最近自動駕駛是一個小風(fēng)口，小菜雞湊巧被mentor分配了研究3d點云檢測，于是搭上了這路末班車，但在研究這一塊的時候發(fā)現(xiàn)：
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- 3d點云檢測目前還處于2d檢測領(lǐng)域初中期的階段，學(xué)習(xí)資料有點少，導(dǎo)致我初期學(xué)的時候不清楚常見的trick有哪些，改哪些模塊可能會有效。說的更簡單一點，3d檢測入門前期快速培養(yǎng)intuition太難，不如2d檢測容易。
- 大家更多的關(guān)注的都是精度，可能偶爾會有一些零零散散的文章關(guān)注速度。初期找這一部分文章花了很久很久。
- 沒有一個隨時更新的paper list供個人來follow學(xué)術(shù)前沿。比如現(xiàn)在cvpr2022結(jié)果出來了，但還沒有人匯總cvpr20223d檢測的文章。
鑒于以上原因，小菜雞在GitHub總結(jié)了一個相關(guān)資源的list，【如果你覺得有用，請給我點個star，感謝～】
并且會不斷維護更新，無他，只是不想自己走過的彎路讓別人再走一遍，希望各路大神能前來指點一二。
如何用好這個repo?
- 大致掃完一眼后，快速進入blog部分，學(xué)習(xí)經(jīng)典方法。
- 進入course部分，學(xué)習(xí)圖賓根大學(xué)課程的3d檢測部分。
- 進入video部分，看3d檢測相關(guān)的seminar。
- 現(xiàn)在，你已經(jīng)大致具備3d檢測領(lǐng)域的intuition了，之后
- 如果你想發(fā)論文，你可以進入paper部分。
- 如果你想打比賽，你可以進入competition solution部分。【等待更新】
- 如果你想做工程，你可以進入engineering部分?！镜却隆?/li>
repo內(nèi)容有哪些？【部分內(nèi)容節(jié)選】
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系列論文解讀
- Deep3dBox: 3D Bounding Box Estimation Using Deep Learning and Geometry
- SECOND算法解析
- PointRCNN深度解讀
- Fast PointRCNN論文解讀
- PointPillars論文和代碼解析
- VoxelNet論文和代碼解析
- CenterPoint源碼分析
- PV-RCNN: 3D目標(biāo)檢測 Waymo挑戰(zhàn)賽+KITTI榜 單模態(tài)第一算法
- LiDAR R-CNN：一種快速、通用的二階段3D檢測器
- 混合體素網(wǎng)絡(luò)（HVNet)
- 自動駕駛感知| Range Image paper分享
- SST：單步長稀疏Transformer 3D物體檢測器
Famous Research Group/Scholar
- Naiyan Wang@Tusimple
- Hongsheng Li@CUHK
- Oncel Tuzel@Apple
- Oscar Beijbom@nuTonomy
- Raquel Urtasun@University of Toronto
- Philipp Kr?henbühl@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
以上就是一個小小的簡短的介紹，未完待續(xù)，之后的文章將具體講一些3d檢測領(lǐng)域的paper套路、比賽套路、工程套路等等。【自己總結(jié)的肯定和各路大神比不了，所以非常非常歡迎各路大神能前來和小菜雞交流】
希望大家多多多提一些意見，共同進步！
