posts by topics
I regularly update my blog in Toward Data Science.
2023-12 (1)
- ChatGPT for Robotics: Design Principles and Model Abilities [Notes] [Microsoft, LLM for robotics]
- RoboVQA: Multimodal Long-Horizon Reasoning for Robotics [Notes] [Google DeepMind, LLM for robotics]
- ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Application [Microsoft Robotics]
- GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstration [Notes] [LLM for robotics, Microsoft Robotics]
- LLM-Brain: LLM as A Robotic Brain: Unifying Egocentric Memory and Control [Notes]
- Language to Rewards for Robotic Skill Synthesis
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent [UM]
- LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action [Sergey Levine]
- A Survey of Embodied AI: From Simulators to Research Tasks IEEE TETCI 2021
- Habitat Challenge 2021
- Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions
- DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment [Jianyu Chen]
- The Power of Scale for Parameter-Efficient Prompt Tuning EMNLP 2021
- Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents ICML 2022
- ProgPrompt: Generating Situated Robot Task Plans using Large Language Models ICRA 2023
- CLIPort: What and Where Pathways for Robotic Manipulation CoRL 2021
- Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation CoRL 2022
- LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale NeurIPS 2022 [LLM Quant]
- AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration [Song Han, LLM Quant]
- RoFormer: Enhanced Transformer with Rotary Position Embedding
- CoDi: Any-to-Any Generation via Composable Diffusion NeurIPS 2023
- What if a Vacuum Robot has an Arm? UR 2023
- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
- GPT in 60 Lines of NumPy
- Speeding up the GPT - KV cache
- LLM Parameter Counting
- Transformer Inference Arithmetic
- ALBEF: Align before Fuse: Vision and Language Representation Learning with Momentum Distillation NeurIPS 2021 [Junnan Li]
- BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation ICML 2022 [Junnan Li]
- BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models [Junnan Li]
- MOO: Open-World Object Manipulation using Pre-trained Vision-Language Models [Google Robotics, end-to-end visuomotor]
- VC-1: Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
- CLIPort: What and Where Pathways for Robotic Manipulation CoRL 2021 [Nvidia, end-to-end visuomotor]
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers ICLR 2023
- SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models ICML 2023 [Song Han, LLM Quant]
- SAPIEN: A SimulAted Part-based Interactive ENvironment CVPR 2020
- FiLM: Visual Reasoning with a General Conditioning Layer AAAI 2018
- TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? NeurIPS 2021
- MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge NeurIPS 2022 [Outstanding paper award]
- QLoRA: Efficient Finetuning of Quantized LLMs
- OVO: Open-Vocabulary Occupancy
- Code Llama: Open Foundation Models for Code
- Chinchilla: Training Compute-Optimal Large Language Models [DeepMind]
- GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
- RoFormer: Enhanced Transformer with Rotary Position Embedding
- RH20T: A Robotic Dataset for Learning Diverse Skills in One-Shot
- Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation
- VIMA: General Robot Manipulation with Multimodal Prompts
- An Attention Free Transformer [Apple]
- PDDL Planning with Pretrained Large Language Models [MIT, Leslie Kaelbling]
- Task and Motion Planning with Large Language Models for Object Rearrangement IROS 2023
2023-09 (3)
2023-08 (3)
2023-07 (6)
2023-06 (5)
2023-05 (7)
2023-04 (1)
2023-03 (5)
2023-02 (4)
2023-01 (2)
2022-11 (1)
2022-10 (1)
2022-09 (3)
2022-08 (1)
2022-07 (8)
2022-06 (3)
2022-03 (1)
2022-02 (1)
2022-01 (1)
2021-12 (5)
2021-11 (4)
2021-10 (3)
2021-09 (11)
2021-08 (11)
2021-07 (1)
2021-06 (2)
2021-04 (5)
2021-03 (4)
2021-01 (7)
2020-12 (17)
- DeFCN: End-to-End Object Detection with Fully Convolutional Network [Notes] [Transformer, DETR]
- OneNet: End-to-End One-Stage Object Detection by Classification Cost [Notes] [Transformer, DETR]
- Traffic Light Mapping, Localization, and State Detection for Autonomous Vehicles [Notes] ICRA 2011 [traffic light, Sebastian Thrun]
- Towards lifelong feature-based mapping in semi-static environments [Notes] ICRA 2016
- How to Keep HD Maps for Automated Driving Up To Date [Notes] ICRA 2020 [BMW]
- Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection [Notes] CVPR 2021 [focal loss]
- Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning [Notes] CVPR 2018 workshop
- Centroid Voting: Object-Aware Centroid Voting for Monocular 3D Object Detection [Notes] IROS 2020 [mono3D, geometry + appearance = distance]
- Monocular 3D Object Detection in Cylindrical Images from Fisheye Cameras [Notes] [GM Israel, mono3D]
- DeepPS: Vision-Based Parking-Slot Detection: A DCNN-Based Approach and a Large-Scale Benchmark Dataset TIP 2018 [Parking slot detection, PS2.0 dataset]
- PSDet: Efficient and Universal Parking Slot Detection [Notes] IV 2020 [Zongmu, Parking slot detection]
- PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning [Notes] ASPLOS 2020 [pruning]
- Scaled-YOLOv4: Scaling Cross Stage Partial Network [Notes] [yolo]
- Yolov5 by Ultralytics [Notes] [yolo, spatial2channel]
- PP-YOLO: An Effective and Efficient Implementation of Object Detector [Notes] [yolo, paddle-paddle, baidu]
- PointPainting: Sequential Fusion for 3D Object Detection [Notes] [nuscenece]
- MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps [Notes] CVPR 2020 [Unseen moving objects, BEV]
- Locating Objects Without Bounding Boxes [Notes] CVPR 2019 [weighted Haussdorf distance, NMS-free]
2020-11 (18)
- TSP: Rethinking Transformer-based Set Prediction for Object Detection [Notes] ICCV 2021 [DETR, transformers, Kris Kitani]
- Sparse R-CNN: End-to-End Object Detection with Learnable Proposals [Notes] CVPR 2020 [DETR, Transformer]
- Unsupervised Monocular Depth Learning in Dynamic Scenes [Notes] CoRL 2020 [LearnK improved ver, Google]
- MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time [Notes] ICML 2020 [Mono3D, pairwise relationship]
- Argoverse: 3D Tracking and Forecasting with Rich Maps [Notes] CVPR 2019 [HD maps, dataset, CV lidar]
- The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes [Notes] ICRA 2019
- Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection CVPRW 2020 [dataset, Daimler, mono3D]
- NYC3DCars: A Dataset of 3D Vehicles in Geographic Context ICCV 2013
- Towards Fully Autonomous Driving: Systems and Algorithms IV 2011
- Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding [Notes] [mono3D, LID+DepJoint]
- ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection AAAI 2020 oral [mono3D]
- CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection [Notes] WACV 2021 [early fusion, camera, radar]
- 3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation [Notes] NeurIPS 2020 workshop [GM Israel, 3D LLD]
- LSTR: End-to-end Lane Shape Prediction with Transformers [Notes] WACV 2021 [LLD, transformers]
- PIXOR: Real-time 3D Object Detection from Point Clouds [Notes] CVPR 2018 (birds eye view)
- HDNET/PIXOR++: Exploiting HD Maps for 3D Object Detection [Notes] CoRL 2018
- CPNDet: Corner Proposal Network for Anchor-free, Two-stage Object Detection ECCV 2020 [anchor free, two stage]
- MVF: End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds [Notes] CoRL 2019 [Waymo, VoxelNet 1st author]
- Pillar-based Object Detection for Autonomous Driving [Notes] ECCV 2020
