This is a paper and code list of some awesome 3D detection methods. We mainly collect LiDAR-involved methods in autonomous driving. It is worth noticing that we include both official and unofficial codes for each paper.

2022.2.17 Add the link for every paper. (Happy Chinese New Year!)

• mmdetection3d in pytorch

  Methods supported: SECOND, PointPillars, FreeAnchor, VoteNet, Part-A2, MVXNet

  Benchmark supported: KITTI, nuScenes, Lyft, ScanNet, SUNRGBD

• OpenPCDet: An open source project for LiDAR-based 3D scene perception in Pytorch.

  Methods supported : PointPillars, SECOND, Part A^2, PV-RCNN, PointRCNN(ongoing).

  Benchmark supported: KITTI, Waymo (ongoing).

• Det3d: A general 3D Object Detection codebase in PyTorch.

  Methods supported : PointPillars, SECOND, PIXOR.

  Benchmark supported: KITTI, nuScenes, Lyft.

• second.pytorch: SECOND detector in Pytorch.

  Methods supported : PointPillars, SECOND.

  Benchmark supported: KITTI, nuScenes.

• CenterPoint: "Center-based 3D Object Detection and Tracking" in Pytorch.

  Methods supported : CenterPoint-Pillar, Center-Voxel.

  Benchmark supported: nuScenes，Waymo.

• SA-SSD: "SA-SSD: Structure Aware Single-stage 3D Object Detection from Point Cloud" in pytorch

  Methods supported : SA-SSD.

  Benchmark supported: KITTI.

• 3DSSD: "Point-based 3D Single Stage Object Detector " in Tensorflow.

  Methods supported : 3DSSD, PointRCNN, STD (ongoing).

  Benchmark supported: KITTI, nuScenes (ongoing).

• Point-GNN: "Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud" in Tensorflow.

  Methods supported : Point-GNN.

  Benchmark supported: KITTI.

• TANet: "TANet: Robust 3D Object Detection from Point Clouds with Triple Attention" in Pytorch.

  Methods supported : TANet (PointPillars, Second).

  Benchmark supported: KITTI.

• Complex-YOLOv4-pytorch: " Complex-YOLO: Real-time 3D Object Detection on Point Clouds)" in pytorch.

  Methods supported : YOLO

  Benchmark supported: KITTI.

• EPNet: "EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection "

  Methods supported: EPNet

  Benchmark supported: KITTI, SUN-RGBD

• Super Fast and Accurate 3D Detector:"Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds"

  Benchmark supported: KITTI

(reference: https://mp.weixin.qq.com/s/3mpbulAgiwi5J66MzPNpJA from WeChat official account: "CNNer")

• KITTI

  Website: http://www.cvlibs.net/datasets/kitti/raw_data.php

  Paper: http://www.cvlibs.net/publications/Geiger2013IJRR.pdf

• Waymo

  Website: https://waymo.com/open

  Paper: https://arxiv.org/abs/1912.04838v5

• NuScenes

  Website: https://www.nuscenes.org/

  Paper: https://arxiv.org/abs/1903.11027

• Lyft

  Website: https://level5.lyft.com/

  Paper: https://level5.lyft.com/dataset/

• Audi autonomous driving dataset

  Website: http://www.a2d2.audi

  Paper: https://arxiv.org/abs/2004.06320

• Apollo

  Website: http://apolloscape.auto/

  Paper: https://arxiv.org/pdf/1803.06184.pdf