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Introduction

MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is a part of the OpenMMLab project.

The main branch works with PyTorch 1.8+.

Major features

• Support multi-modality/single-modality detectors out of box

  It directly supports multi-modality/single-modality detectors including MVXNet, VoteNet, PointPillars, etc.

• Support indoor/outdoor 3D detection out of box

  It directly supports popular indoor and outdoor 3D detection datasets, including ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, and KITTI. For nuScenes dataset, we also support nuImages dataset.

• Natural integration with 2D detection

  All the about 300+ models, methods of 40+ papers, and modules supported in MMDetection can be trained or used in this codebase.

• High efficiency

  It trains faster than other codebases. The main results are as below. Details can be found in benchmark.md. We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by ✗.

  Methods	MMDetection3D	OpenPCDet	votenet	Det3D
  VoteNet	358	✗	77	✗
  PointPillars-car	141	✗	✗	140
  PointPillars-3class	107	44	✗	✗
  SECOND	40	30	✗	✗
  Part-A2	17	14	✗	✗

Like MMDetection and MMCV, MMDetection3D can also be used as a library to support different projects on top of it.

What's New

Highlight

In version 1.4, MMDetecion3D refactors the Waymo dataset and accelerates the preprocessing, training/testing setup, and evaluation of Waymo dataset. We also extends the support for camera-based, such as Monocular and BEV, 3D object detection models on Waymo. A detailed description of the Waymo data information is provided here.

Besides, in version 1.4, MMDetection3D provides Waymo-mini to help community users get started with Waymo and use it for quick iterative development.

v1.4.0 was released in 8/1/2024：

• Support the training of DSVT in projects
• Support Nerf-Det in projects
• Refactor Waymo dataset

v1.3.0 was released in 18/10/2023:

• Support CENet in projects
• Enhance demos with new 3D inferencers

v1.2.0 was released in 4/7/2023

• Support New Config Type in mmdet3d/configs
• Support the inference of DSVT in projects
• Support downloading datasets from OpenDataLab using mim

v1.1.1 was released in 30/5/2023:

• Support TPVFormer in projects
• Support the training of BEVFusion in projects
• Support lidar-based 3D semantic segmentation benchmark

Installation

Please refer to Installation for installation instructions.

Getting Started

For detailed user guides and advanced guides, please refer to our documentation:

User Guides

• Train & Test
  • Learn about Configs
  • Coordinate System
  • Dataset Preparation
  • Customize Data Pipelines
  • Test and Train on Standard Datasets
  • Inference
  • Train with Customized Datasets
• Useful Tools

Advanced Guides

• Datasets
  • KITTI Dataset
  • NuScenes Dataset
  • Lyft Dataset
  • Waymo Dataset
  • SUN RGB-D Dataset
  • ScanNet Dataset
  • S3DIS Dataset
  • SemanticKITTI Dataset
• Supported Tasks
  • LiDAR-Based 3D Detection
  • Vision-Based 3D Detection
  • LiDAR-Based 3D Semantic Segmentation
• Customization
  • Customize Datasets
  • Customize Models
  • Customize Runtime Settings

Overview of Benchmark and Model Zoo

Results and models are available in the model zoo.

Components

Backbones	Heads	Features
PointNet (CVPR'2017) PointNet++ (NeurIPS'2017) RegNet (CVPR'2020) DGCNN (TOG'2019) DLA (CVPR'2018) MinkResNet (CVPR'2019) MinkUNet (CVPR'2019) Cylinder3D (CVPR'2021)	FreeAnchor (NeurIPS'2019)	Dynamic Voxelization (CoRL'2019)

Architectures

LiDAR-based 3D Object Detection	Camera-based 3D Object Detection	Multi-modal 3D Object Detection	3D Semantic Segmentation
Outdoor SECOND (Sensor'2018) PointPillars (CVPR'2019) SSN (ECCV'2020) 3DSSD (CVPR'2020) SA-SSD (CVPR'2020) PointRCNN (CVPR'2019) Part-A2 (TPAMI'2020) CenterPoint (CVPR'2021) PV-RCNN (CVPR'2020) CenterFormer (ECCV'2022) Indoor VoteNet (ICCV'2019) H3DNet (ECCV'2020) Group-Free-3D (ICCV'2021) FCAF3D (ECCV'2022) TR3D (ArXiv'2023)	Outdoor ImVoxelNet (WACV'2022) SMOKE (CVPRW'2020) FCOS3D (ICCVW'2021) PGD (CoRL'2021) MonoFlex (CVPR'2021) DETR3D (CoRL'2021) PETR (ECCV'2022) Indoor ImVoxelNet (WACV'2022)	Outdoor MVXNet (ICRA'2019) BEVFusion (ICRA'2023) Indoor ImVoteNet (CVPR'2020)	Outdoor MinkUNet (CVPR'2019) SPVCNN (ECCV'2020) Cylinder3D (CVPR'2021) TPVFormer (CVPR'2023) Indoor PointNet++ (NeurIPS'2017) PAConv (CVPR'2021) DGCNN (TOG'2019)