- Training-Time-Friendly Network for Real-Time Object Detection AAAI 2020 [anchor-free, fast training]
- Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies [Review of autonomous stack, Yu Huang]
- Dense Monocular Depth Estimation in Complex Dynamic Scenes CVPR 2016
- Probabilistic Future Prediction for Video Scene Understanding
- AB3D: A Baseline for 3D Multi-Object Tracking IROS 2020 [3D MOT]
- Spatial-Temporal Relation Networks for Multi-Object Tracking ICCV 2019 [MOT, feature location over time]
- Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking ICRA 2018 [MOT, IIT, 3D shape]
- ST-3D: Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking CVPR 2020 [Peilinag LI, author of VINS and S3DOT]
- Augment Your Batch: Improving Generalization Through Instance Repetition CVPR 2020
- RetinaTrack: Online Single Stage Joint Detection and Tracking CVPR 2020 [MOT]
- Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots
- Gradient Centralization: A New Optimization Technique for Deep Neural Networks ECCV 2020 oral
- Depth Completion via Deep Basis Fitting WACV 2020
- BTS: From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation [monodepth, supervised]
- The Edge of Depth: Explicit Constraints between Segmentation and Depth CVPR 2020 [monodepth, Xiaoming Liu]
- On the Continuity of Rotation Representations in Neural Networks CVPR 2019 [rotational representation]
- VDO-SLAM: A Visual Dynamic Object-aware SLAM System IJRR 2020
- Dynamic SLAM: The Need For Speed
- Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction ECCV 2020
- Traffic Light Mapping and Detection [Notes] ICRA 2011 [traffic light, Google, Chris Urmson]
- Traffic light recognition exploiting map and localization at every stage [Notes] Expert Systems 2017 [traffic light, 鲜于明镐，徐在圭，郑浩奇]
- Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars [Notes] IJCNN 2019 [traffic light, Espirito Santo Brazil]
2020-10 (14)
- TSM: Temporal Shift Module for Efficient Video Understanding [Notes] ICCV 2019 [Song Han, video, object detection]
- WOD: Waymo Dataset: Scalability in Perception for Autonomous Driving: Waymo Open Dataset [Notes] CVPR 2020
- Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection [Notes] NeurIPS 2020 [classification as regression]
- A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection NeurIPS 2020 spotlight
- Rethinking the Value of Labels for Improving Class-Imbalanced Learning NeurIPS 2020
- RepLoss: Repulsion Loss: Detecting Pedestrians in a Crowd [Notes] CVPR 2018 [crowd detection, Megvii]
- Adaptive NMS: Refining Pedestrian Detection in a Crowd [Notes] CVPR 2019 oral [crowd detection, NMS]
- AggLoss: Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd [Notes] ECCV 2018 [crowd detection]
- CrowdDet: Detection in Crowded Scenes: One Proposal, Multiple Predictions [Notes] CVPR 2020 oral [crowd detection, Megvii, Earth mover’s distance]
- R2-NMS: NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing [Notes] CVPR 2020
- Double Anchor R-CNN for Human Detection in a Crowd [Notes] [head-body bundle]
- Review: AP vs MR
- SKU110K: Precise Detection in Densely Packed Scenes [Notes] CVPR 2019 [crowd detection, no occlusion]
- GossipNet: Learning non-maximum suppression CVPR 2017
- TLL: Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation ECCV 2018
- Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels GCPR 2020 [mono3D, Daniel Cremers, TUM]
- CubifAE-3D: Monocular Camera Space Cubification on Autonomous Vehicles for Auto-Encoder based 3D Object Detection [Notes] [mono3D, depth AE pretraining]
- Deformable DETR: Deformable Transformers for End-to-End Object Detection [Notes] ICLR 2021 [Jifeng Dai, DETR]
- ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale [Notes] ICLR 2021
- BYOL: Bootstrap your own latent: A new approach to self-supervised Learning [self-supervised]
2020-09 (15)
- SDFLabel: Autolabeling 3D Objects With Differentiable Rendering of SDF Shape Priors [Notes] CVPR 2020 oral [TRI, differentiable rendering]
- DensePose: Dense Human Pose Estimation In The Wild [Notes] CVPR 2018 oral [FAIR]
- NOCS: Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation CVPR 2019
- monoDR: Monocular Differentiable Rendering for Self-Supervised 3D Object Detection [Notes] ECCV 2020 [TRI, mono3D]
- Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D [Notes] ECCV 2020 [BEV-Net, Utoronto, Sanja Fidler]
- Implicit Latent Variable Model for Scene-Consistent Motion Forecasting ECCV 2020 [Uber ATG, Rachel Urtasun]
- FISHING Net: Future Inference of Semantic Heatmaps In Grids [Notes] CVPRW 2020 [BEV-Net, Mapping, Zoox]
- VPN: Cross-view Semantic Segmentation for Sensing Surroundings [Notes] RAL 2020 [Bolei Zhou, BEV-Net]
- VED: Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks [Notes] ICRA 2019 [BEV-Net]
- Cam2BEV: A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird’s Eye View [Notes] ITSC 2020 [BEV-Net]
- Learning to Look around Objects for Top-View Representations of Outdoor Scenes [Notes] ECCV 2018 [BEV-Net, UCSD, Manmohan Chandraker]
- A Parametric Top-View Representation of Complex Road Scenes CVPR 2019 [BEV-Net, UCSD, Manmohan Chandraker]
- FTM: Understanding Road Layout from Videos as a Whole CVPR 2020 [BEV-Net, UCSD, Manmohan Chandraker]
- KM3D-Net: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training [Notes] RAL 2021 [RTM3D, Peixuan Li]
- InstanceMotSeg: Real-time Instance Motion Segmentation for Autonomous Driving [Notes] IROS 2020 [motion segmentation]
- MPV-Nets: Monocular Plan View Networks for Autonomous Driving [Notes] IROS 2019 [BEV-Net]
- Class-Balanced Loss Based on Effective Number of Samples [Notes] CVPR 2019 [Focal loss authors]
- Geometric Pretraining for Monocular Depth Estimation [Notes] ICRA 2020
- Robust Traffic Light and Arrow Detection Using Digital Map with Spatial Prior Information for Automated Driving [Notes] Sensors 2020 [traffic light, 金沢]
2020-08 (26)
- Feature-metric Loss for Self-supervised Learning of Depth and Egomotion [Notes] ECCV 2020 [feature-metric, local minima, monodepth]
- Depth-VO-Feat: Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction CVPR 2018 [feature-metric, monodepth]
- MonoResMatch: Learning monocular depth estimation infusing traditional stereo knowledge [Notes] CVPR 2019 [monodepth, local minima, cheap stereo GT]
- SGDepth: Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance [Notes] ECCV 2020 [Moving objects]
- Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding ECCV 2018 [dynamic objects, rigid and dynamic motion]
- Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding TPAMI 2018
- CC: Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation [Notes] CVPR 2019
- ObjMotionNet: Self-supervised Object Motion and Depth Estimation from Video [Notes] CVPRW 2020 [object motion prediction, velocity prediction]
- Instance-wise Depth and Motion Learning from Monocular Videos
- Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation
- Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
- DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency ECCV 2018
- LineNet: a Zoomable CNN for Crowdsourced High Definition Maps Modeling in Urban Environments [mapping]
- Road-SLAM: Road Marking based SLAM with Lane-level Accuracy [Notes] [HD mapping]
- AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot [Notes] IROS 2020 [Huawei, HD mapping, Tong Qin, VINS author, autonomous valet parking]
- AVP-SLAM-Late-Fusion: Mapping and Localization using Semantic Road Marking with Centimeter-level Accuracy in Indoor Parking Lots [Notes] ITSC 2019
- Lane markings-based relocalization on highway ITSC 2019
- DeepRoadMapper: Extracting Road Topology from Aerial Images [Notes] ICCV 2017 [Uber ATG, NOT HD maps]
