List of 3D detection methods

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.

News

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

Paper list

Title	code	Pub.	Input
MV3D (Multi-View 3D Object Detection Network for Autonomous Driving)		CVPR2017	I+L
F-PointNet (Frustum PointNets for 3D Object Detection from RGB-D Data)	code	CVPR2018	I+L
VoxelNet (VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection)		CVPR2018	L
PIXOR (PIXOR: Real-time 3D Object Detection from Point Clouds)	code	CVPR2018	L
AVOD (Joint 3D Proposal Generation and Object Detection from View Aggregation)	code	IROS2018	I+L
ContFusion (Deep Continuous Fusion for Multi-Sensor 3D Object Detection)		ECCV2018	I+L
SECOND (SECOND: Sparsely Embedded Convolutional Detection)	code	Sensors 2018	L
Complex-YOLO (Complex-YOLO: Real-time 3D Object Detection on Point Clouds)	code	Axiv2018	L
FBF（Fusing Bird’s Eye View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection）	code	IV2018	I+L
RoarNet (RoarNet: A Robust 3D Object Detection based on Region Approximation Refinement)	code	IV2019	I+L
PVCNN (Point-Voxel CNN for Efficient 3D Deep Learning)	code	NIPS2019	L
MMF(Multi-Task Multi-Sensor Fusion for 3D Object Detection)	code	CVPR2019	I+L
PointPillars (PointPillars: Fast Encoders for Object Detection from Point Clouds)	code	CVPR2019	L
Point RCNN (PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud)	code	CVPR2019	L
LaserNet (LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving)		CVPR2019	L
LaserNet++ (Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation)		CVPR2019	I+L
**Fast PointRCNN **(Fast PointRCNN)		ICCV2019	L
STD (STD: Sparse-to-Dense 3D Object Detector for Point Cloud)		ICCV2019	L
VoteNet (Deep Hough Voting for 3D Object Detection in Point Clouds)	code	ICCV2019	L
MVX-Net (MVX-Net: Multimodal VoxelNet for 3D Object Detection)	code	ICRA2019	I+L
Patchs (Patch Refinement - Localized 3D Object Detection)		Arxiv2019	L
StarNet (StarNet: Targeted Computation for Object Detection in Point Clouds)	code	Arxiv2019	L
F-ConvNet (Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection)		IROS2019	I+L
PI-RCNN（An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module）		AAAI2020	I+L
TANet (TANet: Robust 3D Object Detection from Point Clouds with Triple Attention)	code	AAAI2020	L
MVF (End-to-end multi-view fusion for 3d object detection in lidar point clouds)	code	ICRL2020	L
SegVoxelNet (SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud)		ICRA2020	L
Voxel-FPN (Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds)		Sensors 2020	L
AA3D (Adaptive and Azimuth-Aware Fusion Network of Multimodal Local Features for 3D Object Detection)		Neurocomputing2020	I+L
Part A^2 (From Points to Parts: 3D Object Detection From Point Cloud With Part-Aware and Part-Aggregation Network)	code	TPAMI2020	L
PV-RCNN (PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection)	code	CVPR2020	L
3D SSD (3DSSD: Point-based 3D Single Stage Object Detector)	code	CVPR2020	L
Associate-3Ddet (Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection)	code	CVPR2020	L
HVNet (HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection)	code	CVPR2020	L
ImVoteNet (ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes)		CVPR2020	I+L
Point GNN (Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud)		CVPR2020	L
SA-SSD (Structure Aware Single-stage 3D Object Detection from Point Cloud)	code	CVPR2020	L
(What You See is What You Get: Exploiting Visibility for 3D Object Detection)		CVPR2020	L
DOPS (DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes)		CVPR2020	L
3D IoU-Net (3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds)		Arxiv2020	L
3D CVF (3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection)		ECCV2020	I+L
HotSpotNet (Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots)		ECCV2020	L
EPNet: (EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection)	code	ECCV2020	I+L
WS3D (Weakly Supervised 3D Object Detection from Lidar Point Cloud)	code	ECCV2020	L
Pillar-OD Pillar-based Object Detection for Autonomous Driving	code	ECCV2020	L
SSN (SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds)		Arxiv2020	L
CenterPoint (Center-based 3D Object Detection and Tracking)	code	Arxiv2020	L
AFDet (AFDet: Anchor Free One Stage 3D Object Detection)		Waymo2020	L
LGR-Net (Local Grid Rendering Networks for 3D Object Detection in Point Clouds)		arxiv2020.07	L
CenterNet3D (CenterNet3D:An Anchor free Object Detector for Autonomous Driving)	code	arxiv2020.07	L
RCD (Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection)		CoRL 2020	L
VS3D (Weakly Supervised 3D Object Detection from Point Clouds)	code	ACM MM2020	I+L
RangeRCNN (RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation)		arxiv2020.09	L
MVAF-Net (Multi-View Adaptive Fusion Network for 3D Object Detection)		arxiv2020.11	I+L
CADNet (Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds)		TITS 2021.05	L
DA-PointRCNN (A Density-Aware PointRCNN for 3D Objection Detection in Point Clouds)		axiv2020.09	L
CVCNet(Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization)		NIPS2020	L
CIA-SSD（CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud）	code	AAAI2021	L
IAAY（It's All Around You: Range-Guided Cylindrical Network for 3D Object Detection）		arxiv2020	L
SA-Det3D (Self-Attention Based Context-Aware 3D Object Detection)	code	arxiv2020	L
RangeDet(RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection)		ICCV2021	L
HVPR(HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection)		CVPR2021	L
SE-SSD(SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud)		CVPR2021	L
PPC(To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels)		CVPR2021	L
SIENet (SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud)		arxiv2021.04	L
PolarStream(PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars)		arxiv2021.06	L
DV-Det(DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization)	code	arxiv2021.07	L
VPFNet （VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion）		arxiv2021.11	I+L
LC-MV (Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving)		WACV2022	I+L
BtcDet (Behind the Curtain: Learning Occluded Shapes for 3D Object Detection)	code	AAAI2022	L
To be continued...

Code list

• 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

Dataset list

(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