It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/1812.04244), [`[Part-A^2 net]`](https://arxiv.org/abs/1907.03670), [`[PV-RCNN]`](https://arxiv.org/abs/1912.13192) and [`[Voxel R-CNN]`](https://arxiv.org/abs/2012.15712).
It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/1812.04244), [`[Part-A^2 net]`](https://arxiv.org/abs/1907.03670), [`[PV-RCNN]`](https://arxiv.org/abs/1912.13192) and [`[Voxel R-CNN]`](https://arxiv.org/abs/2012.15712).
[2021-12-01] **NEW**: `OpenPCDet` has been updated to `v0.5.0`.
## Overview
## Overview
-[Changelog](#changelog)
-[Changelog](#changelog)
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@@ -18,15 +19,22 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18
## Changelog
## Changelog
[2021-12-01] **NEW:**`OpenPCDet` v0.5.0 is released with the following features:
* Improve the performance of all models on [Waymo Open Dataset](#waymo-open-dataset-baselines). Note that you need to re-prepare the training/validation data and ground-truth database of Waymo Open Dataset (see [GETTING_STARTED.md](docs/GETTING_STARTED.md)).
* Support anchor-free [CenterHead](pcdet/models/dense_heads/center_head.py), add configs of `CenterPoint` and `PV-RCNN with CenterHead`.
* Support lastest **PyTorch 1.1~1.10** and **spconv 1.0~2.x**, where **spconv 2.x** should be easy to install with pip and faster than previous version (see the official update of spconv [here](https://github.com/traveller59/spconv)).
* Support config [`USE_SHARED_MEMORY`](tools/cfgs/dataset_configs/waymo_dataset.yaml) to use shared memory to potentially speed up the training process in case you suffer from an IO problem.
* Support better and faster [visualization script](tools/visual_utils/open3d_vis_utils.py), and you need to install [Open3D](https://github.com/isl-org/Open3D) firstly.
[2021-06-08] Added support for the voxel-based 3D object detection model [`Voxel R-CNN`](#KITTI-3D-Object-Detection-Baselines)
[2021-06-08] Added support for the voxel-based 3D object detection model [`Voxel R-CNN`](#KITTI-3D-Object-Detection-Baselines)
[2021-05-14] Added support for the monocular 3D object detection model [`CaDDN`](#KITTI-3D-Object-Detection-Baselines)
[2021-05-14] Added support for the monocular 3D object detection model [`CaDDN`](#KITTI-3D-Object-Detection-Baselines)
[2020-11-27] **Bugfixed:** Please re-prepare the validation infos of Waymo dataset (version 1.2) if you would like to
[2020-11-27] Bugfixed: Please re-prepare the validation infos of Waymo dataset (version 1.2) if you would like to
use our provided Waymo evaluation tool (see [PR](https://github.com/open-mmlab/OpenPCDet/pull/383)).
use our provided Waymo evaluation tool (see [PR](https://github.com/open-mmlab/OpenPCDet/pull/383)).
Note that you do not need to re-prepare the training data and ground-truth database.
Note that you do not need to re-prepare the training data and ground-truth database.
[2020-11-10] **NEW:** The [Waymo Open Dataset](#waymo-open-dataset-baselines) has been supported with state-of-the-art results. Currently we provide the
[2020-11-10] The [Waymo Open Dataset](#waymo-open-dataset-baselines) has been supported with state-of-the-art results. Currently we provide the
configs and results of `SECOND`, `PartA2` and `PV-RCNN` on the Waymo Open Dataset, and more models could be easily supported by modifying their dataset configs.
configs and results of `SECOND`, `PartA2` and `PV-RCNN` on the Waymo Open Dataset, and more models could be easily supported by modifying their dataset configs.
[2020-08-10] Bugfixed: The provided NuScenes models have been updated to fix the loading bugs. Please redownload it if you need to use the pretrained NuScenes models.
[2020-08-10] Bugfixed: The provided NuScenes models have been updated to fix the loading bugs. Please redownload it if you need to use the pretrained NuScenes models.
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We provide the setting of [`DATA_CONFIG.SAMPLED_INTERVAL`](tools/cfgs/dataset_configs/waymo_dataset.yaml) on the Waymo Open Dataset (WOD) to subsample partial samples for training and evaluation,
We provide the setting of [`DATA_CONFIG.SAMPLED_INTERVAL`](tools/cfgs/dataset_configs/waymo_dataset.yaml) on the Waymo Open Dataset (WOD) to subsample partial samples for training and evaluation,
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By default, all models are trained with **20% data (~32k frames)** of all the training samples on 8 GTX 1080Ti GPUs, and the results of each cell here are mAP/mAPH calculated by the official Waymo evaluation metrics on the **whole** validation set (version 1.2).
By default, all models are trained with **20% data (~32k frames)** of all the training samples on 8 GTX 1080Ti GPUs, and the results of each cell here are mAP/mAPH calculated by the official Waymo evaluation metrics on the **whole** validation set (version 1.2).