- RoadTracer: Automatic Extraction of Road Networks from Aerial Images CVPR 2018 [NOT HD maps]
- PolyMapper: Topological Map Extraction From Overhead Images [Notes] ICCV 2019 [mapping, polygon, NOT HD maps]
- HRAN: Hierarchical Recurrent Attention Networks for Structured Online Maps [Notes] CVPR 2018 [HD mapping, highway, polyline loss, Chamfer distance]
- Deep Structured Crosswalk: End-to-End Deep Structured Models for Drawing Crosswalks [Notes] ECCV 2018
- DeepBoundaryExtractor: Convolutional Recurrent Network for Road Boundary Extraction [Notes] CVPR 2019 [HD mapping, boundary, polyline loss]
- DAGMapper: Learning to Map by Discovering Lane Topology [Notes] ICCV 2019 [HD mapping, highway, forks and merges, polyline loss]
- Sparse-HD-Maps: Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization [Notes] IROS 2019 oral [Uber ATG, metadata, mapping, localization]
- Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks IEEE TGRS 2018
- Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs Sensors 2020 [Tsinghua, 3D HD maps]
- PatchNet: Rethinking Pseudo-LiDAR Representation [Notes] ECCV 2020 [SenseTime, Wanli Ouyang]
- D4LCN: Learning Depth-Guided Convolutions for Monocular 3D Object Detection [Notes] CVPR 2020 [mono3D]
- MfS: Learning Stereo from Single Images [Notes] ECCV 2020 [mono for stereo, learn stereo matching with mono]
- BorderDet: Border Feature for Dense Object Detection ECCV 2020 oral [Megvii]
- Scale-Aware Trident Networks for Object Detection ICCV 2019 [different heads for different scales]
- Learning Depth from Monocular Videos using Direct Methods
- Vid2Depth: Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints CVPR 2018 [Google]
- NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
- Supervising the new with the old: learning SFM from SFM [Notes] ECCV 2018
- Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera CVPR 2019 [multi-frame monodepth]
- Don’t Forget The Past: Recurrent Depth Estimation from Monocular Video [multi-frame monodepth, RNN]
- Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth [multi-frame monodepth, RNN]
- Exploiting temporal consistency for real-time video depth estimation ICCV 2019 [multi-frame monodepth, RNN, indoor]
- SfM-Net: Learning of Structure and Motion from Video [dynamic object, SfM]
- MB-Net: MergeBoxes for Real-Time 3D Vehicles Detection [Notes] IV 2018 [mono3D: Daimler]
- BS3D: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images [Notes] IV 2019 [mono3D, Daimler]
- 3D-GCK: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via
Geometrically Constrained Keypoints in Real-Time [Notes] IV 2020 [[mono3D, Daimler]
- UR3D: Distance-Normalized Unified Representation for Monocular 3D Object Detection [Notes] ECCV 2020 [mono3D]
- DA-3Det: Monocular 3D Object Detection via Feature Domain Adaptation [Notes] ECCV 2020 [mono3D]
- RAR-Net: Reinforced Axial Refinement Network for Monocular 3D Object Detection [Notes] ECCV 2020 [mono3D]
2020-07 (25)
- CenterTrack: Tracking Objects as Points [Notes] ECCV 2020 spotlight [camera based 3D MOD, MOT SOTA, CenterNet, video based object detection, Philipp Krähenbühl]
- CenterPoint: Center-based 3D Object Detection and Tracking [Notes] CVPR 2021 [lidar based 3D MOD, CenterNet]
- Tracktor: Tracking without bells and whistles [Notes] ICCV 2019 [Tracktor/Tracktor++, Laura Leal-Taixe@TUM]
- FairMOT: A Simple Baseline for Multi-Object Tracking [Notes]
- DeepMOT: A Differentiable Framework for Training Multiple Object Trackers [Notes] CVPR 2020 [trainable Hungarian, Laura Leal-Taixe@TUM]
- MPNTracker: Learning a Neural Solver for Multiple Object Tracking CVPR 2020 oral [trainable Hungarian, Laura Leal-Taixe@TUM]
- nuScenes: A multimodal dataset for autonomous driving [Notes] CVPR 2020 [dataset, point cloud, radar]
- CBGS: Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection [Notes] CVPRW 2019 [Megvii, lidar, WAD challenge winner]
- AFDet: Anchor Free One Stage 3D Object Detection and Competition solution [Notes] CVPRW 2020 [Horizon robotics, lidar, winning for Waymo challenge]
- Review of MOT and SOT [Notes]
- CrowdHuman: A Benchmark for Detecting Human in a Crowd [Notes] [megvii, pedestrian, dataset]
- WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild [Notes] TMM 2019 [dataset, pedestrian]
- Tsinghua-Daimler Cyclists: A New Benchmark for Vison-Based Cyclist Detection [Notes] IV 2016 [dataset, cyclist Detection]
- Specialized Cyclist Detection Dataset: Challenging Real-World Computer Vision Dataset for Cyclist Detection Using a Monocular RGB Camera [Notes] IV 2019 [Extention to KITTI]
- PointTrack: Segment as Points for Efficient Online Multi-Object Tracking and Segmentation [Notes] ECCV 2020 oral [MOTS]
- PointTrack++ for Effective Online Multi-Object Tracking and Segmentation [Notes] CVPR 2020 workshop [CVPR2020 MOTS Challenge Winner. PointTrack++ ranks first on KITTI MOTS]
- SpatialEmbedding: Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth [Notes] ICCV 2019 [one-stage, instance segmentation]
- BA-Net: Dense Bundle Adjustment Networks [Notes] ICLR 2019 [Bundle adjustment, multi-frame monodepth, feature-metric]
- DeepSFM: Structure From Motion Via Deep Bundle Adjustment ECCV 2020 oral [multi-frame monodepth, indoor scene]
- CVD: Consistent Video Depth Estimation [Notes] SIGGRAPH 2020 [multi-frame monodepth, online finetune]
- DeepV2D: Video to Depth with Differentiable Structure from Motion [Notes] ICLR 2020 [multi-frame monodepth, Jia Deng]
- GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose [Notes] CVPR 2018 [residual optical flow, monodepth, rigid and dynamic motion]
- GLNet: Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera [Notes] ICCV 2019 [online finetune, rigid and dynamic motion]
- Depth Hints: Self-Supervised Monocular Depth Hints [Notes] ICCV 2019 [monodepth, local minima, cheap stereo GT]
- MonoUncertainty: On the uncertainty of self-supervised monocular depth estimation [Notes] CVPR 2020 [depth uncertainty]
- Self-Supervised Learning of Depth and Ego-motion with Differentiable Bundle Adjustment [Notes] [Bundle adjustment, xmotors.ai, multi-frame monodepth]
- Kinematic 3D Object Detection in Monocular Video [Notes] ECCV 2020 [multi-frame mono3D, Xiaoming Liu]
- VelocityNet: Camera-based vehicle velocity estimation from monocular video [Notes] CVPR 2017 workshop [monocular velocity estimation, CVPR 2017 challenge winner]
- Vehicle Centric VelocityNet: End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera [Notes] [monocular velocity estimation, monocular distance, SOTA]
2020-06 (20)
- LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain [Notes] IROS 2018 [lidar, mapping]
- PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction [Notes] ICCV 2019
- JAAD: Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian
Crosswalk Behavior ICCV 2017
- Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs BMVC 2019
- Is the Pedestrian going to Cross? Answering by 2D Pose Estimation IV 2018
- Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation ITSC 2019 [skeleton, pedestrian, cyclist intention]
- Attentive Single-Tasking of Multiple Tasks CVPR 2019
- DETR: End-to-End Object Detection with Transformers [Notes] ECCV 2020 oral [FAIR]
- Transformer: Attention Is All You Need [Notes] NIPS 2017
- SpeedNet: Learning the Speediness in Videos [Notes] CVPR 2020 oral
- MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships [Notes] CVPR 2020 [Mono3D, pairwise relationship]
- SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation [Notes] CVPRW 2020 [Mono3D, Zongmu]
