Package: mmdet3d
OpenMMLab websiteHOTOpenMMLab platformTRY IT OUT📘Documentation|🛠️Installation|👀Model Zoo|🆕Update News|🚀Ongoing Projects|🤔Reporting IssuesEnglish |简体中文IntroductionMMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is a part of theOpenMMLabproject.The main branch works withPyTorch 1.8+.Major featuresSupport multi-modality/single-modality detectors out of boxIt directly supports multi-modality/single-modality detectors including MVXNet, VoteNet, PointPillars, etc.Support indoor/outdoor 3D detection out of boxIt directly supports popular indoor and outdoor 3D detection datasets, including ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, and KITTI. For nuScenes dataset, we also supportnuImages dataset.Natural integration with 2D detectionAll the about300+ models, methods of 40+ papers, and modules supported inMMDetectioncan be trained or used in this codebase.High efficiencyIt trains faster than other codebases. The main results are as below. Details can be found inbenchmark.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✗.MethodsMMDetection3DOpenPCDetvotenetDet3DVoteNet358✗77✗PointPillars-car141✗✗140PointPillars-3class10744✗✗SECOND4030✗✗Part-A21714✗✗LikeMMDetectionandMMCV, MMDetection3D can also be used as a library to support different projects on top of it.What's NewHighlightIn 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 providedhere.Besides, in version 1.4, MMDetection3D providesWaymo-minito help community users get started with Waymo and use it for quick iterative development.v1.4.0was released in 8/1/2024：Support the training ofDSVTinprojectsSupportNerf-DetinprojectsRefactor Waymo datasetv1.3.0was released in 18/10/2023:SupportCENetinprojectsEnhance demos with new 3D inferencersv1.2.0was released in 4/7/2023SupportNew Config Typeinmmdet3d/configsSupport the inference ofDSVTinprojectsSupport downloading datasets fromOpenDataLabusingmimv1.1.1was released in 30/5/2023:SupportTPVFormerinprojectsSupport the training of BEVFusion inprojectsSupport lidar-based 3D semantic segmentation benchmarkInstallationPlease refer toInstallationfor installation instructions.Getting StartedFor detailed user guides and advanced guides, please refer to ourdocumentation:User GuidesTrain & TestLearn about ConfigsCoordinate SystemDataset PreparationCustomize Data PipelinesTest and Train on Standard DatasetsInferenceTrain with Customized DatasetsUseful ToolsAdvanced GuidesDatasetsKITTI DatasetNuScenes DatasetLyft DatasetWaymo DatasetSUN RGB-D DatasetScanNet DatasetS3DIS DatasetSemanticKITTI DatasetSupported TasksLiDAR-Based 3D DetectionVision-Based 3D DetectionLiDAR-Based 3D Semantic SegmentationCustomizationCustomize DatasetsCustomize ModelsCustomize Runtime SettingsOverview of Benchmark and Model ZooResults and models are available in themodel zoo.ComponentsBackbonesHeadsFeaturesPointNet (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)ArchitecturesLiDAR-based 3D Object DetectionCamera-based 3D Object DetectionMulti-modal 3D Object Detection3D Semantic SegmentationOutdoorSECOND (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)IndoorVoteNet (ICCV'2019)H3DNet (ECCV'2020)Group-Free-3D (ICCV'2021)FCAF3D (ECCV'2022)TR3D (ArXiv'2023)OutdoorImVoxelNet (WACV'2022)SMOKE (CVPRW'2020)FCOS3D (ICCVW'2021)PGD (CoRL'2021)MonoFlex (CVPR'2021)DETR3D (CoRL'2021)PETR (ECCV'2022)IndoorImVoxelNet (WACV'2022)OutdoorMVXNet (ICRA'2019)BEVFusion (ICRA'2023)IndoorImVoteNet (CVPR'2020)OutdoorMinkUNet (CVPR'2019)SPVCNN (ECCV'2020)Cylinder3D (CVPR'2021)TPVFormer (CVPR'2023)IndoorPointNet++ (NeurIPS'2017)PAConv (CVPR'2021)DGCNN (TOG'2019)ResNetVoVNetSwin-TPointNet++SECONDDGCNNRegNetXDLAMinkResNetCylinder3DMinkUNetSECOND✗✗✗✗✓✗✗✗✗✗✗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 about500+ models, methods of 90+ papersin 2D detection supported byMMDetectioncan be trained or used in this codebase.FAQPlease refer toFAQfor frequently asked questions.ContributingWe appreciate all contributions to improve MMDetection3D. Please refer toCONTRIBUTING.mdfor the contributing guideline.AcknowledgementMMDetection3D 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.CitationIf 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}}LicenseThis project is released under theApache 2.0 license.Projects in OpenMMLabMMEngine: 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: OpenMMLabAdvanced,Generative andIntelligentCreation toolbox.MMGeneration: OpenMMLab image and video generative models toolbox.MMDeploy: OpenMMLab model deployment framework.