	ResNet	VoVNet	Swin-T	PointNet++	SECOND	DGCNN	RegNetX	DLA	MinkResNet	Cylinder3D	MinkUNet
SECOND	✗	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗
PointPillars	✗	✗	✗	✗	✓	✗	✓	✗	✗	✗	✗
FreeAnchor	✗	✗	✗	✗	✗	✗	✓	✗	✗	✗	✗
VoteNet	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗	✗
H3DNet	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗	✗
3DSSD	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗	✗
Part-A2	✗	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗
MVXNet	✓	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗
CenterPoint	✗	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗
SSN	✗	✗	✗	✗	✗	✗	✓	✗	✗	✗	✗
ImVoteNet	✓	✗	✗	✓	✗	✗	✗	✗	✗	✗	✗
FCOS3D	✓	✗	✗	✗	✗	✗	✗	✗	✗	✗	✗
PointNet++	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗	✗
Group-Free-3D	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗	✗
ImVoxelNet	✓	✗	✗	✗	✗	✗	✗	✗	✗	✗	✗
PAConv	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗	✗
DGCNN	✗	✗	✗	✗	✗	✓	✗	✗	✗	✗	✗
SMOKE	✗	✗	✗	✗	✗	✗	✗	✓	✗	✗	✗
PGD	✓	✗	✗	✗	✗	✗	✗	✗	✗	✗	✗
MonoFlex	✗	✗	✗	✗	✗	✗	✗	✓	✗	✗	✗
SA-SSD	✗	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗
FCAF3D	✗	✗	✗	✗	✗	✗	✗	✗	✓	✗	✗
PV-RCNN	✗	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗
Cylinder3D	✗	✗	✗	✗	✗	✗	✗	✗	✗	✓	✗
MinkUNet	✗	✗	✗	✗	✗	✗	✗	✗	✗	✗	✓
SPVCNN	✗	✗	✗	✗	✗	✗	✗	✗	✗	✗	✓
BEVFusion	✗	✗	✓	✗	✓	✗	✗	✗	✗	✗	✗
CenterFormer	✗	✗	✗	✗	✓	✗	✗	✗	✗	✗	✗
TR3D	✗	✗	✗	✗	✗	✗	✗	✗	✓	✗	✗
DETR3D	✓	✓	✗	✗	✗	✗	✗	✗	✗	✗	✗
PETR	✗	✓	✗	✗	✗	✗	✗	✗	✗	✗	✗
TPVFormer	✓	✗	✗	✗	✗	✗	✗	✗	✗	✗	✗

Note: All the about 500+ models, methods of 90+ papers in 2D detection supported by MMDetection can be trained or used in this codebase.

FAQ

Please refer to FAQ for frequently asked questions.

Contributing

We appreciate all contributions to improve MMDetection3D. Please refer to CONTRIBUTING.md for the contributing guideline.

Acknowledgement

MMDetection3D is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new 3D detectors.

Citation

If you find this project useful in your research, please consider cite:

@misc{mmdet3d2020,
    title={{MMDetection3D: OpenMMLab} next-generation platform for general {3D} object detection},
    author={MMDetection3D Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmdetection3d}},
    year={2020}
}

License

This project is released under the Apache 2.0 license.

Projects in OpenMMLab

• MMEngine: OpenMMLab foundational library for training deep learning models.
• MMCV: OpenMMLab foundational library for computer vision.
• MMEval: A unified evaluation library for multiple machine learning libraries.
• MIM: MIM installs OpenMMLab packages.
• MMPreTrain: OpenMMLab pre-training toolbox and benchmark.
• MMDetection: OpenMMLab detection toolbox and benchmark.
• MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
• MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
• MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
• MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
• MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
• MMPose: OpenMMLab pose estimation toolbox and benchmark.
• MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
• MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
• MMRazor: OpenMMLab model compression toolbox and benchmark.
• MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
• MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
• MMTracking: OpenMMLab video perception toolbox and benchmark.
• MMFlow: OpenMMLab optical flow toolbox and benchmark.
• MMagic: OpenMMLab Advanced, Generative and Intelligent Creation toolbox.
• MMGeneration: OpenMMLab image and video generative models toolbox.
• MMDeploy: OpenMMLab model deployment framework.