changelog.md

Changelog

v0.16.0 (1/8/2021)

Compatibility

• Remove the rotation and dimension hack in the monocular 3D detection on nuScenes by applying corresponding transformation in the pre-processing and post-processing. The modification only influences nuScenes coco-style json files. Please re-run the data preparation scripts if necessary. See more details in the PR #744.
• Add a new pre-processing module for the ScanNet dataset in order to support multi-view detectors. Please run the updated scripts to extract the RGB data and its annotations. See more details in the PR #696.

Highlights

• Support to use MIM with pip installation
• Support PAConv models and benchmarks on S3DIS
• Enhance the documentation especially on dataset tutorials

New Features

• Support RGB images on ScanNet for multi-view detectors (#696)
• Support FLOPs and number of parameters calculation (#736)
• Support to use MIM with pip installation (#782)
• Support PAConv models and benchmarks on the S3DIS dataset (#783, #809)

Improvements

• Refactor Group-Free-3D to make it inherit BaseModule from MMCV (#704)
• Modify the initialization methods of FCOS3D to be consistent with the refactored approach (#705)
• Benchmark the Group-Free-3D models on ScanNet (#710)
• Add Chinese Documentation for Getting Started (#725), FAQ (#730), Model Zoo (#735), Demo (#745), Quick Run (#746), Data Preparation (#787) and Configs (#788)
• Add documentation for semantic segmentation on ScanNet and S3DIS (#743, #747, #806, #807)
• Add a parameter max_keep_ckpts to limit the maximum number of saved Group-Free-3D checkpoints (#765)
• Add documentation for 3D detection on SUN RGB-D and nuScenes (#770, #793)
• Remove mmpycocotools in the Dockerfile (#785)

Bug Fixes

• Fix versions of OpenMMLab dependencies (#708)
• Convert rt_mat to torch.Tensor in coordinate transformation for compatibility (#709)
• Fix the bev_range initialization in ObjectRangeFilter according to the gt_bboxes_3d type (#717)
• Fix Chinese documentation and incorrect doc format due to the incompatible Sphinx version (#718)
• Fix a potential bug when setting interval == 1 in analyze_logs.py (#720)
• Update the structure of Chinese Documentation (#722)
• Fix FCOS3D FPN BC-Breaking caused by the code refactoring in MMDetection (#739)
• Fix wrong in_channels when with_distance=True in the Dynamic VFE Layers (#749)
• Fix the dimension and yaw hack of FCOS3D on nuScenes (#744, #794, #795, #818)
• Fix the missing default bbox_mode in the show_multi_modality_result (#825)

Contributors

A total of 12 developers contributed to this release.

@yinchimaoliang, @gopi231091, @filaPro, @ZwwWayne, @ZCMax, @hjin2902, @wHao-Wu, @Wuziyi616, @xiliu8006, @THU17cyz, @DCNSW, @Tai-Wang

v0.15.0 (1/7/2021)

Compatibility

In order to fix the problem that the priority of EvalHook is too low, all hook priorities have been re-adjusted in 1.3.8, so MMDetection 2.14.0 needs to rely on the latest MMCV 1.3.8 version. For related information, please refer to #1120, for related issues, please refer to #5343.

Highlights

• Support PAConv
• Support monocular/multi-view 3D detector ImVoxelNet on KITTI
• Support Transformer-based 3D detection method Group-Free-3D on ScanNet
• Add documentation for tasks including LiDAR-based 3D detection, vision-only 3D detection and point-based 3D semantic segmentation
• Add dataset documents like ScanNet

New Features

• Support Group-Free-3D on ScanNet (#539)
• Support PAConv modules (#598, #599)
• Support ImVoxelNet on KITTI (#627, #654)

Improvements

• Add unit tests for pipeline functions LoadImageFromFileMono3D, ObjectNameFilter and ObjectRangeFilter (#615)
• Enhance IndoorPatchPointSample (#617)
• Refactor model initialization methods based MMCV (#622)
• Add Chinese docs (#629)
• Add documentation for LiDAR-based 3D detection (#642)
• Unify intrinsic and extrinsic matrices for all datasets (#653)
• Add documentation for point-based 3D semantic segmentation (#663)
• Add documentation of ScanNet for 3D detection (#664)
• Refine docs for tutorials (#666)
• Add documentation for vision-only 3D detection (#669)
• Refine docs for Quick Run and Useful Tools (#686)

