| <img src="docs/open_mmlab.png" align="right" width="30%"> | |
| # OpenPCDet | |
| `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), [`[PV-RCNN++]`](https://arxiv.org/abs/2102.00463) and [`[MPPNet]`](https://arxiv.org/abs/2205.05979). | |
| **Highlights**: | |
| * `OpenPCDet` has been updated to `v0.6.0` (Sep. 2022). | |
| * The codes of PV-RCNN++ has been supported. | |
| * The codes of MPPNet has been supported. | |
| * The multi-modal 3D detection approaches on Nuscenes have been supported. | |
| ## Overview | |
| - [Changelog](#changelog) | |
| - [Design Pattern](#openpcdet-design-pattern) | |
| - [Model Zoo](#model-zoo) | |
| - [Installation](docs/INSTALL.md) | |
| - [Quick Demo](docs/DEMO.md) | |
| - [Getting Started](docs/GETTING_STARTED.md) | |
| - [Citation](#citation) | |
| ## Changelog | |
| [2023-06-30] **NEW:** Added support for [`DSVT`](https://arxiv.org/abs/2301.06051), which achieves state-of-the-art performance on large-scale Waymo Open Dataset with real-time inference speed (27HZ with TensorRT). | |
| [2023-05-13] **NEW:** Added support for the multi-modal 3D object detection models on Nuscenes dataset. | |
| * Support multi-modal Nuscenes detection (See the [GETTING_STARTED.md](docs/GETTING_STARTED.md) to process data). | |
| * Support [TransFusion-Lidar](https://arxiv.org/abs/2203.11496) head, which ahcieves 69.43% NDS on Nuscenes validation dataset. | |
| * Support [`BEVFusion`](https://arxiv.org/abs/2205.13542), which fuses multi-modal information on BEV space and reaches 70.98% NDS on Nuscenes validation dataset. (see the [guideline](docs/guidelines_of_approaches/bevfusion.md) on how to train/test with BEVFusion). | |
| [2023-04-02] Added support for [`VoxelNeXt`](https://arxiv.org/abs/2303.11301) on Nuscenes, Waymo, and Argoverse2 datasets. It is a fully sparse 3D object detection network, which is a clean sparse CNNs network and predicts 3D objects directly upon voxels. | |
| [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](https://waymo.com/open/challenges/2020/3d-detection) of Waymo Open Dataset on Sept. 2th, 2022. For validation dataset, MPPNet achieves 74.96%, 75.06% and 74.52% for vehicle, pedestrian and cyclist classes in terms of mAPH@Level_2. (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). | |
| [2022-02-12] Added support for using docker. Please refer to the guidance in [./docker](./docker). | |
| [2022-02-07] Added support for Centerpoint models on Nuscenes Dataset. | |
| [2022-01-14] Added support for dynamic pillar voxelization, following the implementation proposed in [`H^23D R-CNN`](https://arxiv.org/abs/2107.14391) with unique operation and [`torch_scatter`](https://github.com/rusty1s/pytorch_scatter) package. | |
| [2022-01-05] **NEW:** Update `OpenPCDet` to v0.5.2: | |
| * The code of [`PV-RCNN++`](https://arxiv.org/abs/2102.00463) has been released to this repo, with higher performance, faster training/inference speed and less memory consumption than PV-RCNN. | |
| * Add performance of several models trained with full training set of [Waymo Open Dataset](#waymo-open-dataset-baselines). | |
| * Support Lyft dataset, see the pull request [here](https://github.com/open-mmlab/OpenPCDet/pull/720). | |
| [2021-12-09] **NEW:** Update `OpenPCDet` to v0.5.1: | |
| * Add PointPillar related baseline configs/results on [Waymo Open Dataset](#waymo-open-dataset-baselines). | |