- Vehicle Re-ID for Surround-view Camera System [Notes] CVPRW 2020 [tireline, vehicle ReID, Zongmu]
- End-to-End Lane Marker Detection via Row-wise Classification [Notes] [Qualcomm Korea, LLD as cls]
- Reliable multilane detection and classification by utilizing CNN as a regression network ECCV 2018 [LLD as reg]
- SUPER: A Novel Lane Detection System [Notes]
- Learning Lightweight Lane Detection CNNs by Self Attention Distillation ICCV 2019
- StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation BMVC 2015
- StixelNetV2: Real-time category-based and general obstacle detection for autonomous driving [Notes] ICCV 2017 [DS]
- Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network [Notes] CVPR 2016 [channel-to-pixel]
- Car Pose in Context: Accurate Pose Estimation with Ground Plane Constraints [mono3D]
- Self-Mono-SF: Self-Supervised Monocular Scene Flow Estimation [Notes] CVPR 2020 oral [scene-flow, Stereo input]
- MEBOW: Monocular Estimation of Body Orientation In the Wild [Notes] CVPR 2020
- VG-NMS: Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes [Notes] NeurIPS 2019 workshop [Crowded scene, NMS, Daimler]
- WYSIWYG: What You See is What You Get: Exploiting Visibility for 3D Object Detection [Notes] CVPR 2020 oral [occupancy grid]
- Real-Time Panoptic Segmentation From Dense Detections [Notes] CVPR 2020 oral [bbox + semantic segmentation = panoptic segmentation, Toyota]
- Human-Centric Efficiency Improvements in Image Annotation for Autonomous Driving [Notes] CVPRW 2020 [efficient annotation]
- SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving [Notes] CVPR 2020 oral [Waymo, auto data generation, surfel]
- LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World [Notes] CVPR 2020 oral [Uber ATG, auto data generation, surfel]
- SuMa++: Efficient LiDAR-based Semantic SLAM IROS 2019 [semantic segmentation, lidar, SLAM]
- PON/PyrOccNet: Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks [Notes] CVPR 2020 oral [BEV-Net, OFT]
- MonoLayout: Amodal scene layout from a single image [Notes] WACV 2020 [BEV-Net]
- BEV-Seg: Bird’s Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud [Notes] CVPR 2020 workshop [BEV-Net, Mapping]
- A Geometric Approach to Obtain a Bird’s Eye View from an Image ICCVW 2019 [mapping, geometry, Andrew Zisserman]
- FrozenDepth: Learning the Depths of Moving People by Watching Frozen People [Notes] CVPR 2019 oral
- ORB-SLAM: a Versatile and Accurate Monocular SLAM System TRO 2015
- ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras TRO 2016
- CubeSLAM: Monocular 3D Object SLAM [Notes] TRO 2019 [dynamic SLAM, orb slam + mono3D]
- ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings [Notes] CVPR 2020 [general dynamic SLAM]
- S3DOT: Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving [Notes] ECCV 2018 [Peiliang Li]
- Multi-object Monocular SLAM for Dynamic Environments [Notes] IV 2020 [monolayout authors]
- PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume [Notes] CVPR 2018 oral [Optical flow]
- LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation CVPR 2018 [Optical flow]
- FlowNet: Learning Optical Flow With Convolutional Networks ICCV 2015 [Optical flow]
- FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks CVPR 2017 [Optical flow]
- ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network CVPR 2019 [semantic segmentation, lightweight]
- Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes ICCV 2019 [depth uncertainty]
2020-05 (19)
2020-04 (14)
- ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst [Notes] RSS 2019 [Waymo]
- IntentNet: Learning to Predict Intention from Raw Sensor Data [Notes] CoRL 2018 [Uber ATG, perception and prediction, Lidar+Map]
- RoR: Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions [Notes] CVPR 2019 [Zoox]
- MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction [Notes] CoRL 2019 [Waymo, authors from RoR and ChauffeurNet]
- NMP: End-to-end Interpretable Neural Motion Planner [Notes] CVPR 2019 oral [Uber ATG]
- Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks [Notes] ICRA 2019 [Henggang Cui, Multimodal, Uber ATG Pittsburgh]
- Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving WACV 2020 [Uber ATG Pittsburgh]
- Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles IROS 2019 Oral [Uber ATG, behavioral planning, motion planning]
- TensorMask: A Foundation for Dense Object Segmentation [Notes] ICCV 2019 [single-stage instance seg]
- BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation [Notes] CVPR 2020 oral
- Mask Encoding for Single Shot Instance Segmentation [Notes] CVPR 2020 oral [single-stage instance seg, Chunhua Shen]
- PolarMask: Single Shot Instance Segmentation with Polar Representation [Notes] CVPR 2020 oral [single-stage instance seg]
- SOLO: Segmenting Objects by Locations [Notes] ECCV 2020 [single-stage instance seg, Chunhua Shen]
- SOLOv2: Dynamic, Faster and Stronger [Notes] [single-stage instance seg, Chunhua Shen]
- CondInst: Conditional Convolutions for Instance Segmentation [Notes] ECCV 2020 oral [single-stage instance seg, Chunhua Shen]
- CenterMask: Single Shot Instance Segmentation With Point Representation [Notes]CVPR 2020
2020-03 (15)
2020-02 (12)
2020-01 (19)
2019-12 (12)
2019-11 (20)
2019-10 (18)
2019-09 (17)
2019-08 (18)
2019-07 (19)
- Deep Parametric Continuous Convolutional Neural Networks [Notes] CVPR 2018 (@Uber, sensor fusion)
- ContFuse: Deep Continuous Fusion for Multi-Sensor 3D Object Detection [Notes] ECCV 2018 [Uber ATG, sensor fusion, BEV]
- Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net [Notes] CVPR 2018 oral [lidar only, perception and prediction]
- LearnK: Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras [Notes] ICCV 2019 [monocular depth estimation, intrinsic estimation, SOTA]
- monodepth: Unsupervised Monocular Depth Estimation with Left-Right Consistency [Notes] CVPR 2017 oral (monocular depth estimation, stereo for training)
- Struct2depth: Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos [Notes] AAAI 2019 [monocular depth estimation, estimating movement of dynamic object, infinite depth problem, online finetune]
- Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency [Notes] AAAI 2018 (monocular depth estimation, static assumption, surface normal)
- LEGO Learning Edge with Geometry all at Once by Watching Videos [Notes] CVPR 2018 spotlight (monocular depth estimation, static assumption, surface normal)
- Object Detection and 3D Estimation via an FMCW Radar Using a Fully Convolutional Network [Notes] (radar, RD map, OD, Arxiv 201902)
- A study on Radar Target Detection Based on Deep Neural Networks [Notes] (radar, RD map, OD)
- 2D Car Detection in Radar Data with PointNets [Notes] (from Ulm Univ, radar, point cloud, OD, Arxiv 201904)
- Learning Confidence for Out-of-Distribution Detection in Neural Networks [Notes] (budget to cheat)
- A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification [Notes] ICRA 2017 (Bosch, traffic lights)
- How hard can it be? Estimating the difficulty of visual search in an image [Notes] CVPR 2016
- Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges [Notes] (review from Bosch)
- Review of monocular 3d object detection (blog from 知乎)
- Deep3dBox: 3D Bounding Box Estimation Using Deep Learning and Geometry [Notes] CVPR 2017 [Zoox]
- MonoPSR: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction [Notes] CVPR 2019
- OFT: Orthographic Feature Transform for Monocular 3D Object Detection [Notes] BMVC 2019 [Convert camera to BEV, Alex Kendall]
2019-06 (12)
2019-05 (18)
2019-04 (12)
2019-03 (19)
2019-02 (9)
2019-01 (10)
2018
2017 and before
Papers to Read
Here is the list of papers waiting to be read.
