Awesome-3D-Object-Detection
A curated list of research in 3D Object Detection(Lidar-based Method).
You are very welcome to pull request to update this list. 😃
Dataset
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- 3,712 training samples
- 3,769 validation samples
- 7,518 testing samples
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- 28k training samples
- 6k validation samples
- 6k testing samples
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- 798 training sequences with around 158, 361 LiDAR samples
- 202 validation sequences with 40, 077 LiDAR samples.
Top conference & workshop
Conferene
- Conference on Computer Vision and Pattern Recognition(CVPR)
- International Conference on Computer Vision(ICCV)
- European Conference on Computer Vision(ECCV)
Workshop
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CVPR 2019 Workshop on Autonomous Driving(nuScenes 3D detection)
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CVPR 2020 Workshop on Autonomous Driving(BDD1k 3D tracking)
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CVPR 2021 Workshop on Autonomous Driving(waymo 3D detection)
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CVPR 2022 Workshop on Autonomous Driving(waymo 3D detection)
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CVPR 2021 Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics
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ICCV 2021 Workshop on Autonomous Vehicle Vision (AVVision), note
Paper (Lidar-based method)
- End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds paper
- Vehicle Detection from 3D Lidar Using Fully Convolutional Network(baidu) paper
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection paper
- Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks paper
- RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving paper
- BirdNet: a 3D Object Detection Framework from LiDAR information paper
- LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDAR paper
- HDNET: Exploit HD Maps for 3D Object Detection paper
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation paper
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space paper
- IPOD: Intensive Point-based Object Detector for Point Cloud paper
- PIXOR: Real-time 3D Object Detection from Point Clouds paper
- DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet paper
- Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds paper
- STD: Sparse-to-Dense 3D Object Detector for Point Cloud paper
- Fast Point R-CNN paper
- StarNet: Targeted Computation for Object Detection in Point Clouds paper
- Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection paper
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving paper
- FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds paper
- Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud paper
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud paper
- Complex-YOLO: Real-time 3D Object Detection on Point Clouds paper
- YOLO4D: A ST Approach for RT Multi-object Detection and Classification from LiDAR Point Clouds paper
- YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud paper
- Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud paper
- Pillar-based Object Detection for Autonomous Driving (ECCV2020) paper
- EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection(ECCV2020) paper
- Multi-Echo LiDAR for 3D Object Detection(ICCV2021) paper
- LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector(ICCV2021) paper
- SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation(ICCV2021) paper
- Structure Aware Single-stage 3D Object Detection from Point Cloud(CVPR2020) paper code
- MLCVNet: Multi-Level Context VoteNet for 3D Object Detection(CVPR2020) paper code
- 3DSSD: Point-based 3D Single Stage Object Detector(CVPR2020) paper code
- LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention(CVPR2020) paper code
- PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection(CVPR2020) paper code
- Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud(CVPR2020) paper code
- MLCVNet: Multi-Level Context VoteNet for 3D Object Detection(CVPR2020) paper
- Density Based Clustering for 3D Object Detection in Point Clouds(CVPR2020) paper
- What You See is What You Get: Exploiting Visibility for 3D Object Detection(CVPR2020) paper