| * Support Pandaset dataloader, see the pull request [here](https://github.com/open-mmlab/OpenPCDet/pull/396). | |
| * Support a set of new augmentations, see the pull request [here](https://github.com/open-mmlab/OpenPCDet/pull/653). | |
| [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). | |
| [2021-05-14] 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 | |
| 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. | |
| [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. | |
| [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. | |
| [2020-07-30] `OpenPCDet` v0.3.0 is released with the following features: | |
| * The Point-based and Anchor-Free models ([`PointRCNN`](#KITTI-3D-Object-Detection-Baselines), [`PartA2-Free`](#KITTI-3D-Object-Detection-Baselines)) are supported now. | |
| * The NuScenes dataset is supported with strong baseline results ([`SECOND-MultiHead (CBGS)`](#NuScenes-3D-Object-Detection-Baselines) and [`PointPillar-MultiHead`](#NuScenes-3D-Object-Detection-Baselines)). | |
| * High efficiency than last version, support **PyTorch 1.1~1.7** and **spconv 1.0~1.2** simultaneously. | |
| [2020-07-17] Add simple visualization codes and a quick demo to test with custom data. | |
| [2020-06-24] `OpenPCDet` v0.2.0 is released with pretty new structures to support more models and datasets. | |
| [2020-03-16] `OpenPCDet` v0.1.0 is released. | |
| ## Introduction | |
| ### What does `OpenPCDet` toolbox do? | |
| Note that we have upgrated `PCDet` from `v0.1` to `v0.2` with pretty new structures to support various datasets and models. | |
| `OpenPCDet` is a general PyTorch-based codebase for 3D object detection from point cloud. | |
| It currently supports multiple state-of-the-art 3D object detection methods with highly refactored codes for both one-stage and two-stage 3D detection frameworks. | |
| Based on `OpenPCDet` toolbox, we win the Waymo Open Dataset challenge in [3D Detection](https://waymo.com/open/challenges/3d-detection/), | |
| [3D Tracking](https://waymo.com/open/challenges/3d-tracking/), [Domain Adaptation](https://waymo.com/open/challenges/domain-adaptation/) | |
| three tracks among all LiDAR-only methods, and the Waymo related models will be released to `OpenPCDet` soon. | |
| We are actively updating this repo currently, and more datasets and models will be supported soon. | |
| Contributions are also welcomed. | |
| ### `OpenPCDet` design pattern | |
| * Data-Model separation with unified point cloud coordinate for easily extending to custom datasets: | |
| <p align="center"> | |
| <img src="docs/dataset_vs_model.png" width="95%" height="320"> | |
| </p> | |
| * Unified 3D box definition: (x, y, z, dx, dy, dz, heading). | |
| * Flexible and clear model structure to easily support various 3D detection models: | |
| <p align="center"> | |
| <img src="docs/model_framework.png" width="95%"> | |
| </p> | |
| * Support various models within one framework as: | |
| <p align="center"> | |
| <img src="docs/multiple_models_demo.png" width="95%"> | |
| </p> | |
| ### Currently Supported Features | |
| - [x] Support both one-stage and two-stage 3D object detection frameworks | |
| - [x] Support distributed training & testing with multiple GPUs and multiple machines | |
| - [x] Support multiple heads on different scales to detect different classes | |
