Bump to v0.14.0 (#606)

* Bump to v0.14.0

* remove the parameter label_weight

README.md

@@ -8,7 +8,7 @@
 [![license](https://img.shields.io/github/license/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/blob/master/LICENSE)
 
 
-**News**: We released the codebase v0.13.0.
+**News**: We released the codebase v0.14.0.
 
 In the recent [nuScenes 3D detection challenge](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any) of the 5th AI Driving Olympics in NeurIPS 2020, we obtained the best PKL award and the second runner-up by multi-modality entry, and the best vision-only results.
 
@@ -62,7 +62,7 @@ This project is released under the [Apache 2.0 license](LICENSE).
 
 ## Changelog
 
-v0.13.0 was released in 1/5/2021.
+v0.14.0 was released in 1/6/2021.
 Please refer to [changelog.md](docs/changelog.md) for details and release history.
 
 ## Benchmark and model zoo

README_zh-CN.md

@@ -8,7 +8,7 @@
 [![license](https://img.shields.io/github/license/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/blob/master/LICENSE)
 
 
-**新闻**: 我们发布了版本v0.13.0.
+**新闻**: 我们发布了版本v0.14.0.
 
 在第三届[ nuScenes 3D 检测挑战赛](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any)（第五届 AI Driving Olympics, NeurIPS 2020）中，我们获得了最佳 PKL 奖、第三名和最好的纯视觉的结果，相关的代码和模型将会在不久后发布。
 
@@ -62,7 +62,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 
 ## 更新日志
 
-最新的版本 v0.13.0 在 2021.05.01发布。
+最新的版本 v0.14.0 在 2021.06.01发布。
 如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/changelog.md)。
 
 ## 基准测试和模型库

docs/changelog.md

@@ -1,5 +1,35 @@
 ## Changelog
 
+### v0.14.0 (1/6/2021)
+
+#### Highlights
+- Support the point cloud segmentation method [PointNet++](https://arxiv.org/abs/1706.02413)
+
+#### 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](https://github.com/open-mmlab/mmdetection3d/tree/master/docs/data_preparation.md) (#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](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/bbox/structures/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

docs/compatibility.md

@@ -2,14 +2,12 @@
 
 This document provides detailed descriptions of the BC-breaking changes in MMDetection3D.
 
-## MMDetection3D 0.15.0
+## MMDetection3D 0.14.0
 
 ### Dataset class for 3D segmentation task
 
 We remove a useless parameter `label_weight` from segmentation datasets including `Custom3DSegDataset`, `ScanNetSegDataset` and `S3DISSegDataset` since this weight is utilized in the loss function of model class. Please modify the code as well as the config files accordingly if you use or inherit from these codes.
 
-## MMDetection3D 0.14.0
-
 ### ScanNet data pre-processing
 
 We adopt new pre-processing and conversion steps of ScanNet dataset. In previous versions of MMDetection3D, ScanNet dataset was only used for 3D detection task, where we trained on the training set and tested on the validation set. In MMDetection3D 0.14.0, we further support 3D segmentation task on ScanNet, which includes online benchmarking on test set. Since the alignment matrix is not provided for test set data, we abandon the alignment of points in data generation steps to support both tasks. Besides, as 3D segmentation requires per-point prediction, we also remove the down-sampling step in data generation.

docs/getting_started.md

@@ -13,6 +13,7 @@ The required versions of MMCV, MMDetection and MMSegmentation for different vers
 | MMDetection3D version | MMDetection version | MMSegmentation version | MMCV version |
 |:-------------------:|:-------------------:|:-------------------:|:-------------------:|
 | master | mmdet>=2.10.0, <=2.11.0| mmseg>=0.13.0 | mmcv-full>=1.3.1, <=1.4|
+| 0.14.0 | mmdet>=2.10.0, <=2.11.0| mmseg>=0.13.0 | mmcv-full>=1.3.1, <=1.4|
 | 0.13.0 | mmdet>=2.10.0, <=2.11.0| Not required | mmcv-full>=1.2.4, <=1.4|
 | 0.12.0 | mmdet>=2.5.0, <=2.11.0 | Not required | mmcv-full>=1.2.4, <=1.4|
 | 0.11.0 | mmdet>=2.5.0, <=2.11.0 | Not required | mmcv-full>=1.2.4, <=1.4|
@@ -95,7 +96,7 @@ pip install mmcv-full
 
 Note:
 
-MMDetection3D v0.13.0 is only compatible with MMDetection version `mmdet>=2.10.0, <=2.11.0`. The future versions will only support `mmdet>=2.12.0` since v0.14.0 (to be released in May).
+MMDetection3D v0.14.0 is only compatible with MMDetection version `mmdet>=2.10.0, <=2.11.0`. The future versions will only support `mmdet>=2.12.0` since v0.15.0 (to be released in July).
 
 ```shell
 pip install git+https://github.com/open-mmlab/mmdetection.git

mmdet3d/version.py

@@ -1,6 +1,6 @@
 # Copyright (c) Open-MMLab. All rights reserved.
 
-__version__ = '0.13.0'
+__version__ = '0.14.0'
 short_version = __version__