- PointPainting: Sequential Fusion for 3D Object Detection(CVPR2020) paper
- HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection(CVPR2020) paper
- LiDAR R-CNN: An Efficient and Universal 3D Object Detector(CVPR2021) paper
- Center-based 3D Object Detection and Tracking(CVPR2021) paper
- 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection(CVPR2021) paper
- Embracing Single Stride 3D Object Detector with Sparse Transformer(CVPR2022) paper, code
- Point Density-Aware Voxels for LiDAR 3D Object Detection(CVPR2022) paper, code
- A Unified Query-based Paradigm for Point Cloud Understanding(CVPR2022) paper
- Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds(CVPR2022) paper, code
- Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds(CVPR2022) paper, code
- Back To Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement(CVPR2022) paper, code
- Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds(CVPR2022) paper, code
- BoxeR: Box-Attention for 2D and 3D Transformers(CVPR2022) paper, code, 中文介绍
- Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes(CVPR2022) paper, code
- DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection(CVPR2022) paper, code
- TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers. (CVPR2022) paper, code
- Point2Seq: Detecting 3D Objects as Sequences. (CVPR2022) paper, code
- CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection(CVPR2022) paper
- LiDAR Snowfall Simulation for Robust 3D Object Detection(CVPR2022) paper, code
- Unified Transformer Tracker for Object Tracking(CVPR2022) paper, code
- Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion(CVPR2022) paper
- M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation(CVPR2022) paper
- RBGNet: Ray-based Grouping for 3D Object Detection(CVPR2022) paper, code
- Fast Point Transformer paper
Competition Solution
Engineering
Survey
- 2021.04 Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy paper
- 2021.07 3D Object Detection for Autonomous Driving: A Survey paper
- 2021.07 Multi-Modal 3D Object Detection in Autonomous Driving: a Survey paper
- 2021.10 A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving paper
- 2021.12 Deep Learning for 3D Point Clouds: A Survey paper
Book
- 3D Object Detection Algorithms Based on Lidar and Camera: Design and Simulation book
Video
- Aivia online workshop: 3D object detection and tracking video
- 3D Object Retrieval 2021 workshop video
- 3D Deep Learning Tutorial from SU lab at UCSD video
- Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen) video
- Current Approaches and Future Directions for Point Cloud Object (2021.04) video
- Latest 3D OBJECT DETECTION with 30+ FPS on CPU - MediaPipe and OpenCV Python (2021.05) video
- MIT autonomous driving seminar (2019.11) video
- sensetime seminar1 video
- sensetime seminar2 slides
Course
- University of Toronto, csc2541
- University of Tübingen, Self-Driving Cars (Strong Recommendation)
- baidu-Udacity
- baidu-apollo
- University of Toronto, coursera
Blog
- Waymo Blog
- apollo介绍之Perception模块
- Apollo notes (Apollo学习笔记) - Apollo learning notes for beginners.
- PointNet系列论文解读
- Deep3dBox: 3D Bounding Box Estimation Using Deep Learning and Geometry
- SECOND算法解析
- PointRCNN深度解读
- Fast PointRCNN论文解读
- PointPillars论文和代码解析
- VoxelNet论文和代码解析
- CenterPoint源码分析
- PV-RCNN: 3D目标检测 Waymo挑战赛+KITTI榜 单模态第一算法
- LiDAR R-CNN:一种快速、通用的二阶段3D检测器
- 混合体素网络(HVNet)
- 自动驾驶感知| Range Image paper分享
- SST:单步长稀疏Transformer 3D物体检测器
Famous Research Group/Scholar
- Naiyan Wang@Tusimple
- Hongsheng Li@CUHK
- Oncel Tuzel@Apple
- Oscar Beijbom@nuTonomy
- Raquel Urtasun@University of Toronto
- Philipp Krähenbühl@UT Austin
- Deva Ramanan@CMU
- Jiaya Jia@CUHK
- Thomas Funkhouser@princeton
- Leonidas Guibas@Stanford
- Steven Waslander@University of Toronto
- Ouais Alsharif@Google Brain
- Yuning CHAI(former)@waymo
- Yulan Guo@NUDT
- Lei Zhang@The Hong Kong Polytechnic University
- Hongyang Li@sensetime
- Luc Van Gool@ETH
Famous CodeBase
- Point Cloud Library (PCL)
- Torchsparse
- Spconv
- Det3D
- mmdetection3d
- OpenPCDet
- Centerpoint
- Apollo Auto - Baidu open autonomous driving platform
- AutoWare - The University of Tokyo autonomous driving platform
- Openpilot - A open source software built to improve upon the existing driver assistance in most new cars on the road today