Deep Learning in general
Self-training
2D Object Detection and Segmentation
Fisheye
Video Understanding
Pruning and Compression
Architecture Improvements
Reinforcement Learning
3D Perception
Stereo and Flow
Traffic light and traffic sign
Datasets and Surveys
Unsupervised depth estimation
Indoor Depth
lidar
Egocentric bbox prediction
Lane Detection
Tracking
keypoints: pose and face
General DL
Mono3D
- 3DOP: 3D Object Proposals for Accurate Object Class Detection NIPS 2015
- DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation
- Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery ECCV 2018 (Monocular 3D object detection and depth estimation)
- Towards Scene Understanding: Unsupervised Monocular Depth Estimation with Semantic-aware Representation CVPR 2019 [unified conditional decoder]
- DDP: Dense Depth Posterior from Single Image and Sparse Range CVPR 2019
- Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes IJCV 2018 (data augmentation with AR, Toyota)
- Exploring the Capabilities and Limits of 3D Monocular Object Detection – A Study on Simulation and Real World Data IITS
- Towards Scene Understanding with Detailed 3D Object Representations IJCV 2014 (keypoint, 3D bbox annotation)
- Deep Cuboid Detection: Beyond 2D Bounding Boxes (Magic Leap)
- Viewpoints and Keypoints (Malik)
- Lifting Object Detection Datasets into 3D (PASCAL)
- 3D Object Class Detection in the Wild (keypoint based)
- Fast Single Shot Detection and Pose Estimation 3DV 2016 (SSD + pose, Wei Liu)
- Virtual KITTI 2
- Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing CVPR 2017
- Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views ICCV 2015 Oral
- Real-Time Seamless Single Shot 6D Object Pose Prediction CVPR 2018
- Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching NIPS 2018 [disparity estimation]
- Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera ICRA 2019
- Learning Depth with Convolutional Spatial Propagation Network (Baidu, depth from SPN) ECCV 2018
- Just Go with the Flow: Self-Supervised Scene Flow Estimation CVPR 2020 oral [Scene flow, Lidar]
- Online Depth Learning against Forgetting in Monocular Videos CVPR 2020 [monodepth]
- Self-Supervised Deep Visual Odometry with Online Adaptation CVPR 2020 oral [DF-VO, TrianFlow, meta-learning]
- Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume CVPR 2020
- Online Depth Learning against Forgetting in Monocular Videos CVPR 2020 [monodepth, online learning]
- SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation CVPR 2020 [monodepth, semantic]
- Inferring Distributions Over Depth from a Single Image TRO [Depth confidence, stitching them together]
- Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths CVPR 2020
- The Edge of Depth: Explicit Constraints between Segmentation and Depth CVPR 2020 [Xiaoming Liu, multimodal, depth bleeding]
Radar Perception
SLAM
Radar Perception
Reviews and Surveys
Beyond Perception in Autonomous Driving
Prediction and Planning
Low level DL
Early NLP papers
Non-DL
Technical Debt
To be organized (CVPR 2021 and ICCV 2021 the pile to be read)
- Capturing Omni-Range Context for Omnidirectional Segmentation CVPR 2021
- UP-DETR: Unsupervised Pre-training for Object Detection with Transformers CVPR 2021 [transformers]
- DCL: Dense Label Encoding for Boundary Discontinuity Free Rotation Detection CVPR 2021
- 4D Panoptic LiDAR Segmentation CVPR 2021 [TUM]
- CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild CVPR 2021
- Fast and Accurate Model Scaling CVPR 2021 [FAIR]
- Cylinder3D: Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation CVPR 2021 [lidar semantic segmentation]
- LiDAR R-CNN: An Efficient and Universal 3D Object Detector CVPR 2021 [TuSimple, Lidar]
- PREDATOR: Registration of 3D Point Clouds with Low Overlap CVPR 2021 oral
- DBB: Diverse Branch Block: Building a Convolution as an Inception-like Unit CVPR 2021 [RepVGG, ACNet, Xiaohan Ding, Megvii]
- GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection CVPR 2021 [mono3D]
- DDMP: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection CVPR 2021 [mono3D]
- M3DSSD: Monocular 3D Single Stage Object Detector CVPR 2021 [mono3D]
- MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation CVPR 2021 [mono3D]
- HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection CVPR 2021 [Lidar]
- PLUME: Efficient 3D Object Detection from Stereo Images [Yan Wang, Uber ATG]
- V2F-Net: Explicit Decomposition of Occluded Pedestrian Detection [crowded, pedestrian, megvii]
- IP-basic: In Defense of Classical Image Processing: Fast Depth Completion on the CPU CRV 2018
- Revisiting Feature Alignment for One-stage Object Detection [cls+reg]
- Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During Deployment WACV 2021 [SafetyNet]
- TSD: Revisiting the Sibling Head in Object Detector CVPR 2020 [sensetime, cls+reg]
- 1st Place Solutions for OpenImage2019 – Object Detection and Instance Segmentation [sensetime, cls+reg, 1st place OpenImage2019]
- Enabling spatio-temporal aggregation in Birds-Eye-View Vehicle Estimation ICRA 2021
- End-to-end Lane Detection through Differentiable Least-Squares Fitting ICCV workshop 2019
- Revisiting ResNets: Improved Training and Scaling Strategies
- Multi-Modality Cut and Paste for 3D Object Detection
- LD: Localization Distillation for Object Detection
- PolyTransform: Deep Polygon Transformer for Instance Segmentation CVPR 2020 [single stage instance segmentation]
- ROAD: The ROad event Awareness Dataset for Autonomous Driving
- LidarMTL: A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding [lidar MTL]
- High-Performance Large-Scale Image Recognition Without Normalization ICLR 2021
- Ground-aware Monocular 3D Object Detection for Autonomous Driving RA-L [mono3D]
- Demystifying Pseudo-LiDAR for Monocular 3D Object Detection [mono3d]
- Pseudo-labeling for Scalable 3D Object Detection [Waymo]
- LLA: Loss-aware Label Assignment for Dense Pedestrian Detection [Megvii]
- VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation CVPR 2020 [Waymo]
- CoverNet: Multimodal Behavior Prediction using Trajectory Sets CVPR 2020 [prediction, nuScenes]
- SplitNet: Divide and Co-training
- VoVNet: An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection CVPR 2019 workshop
- Isometric Neural Networks: Non-discriminative data or weak model? On the relative importance of data and model resolution ICCV 2019 workshop [spatial2channel]
- TResNet WACV 2021 [spatial2channel]
- Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression AAAI 2020 [DIOU, NMS]
- RegNet: Designing Network Design Spaces CVPR 2020 [FAIR]
- On Network Design Spaces for Visual Recognition [FAIR]
- Lane Endpoint Detection and Position Accuracy Evaluation for Sensor Fusion-Based Vehicle Localization on Highways Sensors 2018 [lane endpoints]
- Map-Matching-Based Cascade Landmark Detection and Vehicle Localization IEEE Access 2019 [lane endpoints]
- GCNet: End-to-End Learning of Geometry and Context for Deep Stereo Regression ICCV 2017 [disparity estimation, Alex Kendall, cost volume]
- Traffic Control Gesture Recognition for Autonomous Vehicles IROS 2020 [Daimler]
- Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild ECCV 2020
- OrcVIO: Object residual constrained Visual-Inertial Odometry [dynamic SLAM, very mathematical]
- InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling ECCV 2020
- DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving ECCV 2020
- Towards Lightweight Lane Detection by Optimizing Spatial Embedding ECCV 2020 workshop [LLD]
- Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection ECCV 2020 workshop [lidar]
- DeepIM: Deep iterative matching for 6d pose estimation ECCV 2018 [pose estimation]
- Monocular Depth Prediction through Continuous 3D Loss IROS 2020
- Multi-Task Learning for Dense Prediction Tasks: A Survey [MTL, Luc Van Gool]
- Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems ITSC 2020 oral [MTL]
- NeurAll: Towards a Unified Model for Visual Perception in Automated Driving ITSC 2019 oral [MTL]
- Deep Evidential Regression NeurIPS 2020 [one-pass aleatoric/epistemic uncertainty]
