Need help with Awesome-3D-Detectors?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

Hub-Tian
231 Stars 30 Forks 47 Commits 1 Opened issues

Description

Paperlist of awesome 3D detection methods

Services available

!
?

Need anything else?

Contributors list

No Data

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.

paperlist-map

News

2021.6.30 a.m. Add PPC from waymo.

2020.4.26 p.m. Add HVPR and SE-SSD.

2020.3.25 p.m. Add RangeDet.

2021.1.11 p.m. Add SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection .

2020.12.08 p.m. Add CIA-SSD: An IoU-Aware Single-Stage Object Detector .

Paper list

| Title | 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 | Arxiv2018 | 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 (Part-A^ 2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud) 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) | arxiv2020.06 | L | | VS3D (Weakly Supervised 3D Object Detection from Point Clouds) code | ACM MM2020 | I+L | | LC-MV (Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving) | CoRL2020 | 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) | arxiv2020.07 | 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 | | IAAYIt'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) | arxiv2021 | 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 | | To be continued... | | |

Code list

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.

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: nuScenesWaymo.

  • 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.

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

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

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.