`OpenPCDet` is a clear, simple, self-contained open source project for LiDAR-based 3D object detection.
It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/1812.04244), [`[Part-A2-Net]`](https://arxiv.org/abs/1907.03670), [`[PV-RCNN]`](https://arxiv.org/abs/1912.13192), [`[Voxel R-CNN]`](https://arxiv.org/abs/2012.15712) and[`[PV-RCNN++]`](https://arxiv.org/abs/2102.00463).
It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/1812.04244), [`[Part-A2-Net]`](https://arxiv.org/abs/1907.03670), [`[PV-RCNN]`](https://arxiv.org/abs/1912.13192), [`[Voxel R-CNN]`](https://arxiv.org/abs/2012.15712),[`[PV-RCNN++]`](https://arxiv.org/abs/2102.00463) and [`[MPPNet]`](https://arxiv.org/abs/2205.05979).
**Highlights**:
*`OpenPCDet` has been updated to `v0.5.2` (Jan. 2022).
*`OpenPCDet` has been updated to `v0.6.0` (Sep. 2022).
* The codes of PV-RCNN++ has been supported.
## Overview
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## Changelog
[2022-09-02] **NEW:** Update `OpenPCDet` to v0.6.0:
* Official code release of [MPPNet](https://arxiv.org/abs/2205.05979) for temporal 3D object detection, which supports long-term multi-frame 3D object detection and ranks 1st place on 3D detection learderboard of Waymo Open Dataset (see the [guideline](docs/guidelines_of_approaches/mppnet.md) on how to train/test with MPPNet).
* Support multi-frame training/testing on Waymo Open Dataset (see the [change log](docs/changelog.md) for more details on how to process data).
* Support to save changing training details (e.g., loss, iter, epoch) to file (previous tqdm progress bar is still supported by using `--use_tqdm_to_record`). Please use `pip install gpustat` if you also want to log the GPU related information.
* Support to save latest model every 5 mintues, so you can restore the model training from latest status instead of previous epoch.
[2022-08-22] Added support for [custom dataset tutorial and template](docs/CUSTOM_DATASET_TUTORIAL.md)
[2022-07-05] Added support for the 3D object detection backbone network [`Focals Conv`](https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Focal_Sparse_Convolutional_Networks_for_3D_Object_Detection_CVPR_2022_paper.pdf).
* Generate multi-frame GT database for copy-paste augmentation of multi-frame training. There is also a faster version with parallel data generation by adding `--use_parallel`, but you need to read the codes and rename the file after getting the results.
self.dataset_cfg.SEQUENCE_CONFIG.SAMPLE_OFFSET[0]=min(-4,st_frame)# at least we use 5 frames for generating gt database to support various sequence configs (<= 5 frames)
self.dataset_cfg.SEQUENCE_CONFIG.SAMPLE_OFFSET[0]=min(-4,st_frame)# at least we use 5 frames for generating gt database to support various sequence configs (<= 5 frames)
