MMDetection3D V0.8.0 Release

v0.8.0 (30/11/2020)

Highlights

• Refactor points structure with more constructive and clearer implementation.
• Support axis-aligned IoU loss for VoteNet with better performance.
• Update and enhance SECOND benchmark on Waymo.

New Features

• Support axis-aligned IoU loss for VoteNet. (#194)
• Support points structure for consistent processing of all the point related representation. (#196, #204)

Improvements

• Enhance SECOND benchmark on Waymo with stronger baselines. (#166)
• Add model zoo statistics and polish the documentation. (#201)

MMDetection3D V0.7.0 Release

Highlights

• Support a new method SSN with benchmarks on nuScenes and Lyft datasets.
• Update benchmarks for SECOND on Waymo, CenterPoint with TTA on nuScenes and models with mixed precision training on KITTI and nuScenes.
• Support semantic segmentation on nuImages and provide HTC models with configurations and performance for reference.

Bug Fixes

• Fix incorrect code weights in anchor3d_head when introducing mixed precision training (#173)
• Fix the incorrect label mapping on nuImages dataset (#155)

New Features

• Modified primitive head which can support the setting on SUN-RGBD dataset (#136)
• Support semantic segmentation and HTC with models for reference on nuImages dataset (#155)
• Support SSN on nuScenes and Lyft datasets (#147, #174, #166, #182)
• Support double flip for test time augmentation of CenterPoint with updated benchmark (#143)

Improvements

• Update SECOND benchmark with configurations for reference on Waymo (#166)
• Delete checkpoints on Waymo to comply its specific license agreement (#180)
• Update models and instructions with mixed precision training on KITTI and nuScenes (#178)

MMDetection3D V0.6.1 Release

Highlights

• Support mixed precision training of voxel-based methods
• Support docker with PyTorch 1.6.0
• Update baseline configs and results (CenterPoint on nuScenes and PointPillars on Waymo with full dataset)
• Switch model zoo to download.openmmlab.com

Bug Fixes

• Fix a bug of visualization in multi-batch case (#120)
• Fix bugs in DCN unit test (#130)
• Fix DCN bias bug in CenterPoint (#137)
• Fix dataset mapping in the evaluation of nuScenes mini dataset (#140)
• Fix origin initialization in CameraInstance3DBoxes (#148, #150)
• Correct documentation link in the getting_started.md (#159)
• Fix model save path bug in gather_models.py (#153)
• Fix image padding shape bug in PointFusion (#162)

New Features

• Support dataset pipeline VoxelBasedPointSampler to sample multi-sweep points based on voxelization. (#125)
• Support mixed precision training of voxel-based methods (#132)
• Support docker with PyTorch 1.6.0 (#160)

Improvements

• Reduce requirements for the case exclusive of Waymo (#121)
• Switch model zoo to download.openmmlab.com (#126)
• Update docs related to Waymo (#128)
• Add version assertion in the init file (#129)
• Add evaluation interval setting for CenterPoint (#131)
• Add unit test for CenterPoint (#133)
• Update PointPillars baselines on Waymo with full dataset (#142)
• Update CenterPoint results with models and logs (#154)

MMDetection3D V0.6.0 Release

Highlights

• Support new methods H3DNet, 3DSSD, CenterPoint.
• Support new dataset Waymo (with PointPillars baselines) and nuImages (with Mask R-CNN and Cascade Mask R-CNN baselines).
• Support Batch Inference
• Support Pytorch 1.6
• Start to publish mmdet3d package to PyPI since v0.5.0. You can use mmdet3d through pip install mmdet3d.

Backwards Incompatible Changes

• Support Batch Inference (#95, #103, #116): MMDetection3D v0.6.0 migrates to support batch inference based on MMDetection >= v2.4.0. This change influences all the test APIs in MMDetection3D and downstream codebases.
• Start to use collect environment function from MMCV (#113): MMDetection3D v0.6.0 migrates to use collect_env function in MMCV.
  get_compiler_version and get_compiling_cuda_version compiled in mmdet3d.ops.utils are removed. Please import these two functions from mmcv.ops.

Bug Fixes

• Rename CosineAnealing to CosineAnnealing (#57)
• Fix device inconsistant bug in 3D IoU computation (#69)
• Fix a minor bug in json2csv of lyft dataset (#78)
• Add missed test data for pointnet modules (#85)
• Fix use_valid_flag bug in CustomDataset (#106)

New Features

• Support nuImages dataset by converting them into coco format and release Mask R-CNN and Cascade Mask R-CNN baseline models (#91, #94)
• Support to publish to PyPI in github-action (#17, #19, #25, #39, #40)
• Support CBGSDataset and make it generally applicable to all the supported datasets (#75, #94)
• Support H3DNet and release models on ScanNet dataset (#53, #58, #105)
• Support Fusion Point Sampling used in 3DSSD (#66)
• Add BackgroundPointsFilter to filter background points in data pipeline (#84)
• Support pointnet2 with multi-scale grouping in backbone and refactor pointnets (#82)
• Support dilated ball query used in 3DSSD (#96)
• Support 3DSSD and release models on KITTI dataset (#83, #100, #104)
• Support CenterPoint and release models on nuScenes dataset (#49, #92)
• Support Waymo dataset and release PointPillars baseline models (#118)
• Allow LoadPointsFromMultiSweeps to pad empty sweeps and select multiple sweeps randomly (#67)

Improvements

• Fix all warnings and bugs in Pytorch 1.6.0 (#70, #72)
• Update issue templates (#43)
• Update unit tests (#20, #24, #30)
• Update documentation for using ply format point cloud data (#41)
• Use points loader to load point cloud data in ground truth (GT) samplers (#87)
• Unify version file of OpenMMLab projects by using version.py (#112)
• Remove unnecessary data preprocessing commands of SUN RGB-D dataset (#110)