Mmdet

Highlights

- Refactor points structure with more constructive and clearer implementation.
- Support axis-aligned IoU loss for VoteNet with better performance.
- Update and enhance [SECOND](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/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](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/second) benchmark on Waymo with stronger baselines. (#166)
- Add model zoo statistics and polish the documentation. (201)

Highlights

- Support a new method [SSN](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700579.pdf) 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](https://arxiv.org/abs/1901.07518) 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](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/nuimages) with models for reference on nuImages dataset (#155)
- Support [SSN](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/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](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/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](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/fp16) on KITTI and nuScenes (#178)

Highlights

- Support mixed precision training of voxel-based methods
- Support docker with PyTorch 1.6.0
- Update baseline configs and results ([CenterPoint](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/centerpoint) on nuScenes and [PointPillars](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/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](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/__init__.py) (#129)
- Add evaluation interval setting for CenterPoint (131)
- Add unit test for CenterPoint (133)
- Update [PointPillars](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/pointpillars) baselines on Waymo with full dataset (#142)
- Update [CenterPoint](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/centerpoint) results with models and logs (#154)

Highlights

- Support new methods [H3DNet](https://arxiv.org/abs/2006.05682), [3DSSD](https://arxiv.org/abs/2002.10187), [CenterPoint](https://arxiv.org/abs/2006.11275).
- Support new dataset [Waymo](https://waymo.com/open/) (with PointPillars baselines) and [nuImages](https://www.nuscenes.org/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](https://www.nuscenes.org/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](https://arxiv.org/abs/2006.05682) and release models on ScanNet dataset (#53, 58, 105)
- Support Fusion Point Sampling used in [3DSSD](https://arxiv.org/abs/2002.10187) (#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](https://arxiv.org/abs/2002.10187) (#96)
- Support [3DSSD](https://arxiv.org/abs/2002.10187) and release models on KITTI dataset (#83, 100, 104)
- Support [CenterPoint](https://arxiv.org/abs/2006.11275) and release models on nuScenes dataset (#49, 92)
- Support [Waymo](https://waymo.com/open/) 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)