Bug Fixes

• Fix the bug of BackgroundPointsFilter using the bottom center of ground truth (#609)
• Fix LoadMultiViewImageFromFiles to unravel stacked multi-view images to list to be consistent with DefaultFormatBundle (#611)
• Fix the potential bug in analyze_logs when the training resumes from a checkpoint or is stopped before evaluation (#634)
• Fix test commands in docs and make some refinements (#635)
• Fix wrong config paths in unit tests (#641)

v0.14.0 (1/6/2021)

Highlights

• Support the point cloud segmentation method PointNet++

New Features

• Support PointNet++ (#479, #528, #532, #541)
• Support RandomJitterPoints transform for point cloud segmentation (#584)
• Support RandomDropPointsColor transform for point cloud segmentation (#585)

Improvements

• Move the point alignment of ScanNet from data pre-processing to pipeline (#439, #470)
• Add compatibility document to provide detailed descriptions of BC-breaking changes (#504)
• Add MMSegmentation installation requirement (#535)
• Support points rotation even without bounding box in GlobalRotScaleTrans for point cloud segmentaiton (#540)
• Support visualization of detection results and dataset browse for nuScenes Mono-3D dataset (#542, #582)
• Support faster implementation of KNN (#586)
• Support RegNetX models on Lyft dataset (#589)
• Remove a useless parameter label_weight from segmentation datasets including Custom3DSegDataset, ScanNetSegDataset and S3DISSegDataset (#607)

Bug Fixes

• Fix a corrupted lidar data file in Lyft dataset in data_preparation (#546)
• Fix evaluation bugs in nuScenes and Lyft dataset (#549)
• Fix converting points between coordinates with specific transformation matrix in the coord_3d_mode.py (#556)
• Support PointPillars models on Lyft dataset (#578)
• Fix the bug of demo with pre-trained VoteNet model on ScanNet (#600)

v0.13.0 (1/5/2021)

Highlights

• Support a monocular 3D detection method FCOS3D
• Support ScanNet and S3DIS semantic segmentation dataset
• Enhancement of visualization tools for dataset browsing and demos, including support of visualization for multi-modality data and point cloud segmentation.

New Features

• Support ScanNet semantic segmentation dataset (#390)
• Support monocular 3D detection on nuScenes (#392)
• Support multi-modality visualization (#405)
• Support nuimages visualization (#408)
• Support monocular 3D detection on KITTI (#415)
• Support online visualization of semantic segmentation results (#416)
• Support ScanNet test results submission to online benchmark (#418)
• Support S3DIS data pre-processing and dataset class (#433)
• Support FCOS3D (#436, #442, #482, #484)
• Support dataset browse for multiple types of datasets (#467)
• Adding paper-with-code (PWC) metafile for each model in the model zoo (#485)

Improvements

• Support dataset browsing for SUNRGBD, ScanNet or KITTI points and detection results (#367)
• Add the pipeline to load data using file client (#430)
• Support to customize the type of runner (#437)
• Make pipeline functions process points and masks simultaneously when sampling points (#444)
• Add waymo unit tests (#455)
• Split the visualization of projecting points onto image from that for only points (#480)
• Efficient implementation of PointSegClassMapping (#489)
• Use the new model registry from mmcv (#495)

Bug Fixes

• Fix Pytorch 1.8 Compilation issue in the scatter_points_cuda.cu (#404)
• Fix dynamic_scatter errors triggered by empty point input (#417)
• Fix the bug of missing points caused by using break incorrectly in the voxelization (#423)
• Fix the missing coord_type in the waymo dataset config (#441)
• Fix errors in four unittest functions of configs, test_detectors.py, test_heads.py (#453)
• Fix 3DSSD training errors and simplify configs (#462)
• Clamp 3D votes projections to image boundaries in ImVoteNet (#463)
• Update out-of-date names of pipelines in the config of pointpillars benchmark (#474)
• Fix the lack of a placeholder when unpacking RPN targets in the h3d_bbox_head.py (#508)
• Fix the incorrect value of K when creating pickle files for SUN RGB-D (#511)

v0.12.0 (1/4/2021)

Highlights

• Support a new multi-modality method ImVoteNet.
• Support PyTorch 1.7 and 1.8
• Refactor the structure of tools and train.py/test.py

New Features

• Support LiDAR-based semantic segmentation metrics (#332)
• Support ImVoteNet (#352, #384)
• Support the KNN GPU operation (#360, #371)

