最近自动驾驶是一个小风口，小菜鸡凑巧被mentor分配了研究3d点云检测，于是搭上了这路末班车，但在研究这一块的时候发现：
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- 3d点云检测目前还处于2d检测领域初中期的阶段，学习资料有点少，导致我初期学的时候不清楚常见的trick有哪些，改哪些模块可能会有效。说的更简单一点，3d检测入门前期快速培养intuition太难，不如2d检测容易。
- 大家更多的关注的都是精度，可能偶尔会有一些零零散散的文章关注速度。初期找这一部分文章花了很久很久。
- 没有一个随时更新的paper list供个人来follow学术前沿。比如现在cvpr2022结果出来了，但还没有人汇总cvpr20223d检测的文章。
鉴于以上原因，小菜鸡在GitHub总结了一个相关资源的list，【如果你觉得有用，请给我点个star，感谢～】
并且会不断维护更新，无他，只是不想自己走过的弯路让别人再走一遍，希望各路大神能前来指点一二。
如何用好这个repo?
- 大致扫完一眼后，快速进入blog部分，学习经典方法。
- 进入course部分，学习图宾根大学课程的3d检测部分。
- 进入video部分，看3d检测相关的seminar。
- 现在，你已经大致具备3d检测领域的intuition了，之后
- 如果你想发论文，你可以进入paper部分。
- 如果你想打比赛，你可以进入competition solution部分。【等待更新】
- 如果你想做工程，你可以进入engineering部分。【等待更新】
repo内容有哪些？【部分内容节选】
Dataset
3,712 training samples
3,769 validation samples
7,518 testing samples
28k training samples
6k validation samples
6k testing 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
- CVPR 2021 Workshop on Autonomous Driving(waymo 3D detection)
- ICCV 2021 Workshop on Autonomous Vehicle Vision (AVVision), note
- ICCV 2021 Workshop SSLAD Track 2 - 3D Object Detection
- ECCV 2020 Workshop on Perception for Autonomous Driving
Paper (Lidar-based method)
- 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
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
Course
- University of Toronto, csc2541
- University of Tübingen, Self-Driving Cars(Strong Recommendation)
Blog
- Waymo Blog
- 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
Famous CodeBase
Famous Toolkit
以上就是一个小小的简短的介绍，未完待续，之后的文章将具体讲一些3d检测领域的paper套路、比赛套路、工程套路等等。【自己总结的肯定和各路大神比不了，所以非常非常欢迎各路大神能前来和小菜鸡交流】
希望大家多多多提一些意见，共同进步！