| - [x] Support stacked version set abstraction to encode various number of points in different scenes | |
| - [x] Support Adaptive Training Sample Selection (ATSS) for target assignment | |
| - [x] Support RoI-aware point cloud pooling & RoI-grid point cloud pooling | |
| - [x] Support GPU version 3D IoU calculation and rotated NMS | |
| ## Model Zoo | |
| ### KITTI 3D Object Detection Baselines | |
| Selected supported methods are shown in the below table. The results are the 3D detection performance of moderate difficulty on the *val* set of KITTI dataset. | |
| * All LiDAR-based models are trained with 8 GTX 1080Ti GPUs and are available for download. | |
| * The training time is measured with 8 TITAN XP GPUs and PyTorch 1.5. | |
| | | training time | Car@R11 | Pedestrian@R11 | Cyclist@R11 | download | | |
| |---------------------------------------------|----------:|:-------:|:-------:|:-------:|:---------:| | |
| | [PointPillar](tools/cfgs/kitti_models/pointpillar.yaml) |~1.2 hours| 77.28 | 52.29 | 62.68 | [model-18M](https://drive.google.com/file/d/1wMxWTpU1qUoY3DsCH31WJmvJxcjFXKlm/view?usp=sharing) | | |
| | [SECOND](tools/cfgs/kitti_models/second.yaml) | ~1.7 hours | 78.62 | 52.98 | 67.15 | [model-20M](https://drive.google.com/file/d/1-01zsPOsqanZQqIIyy7FpNXStL3y4jdR/view?usp=sharing) | | |
| | [SECOND-IoU](tools/cfgs/kitti_models/second_iou.yaml) | - | 79.09 | 55.74 | 71.31 | [model-46M](https://drive.google.com/file/d/1AQkeNs4bxhvhDQ-5sEo_yvQUlfo73lsW/view?usp=sharing) | | |
| | [PointRCNN](tools/cfgs/kitti_models/pointrcnn.yaml) | ~3 hours | 78.70 | 54.41 | 72.11 | [model-16M](https://drive.google.com/file/d/1BCX9wMn-GYAfSOPpyxf6Iv6fc0qKLSiU/view?usp=sharing)| | |
| | [PointRCNN-IoU](tools/cfgs/kitti_models/pointrcnn_iou.yaml) | ~3 hours | 78.75 | 58.32 | 71.34 | [model-16M](https://drive.google.com/file/d/1V0vNZ3lAHpEEt0MlT80eL2f41K2tHm_D/view?usp=sharing)| | |
| | [Part-A2-Free](tools/cfgs/kitti_models/PartA2_free.yaml) | ~3.8 hours| 78.72 | 65.99 | 74.29 | [model-226M](https://drive.google.com/file/d/1lcUUxF8mJgZ_e-tZhP1XNQtTBuC-R0zr/view?usp=sharing) | | |
| | [Part-A2-Anchor](tools/cfgs/kitti_models/PartA2.yaml) | ~4.3 hours| 79.40 | 60.05 | 69.90 | [model-244M](https://drive.google.com/file/d/10GK1aCkLqxGNeX3lVu8cLZyE0G8002hY/view?usp=sharing) | | |
| | [PV-RCNN](tools/cfgs/kitti_models/pv_rcnn.yaml) | ~5 hours| 83.61 | 57.90 | 70.47 | [model-50M](https://drive.google.com/file/d/1lIOq4Hxr0W3qsX83ilQv0nk1Cls6KAr-/view?usp=sharing) | | |
| | [Voxel R-CNN (Car)](tools/cfgs/kitti_models/voxel_rcnn_car.yaml) | ~2.2 hours| 84.54 | - | - | [model-28M](https://drive.google.com/file/d/19_jiAeGLz7V0wNjSJw4cKmMjdm5EW5By/view?usp=sharing) | | |
| | [Focals Conv - F](tools/cfgs/kitti_models/voxel_rcnn_car_focal_multimodal.yaml) | ~4 hours| 85.66 | - | - | [model-30M](https://drive.google.com/file/d/1u2Vcg7gZPOI-EqrHy7_6fqaibvRt2IjQ/view?usp=sharing) | | |
| || | |
| | [CaDDN (Mono)](tools/cfgs/kitti_models/CaDDN.yaml) |~15 hours| 21.38 | 13.02 | 9.76 | [model-774M](https://drive.google.com/file/d/1OQTO2PtXT8GGr35W9m2GZGuqgb6fyU1V/view?usp=sharing) | | |
| ### Waymo Open Dataset Baselines | |
| 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, | |
| so you could also play with WOD by setting a smaller `DATA_CONFIG.SAMPLED_INTERVAL` even if you only have limited GPU resources. | |