Improvements

• Add FAQ for common problems in the documentation (#333)
• Refactor the structure of tools (#339)
• Refactor train.py and test.py (#343)
• Support demo on nuScenes (#353)
• Add 3DSSD checkpoints (#359)
• Update the Bibtex of CenterPoint (#368)
• Add citation format and reference to other OpenMMLab projects in the README (#374)
• Upgrade the mmcv version requirements (#376)
• Add numba and numpy version requirements in FAQ (#379)
• Avoid unnecessary for-loop execution of vfe layer creation (#389)
• Update SUNRGBD dataset documentation to stress the requirements for training ImVoteNet (#391)
• Modify vote head to support 3DSSD (#396)

Bug Fixes

• Fix missing keys coord_type in database sampler config (#345)
• Rename H3DNet configs (#349)
• Fix CI by using ubuntu 18.04 in github workflow (#350)
• Add assertions to avoid 4-dim points being input to points_in_boxes (#357)
• Fix the SECOND results on Waymo in the corresponding README (#363)
• Fix the incorrect adopted pipeline when adding val to workflow (#370)
• Fix a potential bug when indices used in the backwarding in ThreeNN (#377)
• Fix a compilation error triggered by scatter_points_cuda.cu in PyTorch 1.7 (#393)

v0.11.0 (1/3/2021)

Highlights

• Support more friendly visualization interfaces based on open3d
• Support a faster and more memory-efficient implementation of DynamicScatter
• Refactor unit tests and details of configs

New Features

• Support new visualization methods based on open3d (#284, #323)

Improvements

• Refactor unit tests (#303)
• Move the key train_cfg and test_cfg into the model configs (#307)
• Update README with Chinese version and instructions for getting started. (#310, #316)
• Support a faster and more memory-efficient implementation of DynamicScatter (#318, #326)

Bug Fixes

• Fix an unsupported bias setting in the unit test for centerpoint head (#304)
• Fix errors due to typos in the centerpoint head (#308)
• Fix a minor bug in points_in_boxes.py when tensors are not in the same device. (#317)
• Fix warning of deprecated usages of nonzero during training with PyTorch 1.6 (#330)

v0.10.0 (1/2/2021)

Highlights

• Preliminary release of API for SemanticKITTI dataset.
• Documentation and demo enhancement for better user experience.
• Fix a number of underlying minor bugs and add some corresponding important unit tests.

New Features

• Support SemanticKITTI dataset preliminarily (#287)

Improvements

• Add tag to README in configurations for specifying different uses (#262)
• Update instructions for evaluation metrics in the documentation (#265)
• Add nuImages entry in README.md and gif demo (#266, #268)
• Add unit test for voxelization (#275)

Bug Fixes

• Fixed the issue of unpacking size in furthest_point_sample.py (#248)
• Fix bugs for 3DSSD triggered by empty ground truths (#258)
• Remove models without checkpoints in model zoo statistics of documentation (#259)
• Fix some unclear installation instructions in getting_started.md (#269)
• Fix relative paths/links in the documentation (#271)
• Fix a minor bug in scatter_points_cuda.cu when num_features != 4 (#275)
• Fix the bug about missing text files when testing on KITTI (#278)
• Fix issues caused by inplace modification of tensors in BaseInstance3DBoxes (#283)
• Fix log analysis for evaluation and adjust the documentation accordingly (#285)

v0.9.0 (31/12/2020)

Highlights

• Documentation refactoring with better structure, especially about how to implement new models and customized datasets.
• More compatible with refactored point structure by bug fixes in ground truth sampling.

Improvements

• Documentation refactoring (#242)

Bug Fixes

• Fix point structure related bugs in ground truth sampling (#211)
• Fix loading points in ground truth sampling augmentation on nuScenes (#221)
• Fix channel setting in the SeparateHead of CenterPoint (#228)
• Fix evaluation for indoors 3D detection in case of less classes in prediction (#231)
• Remove unreachable lines in nuScenes data converter (#235)
• Minor adjustments of numpy implementation for perspective projection and prediction filtering criterion in KITTI evaluation (#241)

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. (#205)
• Add model zoo statistics and polish the documentation. (#201)

v0.7.0 (1/11/2020)

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.

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)

Bug Fixes

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

v0.6.1 (11/10/2020)

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

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)

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)

v0.6.0 (20/9/2020)

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.

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)

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)

v0.5.0 (9/7/2020)

MMDetection3D is released.