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Maintenance Ask Me Anything ! Awesome GitHub license

Awesome-3D-Object-Detection

A curated list of research in 3D Object Detection(Lidar-based Method).

You are very welcome to pull request to update this list. 😃
3D Object Detection

Dataset

  • KITTI Dataset

    • 3,712 training samples
    • 3,769 validation samples
    • 7,518 testing samples
  • nuScenes Dataset

    • 28k training samples
    • 6k validation samples
    • 6k testing samples
  • Lyft Dataset

  • Waymo Open Dataset

    • 798 training sequences with around 158, 361 LiDAR samples
    • 202 validation sequences with 40, 077 LiDAR 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

Paper (Lidar-based method)

  • End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds paper
  • Vehicle Detection from 3D Lidar Using Fully Convolutional Network(baidu) paper
  • VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection paper
  • Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks paper
  • RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving paper
  • BirdNet: a 3D Object Detection Framework from LiDAR information paper
  • LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDAR paper
  • HDNET: Exploit HD Maps for 3D Object Detection paper
  • PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation paper
  • PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space paper
  • IPOD: Intensive Point-based Object Detector for Point Cloud paper
  • PIXOR: Real-time 3D Object Detection from Point Clouds paper
  • DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet paper
  • Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds paper
  • STD: Sparse-to-Dense 3D Object Detector for Point Cloud paper
  • Fast Point R-CNN paper
  • StarNet: Targeted Computation for Object Detection in Point Clouds paper
  • Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection paper
  • LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving paper
  • FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds paper
  • Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud paper
  • PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud paper
  • Complex-YOLO: Real-time 3D Object Detection on Point Clouds paper
  • YOLO4D: A ST Approach for RT Multi-object Detection and Classification from LiDAR Point Clouds paper
  • YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud paper
  • Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud paper
  • Pillar-based Object Detection for Autonomous Driving (ECCV2020) paper
  • EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection(ECCV2020) paper
  • Multi-Echo LiDAR for 3D Object Detection(ICCV2021) paper
  • LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector(ICCV2021) paper
  • SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation(ICCV2021) paper
  • Structure Aware Single-stage 3D Object Detection from Point Cloud(CVPR2020) paper code
  • MLCVNet: Multi-Level Context VoteNet for 3D Object Detection(CVPR2020) paper code
  • 3DSSD: Point-based 3D Single Stage Object Detector(CVPR2020) paper code
  • LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention(CVPR2020) paper code
  • PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection(CVPR2020) paper code
  • Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud(CVPR2020) paper code
  • MLCVNet: Multi-Level Context VoteNet for 3D Object Detection(CVPR2020) paper
  • Density Based Clustering for 3D Object Detection in Point Clouds(CVPR2020) paper
  • What You See is What You Get: Exploiting Visibility for 3D Object Detection(CVPR2020) paper
  • PointPainting: Sequential Fusion for 3D Object Detection(CVPR2020) paper
  • 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
  • BoxeR: Box-Attention for 2D and 3D Transformers(CVPR2022) paper, code, 中文介绍
  • Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes(CVPR2022) paper, code
  • DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection(CVPR2022) paper, code
  • TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers. (CVPR2022) paper, code
  • Point2Seq: Detecting 3D Objects as Sequences. (CVPR2022) paper, code
  • CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection(CVPR2022) paper
  • LiDAR Snowfall Simulation for Robust 3D Object Detection(CVPR2022) paper, code
  • Unified Transformer Tracker for Object Tracking(CVPR2022) paper, code
  • Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion(CVPR2022) paper
  • M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation(CVPR2022) paper
  • RBGNet: Ray-based Grouping for 3D Object Detection(CVPR2022) paper, code
  • Fast Point Transformer(CVPR2022) paper
  • Focal Sparse Convolutional Networks for 3D Object Detection(CVPR2022) paper, code
  • FUTR3D: A Unified Sensor Fusion Framework for 3D Detection(CVPR2022) paper
  • VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention(CVPR2022) paper, code
  • OccAM’s Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data(CVPR2022) paper
  • Voxel Field Fusion for 3D Object Detection(CVPR2022) paper, code
  • FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels
  • LinK: Linear Kernel for LiDAR-based 3D Perception(CVPR2023) paper, code
  • DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets(CVPR2023) paper, code
  • VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking(CVPR2023) paper, code
  • LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs(CVPR2023) paper, code
  • FocalFormer3D : Focusing on Hard Instance for 3D Object Detection(ICCV2023) paper, code
  • CTRL: Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection(ICCV2023) paper, code
  • Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection(arxiv2023) paper, code

Competition Solution

Engineering

  • Pointpillars-ONNX code
  • Centerpoint-ONNX code
  • BEVFormer-TensorRT 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
  • MIT autonomous driving seminar (2019.11) video
  • sensetime seminar1 video
  • sensetime seminar2 slides

Course

Blog

Famous Research Group/Scholar

Famous CodeBase

Famous Toolkit

Acknowlegement

Awesome System for Machine Learning

awesome-3D-object-detection