- Estimating Drivable Collision-Free Space from Monocular Video WACV 2015 [Drivable space]
- Visualization of Convolutional Neural Networks for Monocular Depth Estimation ICCV 2019 [monodepth]
- Differentiable Rendering: A Survey [differentiable rendering, TRI]
- SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware
Feature Extraction [monodepth, semantics, Naver labs]
- Toward Interactive Self-Annotation For Video Object Bounding Box: Recurrent Self-Learning And Hierarchical Annotation Based Framework WACV 2020
- Towards Good Practice for CNN-Based Monocular Depth Estimation WACV 2020
- Self-Supervised Scene De-occlusion CVPR 2020 oral
- TP-LSD: Tri-Points Based Line Segment Detector
- Data Distillation: Towards Omni-Supervised Learning CVPR 2018 [Kaiming He, FAIR]
- MiDas: Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer [monodepth, dynamic object, synthetic dataset]
- Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation [monodepth]
- Towards Lightweight Lane Detection by Optimizing Spatial Embedding ECCV 2020 workshop
- Synthetic-to-Real Domain Adaptation for Lane Detection [GM Israel, LLD]
- PolyLaneNet: Lane Estimation via Deep Polynomial Regression ICPR 2020 [polynomial, LLD]
- Learning Universal Shape Dictionary for Realtime Instance Segmentation
- End-to-End Video Instance Segmentation with Transformers [DETR, transformers]
- Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks CVPR 2020 workshop
- When and Why Test-Time Augmentation Works
- Footprints and Free Space from a Single Color Image CVPR 2020 oral [Parking use, footprint]
- Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning [BEV, only predict footprint]
- Rethinking Classification and Localization for Object Detection CVPR 2020
- Monocular 3D Object Detection with Sequential Feature Association and Depth Hint Augmentation [mono3D]
- Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
- ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
- MVSNet: Depth Inference for Unstructured Multi-view Stereo ECCV 2018
- Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference CVPR 2019 [Deep learning + MVS, Vidar, same author MVSNet]
- Artificial Dummies for Urban Dataset Augmentation AAAI 2021
- DETR for Pedestrian Detection [transformer, pedestrian detection]
- Multi-Modality Cut and Paste for 3D Object Detection [SenseTime]
- Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers [transformer, semantic segmenatation]
- TransPose: Towards Explainable Human Pose Estimation by Transformer [transformer, pose estimation]
- Seesaw Loss for Long-Tailed Instance Segmentation
- SWA Object Detection [Stochastic Weights Averaging (SWA)]
- 3D Object Detection with Pointformer
- Toward Transformer-Based Object Detection [DETR-like]
- Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion [dense SfM]
- Multi-Modality Cut and Paste for 3D Object Detection
- Vision Global Localization with Semantic Segmentation and Interest Feature Points
- Transformer Interpretability Beyond Attention Visualization [transformers]
- Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU
- DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
- Empirical Upper Bound in Object Detection and More
- Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline [Fisheye, Senthil Yogamani]
- Monocular 3D Object Detection with Sequential Feature Association and Depth Hint Augmentation [mono3D]
- SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images [Jiwen Lu, monodepth]
- Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion [TRI]
- Linformer: Self-Attention with Linear Complexity
- Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks ICML 2019
- PCT: Point cloud transformer Computational Visual Media 2021
- DDT: Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming IJCAI 2017
- Hierarchical Road Topology Learning for Urban Map-less Driving [Mercedes]
- Probabilistic Future Prediction for Video Scene Understanding ECCV 2020 [Alex Kendall]
- Detecting 32 Pedestrian Attributes for Autonomous Vehicles [VRU, MTL]
- Cascaded deep monocular 3D human pose estimation with evolutionary training data CVPR 2020 oral
- MonoGeo: Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection [mono3D]
- Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth [mono3D]
- Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting [mono3D]
- Lite-FPN for Keypoint-based Monocular 3D Object Detection [mono3D]
- Lidar Point Cloud Guided Monocular 3D Object Detection
- Vision Transformers for Dense Prediction [Vladlen Koltun, Intel]
- Efficient Transformers: A Survey
- Do Vision Transformers See Like Convolutional Neural Networks?
- Progressive Coordinate Transforms for Monocular 3D Object Detection [mono3D]
- AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection ICCV 2021 [mono3D]
- BlazePose: On-device Real-time Body Pose tracking
TODO
- Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language [Andy Zeng]
- Large Language Models as General Pattern Machines [Embodied AI]
- RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer
- PlaNet: Learning Latent Dynamics for Planning from Pixels ICML 2019
- Dreamer: Dream to Control: Learning Behaviors by Latent Imagination ICLR 2020 oral
- DreamerV2: Mastering Atari with Discrete World Models ICLR 2021 [World models]
- DreamerV3: Mastering Diverse Domains through World Models
- DayDreamer: World Models for Physical Robot Learning CoRL 2022
- JEPA: A Path Towards Autonomous Machine Intelligence
- I-JEPA: Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture CVPR 2023
- MAGVIT: Masked Generative Video Transformer CVPR 2023 highlight [Video prediction]
- Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models CVPR 2023 [Video prediction]
- Runway Gen-1: Structure and Content-Guided Video Synthesis with Diffusion Models
- Learning to drive from a world on rails ICCV 2021 oral [Philipp Krähenbühl]
- Learning from All Vehicles CVPR 2022 [Philipp Krähenbühl]
- End-to-End Urban Driving by Imitating a Reinforcement Learning Coach ICCV 2021
- End-to-end Autonomous Driving: Challenges and Frontiers
- IL Difficulty Model: Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula CoRL 2022 [Waymo]
- Decision Transformer: Reinforcement Learning via Sequence Modeling NeurIPS 2021 [LLM for planning]
- LID: Pre-Trained Language Models for Interactive Decision-Making NeurIPS 2022 [LLM for planning]
- Planning with Large Language Models via Corrective Re-prompting NeurIPS 2022 Workshop
- Object as Query: Equipping Any 2D Object Detector with 3D Detection Ability ICCV 2023 [TuSimple]
- Speculative Sampling: Accelerating Large Language Model Decoding with Speculative Sampling [Accelerated LLM, DeepMind]
- Inference with Reference: Lossless Acceleration of Large Language Models [Accelerated LLM, Microsoft]
- EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments T-RO 2021
- Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching ICRA 2020
- StreamPETR: Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
- SSCNet: Semantic Scene Completion from a Single Depth Image CVPR 2017
- SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences ICCV 2019
- PixPro: Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning [self-supervised]
- Pixel-Wise Contrastive Distillation [self-supervised]
- VICRegL: Self-Supervised Learning of Local Visual Features NeurIPS 2022
- ImageBind: One Embedding Space To Bind Them All CVPR 2023
- KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction ICRA 2022 [Planning]
- Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models L4DC [Planning]
- GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving [Planning]
- LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving [Planning, Raquel]
- DIPP: Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving [Planning]
- Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios [Planning, Waymo]
- Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving IROS 2022 [Planning, Waymo]
- Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation ICRA 2022 [Planning, Waymo]
- JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving [Planning, Waymo]
