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MonoCon/mmdetection3d-0.14.0/docs/changelog.md

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Changelog

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

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

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.