- MaskFormer: Per-Pixel Classification is Not All You Need for Semantic Segmentation NeurIPS 2021
- 3D Semantic Scene Completion: a Survey IJCV 2022
- DETIC: Detecting Twenty-thousand Classes using Image-level Supervision ECCV 2022
- Atlas: End-to-End 3D Scene Reconstruction from Posed Images ECCV 2020
- TransformerFusion: Monocular RGB Scene Reconstruction using Transformers NeurIPS 2021
- SimpleOccupancy: A Simple Attempt for 3D Occupancy Estimation in Autonomous Driving [Occupancy Network]
- OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion [Occupancy Network, stereo]
- Fast-BEV: Towards Real-time On-vehicle Bird’s-Eye View Perception NeurIPS 2022
- Fast-BEV: A Fast and Strong Bird’s-Eye View Perception Baseline
- ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals CVPR 2023 [Qcraft, prediction]
- Motion Transformer with Global Intention Localization and Local Movement Refinement NeurIPS 2022 Oral
- P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving
- MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction
- ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries
- SAM: Segment Anything [FAIR]
- GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D Understanding
- Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge [Encode Road requirement to prediction]
- [Hivt: Hierarchical vector transformer for multi-agent motion prediction]
- Transformer Feed-Forward Layers Are Key-Value Memories EMNLP 2021
- BEV-LaneDet: a Simple and Effective 3D Lane Detection Baseline CVPR 2023 [BEVNet]
- Exploring Recurrent Long-term Temporal Fusion for Multi-view 3D Perception [BEVNet, megvii]
- VAD: Vectorized Scene Representation for Efficient Autonomous Driving [Horizon]
- A Simple Attempt for 3D Occupancy Estimation in Autonomous Driving
- BEVPoolv2: A Cutting-edge Implementation of BEVDet Toward Deployment [BEVDet, PhiGent]
- NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving
- GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping CVPR 2020 [Cewu Lu]
- AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains [Cewu Lu]
- Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting
- HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding
- MTR: Motion Transformer with Global Intention Localization and Local Movement Refinement NeurIPS 2022
- UVTR: Unifying Voxel-based Representation with Transformer for 3D Object Detection [BEVFusion, Megvii, BEVNet, camera + lidar]
- Don’t Use Large Mini-Batches, Use Local SGD ICLR 2020
- Grokking: Generalization beyond Overfitting on small algorithmic datasets
- Progress measures for grokking via mechanistic interpretability
- Understanding deep learning requires rethinking generalization ICLR 2017
- Unifying Grokking and Double Descent
- Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models L4DC 2022
- Interactive Prediction and Planning for Autonomous Driving: from Algorithms to Fundamental Aspects [PhD thesis of Wei Zhan, 2019]
- Lyft1001: One Thousand and One Hours: Self-driving Motion Prediction Dataset [Lyft Level 5, prediction dataset]
- PCAccumulation: Dynamic 3D Scene Analysis by Point Cloud Accumulation ECCV 2022
- Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos NeurIPS 2022
- UniSim: A Neural Closed-Loop Sensor Simulator CVPR 2023 [simulation, Raquel]
- GeoSim: Realistic Video Simulation via Geometry-Aware Composition for
Self-Driving CVPR 2023
- Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skills [Driving Skill]
- Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors RSS 2023 [Driving Skill]
- IL Difficulty Model: Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula CoRL 2022 [Waymo]
- Neural Map Prior for Autonomous Driving CVPR 2023
- Track Anything: Segment Anything Meets Videos
- Self-Supervised Camera Self-Calibration from Video ICRA 2022 [TRI, calibration]
- Real-time Online Video Detection with Temporal Smoothing Transformers ECCV 2022 [ConvLSTM-style cross-attention]
- NeRF-Supervised Deep Stereo CVPR 2023
- GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images NeurIOS 2022
- OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation CVPR 2023
- Ego-Body Pose Estimation via Ego-Head Pose Estimation CVPR 2023
- FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation
- PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation
- BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
- Visual Instruction Tuning
- VideoChat: Chat-Centric Video Understanding
- ToT: Tree of Thoughts: Deliberate Problem Solving with Large Language Models [Notes]
- CoT: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- CoBEVT: Cooperative Bird’s Eye View Semantic Segmentation with Sparse Transformers CoRL 2022
- BEVFormer v2: Adapting Modern Image Backbones to Bird’s-Eye-View Recognition via Perspective Supervision [BEVNet, Jifeng Dai]
- Fast-BEV: Towards Real-time On-vehicle Bird’s-Eye View Perception NeurIPS 2022
- Traj++: Human Trajectory Forecasting in Crowds: A Deep Learning Perspective TITS 2021
- Data Driven Prediction Architecture for Autonomous Driving and its Application on Apollo Platform IV 2020 [Baidu]
- THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling ICLR 2022
- Learning Lane Graph Representations for Motion Forecasting ECCV 2020 oral
- Identifying Driver Interactions via Conditional Behavior Prediction ICRA 2021 [Waymo]
- Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data ECCV 2020
- TPNet: Trajectory Proposal Network for Motion Prediction CVPR 2020
- GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation
- PECNet: It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction ECCV 2020 oral
- From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting ICCV 2019
- PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings ICCV 2019
- PiP: Planning-informed Trajectory Prediction for Autonomous Driving ECCV 2020
- MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction CoRL 2019
- LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents CVPR 2021
- PRIME: Learning to Predict Vehicle Trajectories with Model-based Planning CoRL 2021
- A Flexible and Explainable Vehicle Motion Prediction and Inference Framework Combining Semi-Supervised AOG and ST-LSTM TITS 2020
- Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs IV 2018 [Trivedi]
- HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling ICRA 2022
- Trajectory Prediction with Linguistic Representations ICRA 2022
- What-If Motion Prediction for Autonomous Driving
- End-to-end Contextual Perception and Prediction with Interaction Transformer IROS 2020 [Auxiliary collision loss, scene compliant pred]
- SafeCritic: Collision-Aware Trajectory Prediction BMVC 2019 [IRL, scene compliant pred]
- Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset ICCV 2021 [Waymo]
- Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graph IROS 2020
- Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks NeurIPS 2019 workshop
- Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures Robotics: science and systems 2020
- Monocular 3D Object Detection: An Extrinsic Parameter Free Approach CVPR 2021 [PJLab]
- UniFormer: Unified Multi-view Fusion Transformer for Spatial-Temporal Representation in Bird’s-Eye-View [BEVFormer, BEVNet, Temporal]
- GitNet: geometric prior-baesd transformation for birds yee view segmentation
- WBF: weighted box fusion: ensembling boxes from differnt object detection modules
- NNI: auto parameter finding algorithm
- BEVFormer++: Improving BEVFormer for 3D Camera-only Object Detection [Waymo open dataset challenge 1st place in mono3d]
- LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection [Waymo open dataset challenge official metric]
- High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps Journal of Advanced Transportation 2018
- A Hybrid Vision-Map Method for Urban Road Detection Journal of Advanced Transportation 2017
- Terminology and Analysis of Map Deviations in Urban Domains: Towards Dependability for HD Maps in Automated Vehicles IV 2020
- TIME WILL TELL: NEW OUTLOOKS AND A BASELINE FOR TEMPORAL MULTI-VIEW 3D OBJECT DETECTION
- Conditional DETR for Fast Training Convergence ICCV 2021
- DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR ICLR 2022
- DN-DETR: Accelerate DETR Training by Introducing Query DeNoising CVPR 2022
- DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
- Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding [Ouyang Wanli]
- Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation [BEVNet, polar]
- MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries [BEVNet, tracking] CVPR 2022 workshop [Hang Zhao]
- ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning ECCV 2022 [Hongyang Li]
- GKT: Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer [BEVNet, Horizon]
- SiamRPN: High Performance Visual Tracking with Siamese Region Proposal Network CVPR 2018
- TPLR: Topology Preserving Local Road Network Estimation from Single Onboard Camera Image CVPR 2022 [STSU, Luc Van Gool]
- LaRa: Latents and Rays for Multi-Camera Bird’s-Eye-View Semantic Segmentation [Valeo, BEVNet, polar]
- PolarDETR: Polar Parametrization for Vision-based Surround-View 3D Detection [BEVNet]
- Exploring Geometric Consistency for Monocular 3D Object Detection CVPR 2022
- ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection WACV 2022 [mono3D]
- Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints AAAI 2022
- Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers ICICN 2021 [BEVNet, lane line]
- Unsupervised Labeled Lane Markers Using Maps ICCV 2019 workshop [Bosch, 2D lane line]
- M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers [Lidar detection, Waymo open dataset] WACV 2022
- K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways [lane line dataset]
- Robust Monocular 3D Lane Detection With Dual Attention ICIP 2021
- OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic 3D Reconstruction CVPR 2022
- MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer ICLR 2022 [lightweight Transformers]
- XFormer: Lightweight Vision Transformer with Cross Feature Attention [Samsung]
- CenterFormer: Center-based Transformer for 3D Object Detection ECCV 2022 oral [TuSimple]
- LidarMultiNet: Towards a Unified Multi-task Network for LiDAR Perception [2022 Waymo Open Dataset, TuSimple]
- MTRA: 1st Place Solution for 2022 Waymo Open Dataset Challenge - Motion Prediction [Waymo open dataset challenge 1st place in motion prediction]
- BEVSegFormer: Bird’s Eye View Semantic Segmentation From Arbitrary Camera Rigs [BEVNet]
- Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers CVPR 2022 [nVidia]
- Efficiently Identifying Task Groupings for Multi-Task Learning NeurIPS 2021 spotlight [MTL]
- Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time [Google, Golden Backbone]
- “The Pedestrian next to the Lamppost” Adaptive Object Graphs for Better Instantaneous Mapping CVPR 2022
- GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation [BEVNet, Baidu]
- FUTR3D: A Unified Sensor Fusion Framework for 3D Detection [Hang Zhao]
- GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation [BEVNet]
- MonoFormer: Towards Generalization of self-supervised monocular depth estimation with Transformers [monodepth]
- Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving
- cosFormer: Rethinking Softmax in Attention ICLR 2022
- StretchBEV: Stretching Future Instance Prediction Spatially and Temporally [BEVNet, prediction]
- Scene Representation in Bird’s-Eye View from Surrounding Cameras with Transformers [BEVNet, LLD] CVPR 2022 workshop
- Multi-Frame Self-Supervised Depth with Transformers CVPR 2022
- It’s About Time: Analog Clock Reading in the Wild CVPR 2022 [Andrew Zisserman]
- SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation CoRL 2022 [Jiwen Lu]
- ONCE-3DLanes: Building Monocular 3D Lane Detection CVPR 2022
- K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways CVPR 2022 workshop [3D LLD]
- Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving CVPR 2022 workshop
- A Simple Baseline for BEV Perception Without LiDAR [TRI, BEVNet, vision+radar]
- Reconstruct from Top View: A 3D Lane Detection Approach based on Geometry Structure Prior CVPR 2022 workshop
- RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding CVPR 2022 [Waymo, Charles Qi]
- Occupancy Flow Fields for Motion Forecasting in Autonomous Driving RAL 2022 [Waymo occupancy flow challenge]
- Safe Local Motion Planning with Self-Supervised Freespace Forecasting CVPR 2021
- 数据闭环的核心 - Auto-labeling 方案分享
- K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways
- LETR: Line Segment Detection Using Transformers without Edges CVPR 2021 oral
- HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps CVPR 2021 [HD mapping]
- SketchRNN: A Neural Representation of Sketch Drawings [David Ha]
- PolyGen: An Autoregressive Generative Model of 3D Meshes ICML 2020
- SOLQ: Segmenting Objects by Learning Queries NeurlPS 2021 [Megvii, end-to-end, instance segmentation]
- MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer 3DV 2022
- MVSTER: Epipolar Transformer for Efficient Multi-View Stereo ECCV 2022</bd>
- MOVEDepth: Crafting Monocular Cues and Velocity Guidance for Self-Supervised Multi-Frame Depth Learning [MVS + monodepth]
- SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation
- Scene Transformer: A unified architecture for predicting multiple agent trajectories [prediction, Waymo] ICLR 2022
- SSIA: Monocular Depth Estimation with Self-supervised Instance Adaptation [VGG team, TTR, test time refinement, CVD]
- CoMoDA: Continuous Monocular Depth Adaptation Using Past Experiences WACV 2021
- MonoRec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera CVPR 2021 [Daniel Cremmers]
- Plenoxels: Radiance Fields without Neural Networks
- Lidar with Velocity: Motion Distortion Correction of Point Clouds from Oscillating Scanning Lidars [Livox, ISEE]
- NWD: A Normalized Gaussian Wasserstein Distance for Tiny Object Detection
- Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation NeurIPS 2021 [Sanja Fidler]
- Insta-DM: Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency AAAI 2021
- Instance-wise Depth and Motion Learning from Monocular Videos NeurIPS 2020 workshop [website]
- NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis ECCV 2020 oral
- BARF: Bundle-Adjusting Neural Radiance Fields ICCV 2021 oral
- NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo ICCV 2021 oral
- Transfuser: Multi-Modal Fusion Transformer for End-to-End Autonomous Driving CVPR 2021
- YOLinO: Generic Single Shot Polyline Detection in Real Time ICCV 2021 workshop [lld]
- MonoRCNN: Geometry-based Distance Decomposition for Monocular 3D Object Detection ICCV 2021
- MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation ICCV 2021 workshop
- PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection CVPR 2020 [Waymo challenge 2nd place]
- Geometry-based Distance Decomposition for Monocular 3D Object Detection ICCV 2021 [mono3D]
- Offboard 3D Object Detection from Point Cloud Sequences CVPR 2021 [Charles Qi]
- FreeAnchor: Learning to Match Anchors for Visual Object Detection NeurIPS 2019
- AutoAssign: Differentiable Label Assignment for Dense Object Detection
- Probabilistic Anchor Assignment with IoU Prediction for Object Detection ECCV 2020
- FOVEA: Foveated Image Magnification for Autonomous Navigation ICCV 2021 [Argo]
- PifPaf: Composite Fields for Human Pose Estimation CVPR 2019
- Monocular 3D Localization of Vehicles in Road Scenes ICCV 2021 workshop [mono3D, tracking]
- TransformerFusion: Monocular RGB Scene Reconstruction using Transformers
- Conditional DETR for Fast Training Convergence
- Anchor DETR: Query Design for Transformer-Based Detector [megvii]
- PGD: Probabilistic and Geometric Depth: Detecting Objects in Perspective CoRL 2021
- Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
- What Makes for End-to-End Object Detection? PMLR 2021
- Instances as Queries ICCV 2021 [instance segmentation]
- One Million Scenes for Autonomous Driving: ONCE Dataset [Huawei]
- NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis 3DV 2021
- Is 2D Heatmap Representation Even Necessary for Human Pose Estimation?
- Topology Preserving Local Road Network Estimation from Single Onboard Camera Image [BEVNet, Luc Van Gool]