| By default, all models are trained with **a single frame** of **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). | |
| | Performance@(train with 20\% Data) | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 | | |
| |---------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:| | |
| | [SECOND](tools/cfgs/waymo_models/second.yaml) | 70.96/70.34|62.58/62.02|65.23/54.24 |57.22/47.49| 57.13/55.62 | 54.97/53.53 | | |
| | [PointPillar](tools/cfgs/waymo_models/pointpillar_1x.yaml) | 70.43/69.83 | 62.18/61.64 | 66.21/46.32|58.18/40.64|55.26/51.75|53.18/49.80 | | |
| [CenterPoint-Pillar](tools/cfgs/waymo_models/centerpoint_pillar_1x.yaml)| 70.50/69.96|62.18/61.69|73.11/61.97|65.06/55.00|65.44/63.85|62.98/61.46| | |
| [CenterPoint-Dynamic-Pillar](tools/cfgs/waymo_models/centerpoint_dyn_pillar_1x.yaml)| 70.46/69.93|62.06/61.58|73.92/63.35|65.91/56.33|66.24/64.69|63.73/62.24| | |
| [CenterPoint](tools/cfgs/waymo_models/centerpoint_without_resnet.yaml)| 71.33/70.76|63.16/62.65| 72.09/65.49 |64.27/58.23| 68.68/67.39 |66.11/64.87| | |
| | [CenterPoint (ResNet)](tools/cfgs/waymo_models/centerpoint.yaml)|72.76/72.23|64.91/64.42 |74.19/67.96 |66.03/60.34| 71.04/69.79 |68.49/67.28 | | |
| | [Part-A2-Anchor](tools/cfgs/waymo_models/PartA2.yaml) | 74.66/74.12 |65.82/65.32 |71.71/62.24 |62.46/54.06 |66.53/65.18 |64.05/62.75 | | |
| | [PV-RCNN (AnchorHead)](tools/cfgs/waymo_models/pv_rcnn.yaml) | 75.41/74.74 |67.44/66.80 |71.98/61.24 |63.70/53.95 |65.88/64.25 |63.39/61.82 | | |
| | [PV-RCNN (CenterHead)](tools/cfgs/waymo_models/pv_rcnn_with_centerhead_rpn.yaml) | 75.95/75.43 |68.02/67.54 |75.94/69.40 |67.66/61.62 |70.18/68.98 |67.73/66.57| | |
| | [Voxel R-CNN (CenterHead)-Dynamic-Voxel](tools/cfgs/waymo_models/voxel_rcnn_with_centerhead_dyn_voxel.yaml) | 76.13/75.66 |68.18/67.74 |78.20/71.98 |69.29/63.59 | 70.75/69.68 |68.25/67.21| | |
| | [PV-RCNN++](tools/cfgs/waymo_models/pv_rcnn_plusplus.yaml) | 77.82/77.32| 69.07/68.62| 77.99/71.36| 69.92/63.74| 71.80/70.71| 69.31/68.26| | |
| | [PV-RCNN++ (ResNet)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet.yaml) |77.61/77.14| 69.18/68.75| 79.42/73.31| 70.88/65.21| 72.50/71.39| 69.84/68.77| | |
| Here we also provide the performance of several models trained on the full training set (refer to the paper of [PV-RCNN++](https://arxiv.org/abs/2102.00463)): | |
| | Performance@(train with 100\% Data) | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 | | |
| |-------------------------------------------------------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:| | |
| | [SECOND](tools/cfgs/waymo_models/second.yaml) | 72.27/71.69 | 63.85/63.33 | 68.70/58.18 | 60.72/51.31 | 60.62/59.28 | 58.34/57.05 | | |
| | [CenterPoint-Pillar](tools/cfgs/waymo_models/centerpoint_pillar_1x.yaml) | 73.37/72.86 | 65.09/64.62 | 75.35/65.11 | 67.61/58.25 | 67.76/66.22 | 65.25/63.77 | | |
| | [Part-A2-Anchor](tools/cfgs/waymo_models/PartA2.yaml) | 77.05/76.51 | 68.47/67.97 | 75.24/66.87 | 66.18/58.62 | 68.60/67.36 | 66.13/64.93 | | |
| | [VoxelNeXt-2D](tools/cfgs/waymo_models/voxelnext2d_ioubranch.yaml) | 77.94/77.47 |69.68/69.25 |80.24/73.47 |72.23/65.88 |73.33/72.20 |70.66/69.56 | | |
| | [VoxelNeXt](tools/cfgs/waymo_models/voxelnext_ioubranch_large.yaml) | 78.16/77.70 |69.86/69.42 |81.47/76.30 |73.48/68.63 |76.06/74.90 |73.29/72.18 | | |
| | [PV-RCNN (CenterHead)](tools/cfgs/waymo_models/pv_rcnn_with_centerhead_rpn.yaml) | 78.00/77.50 | 69.43/68.98 | 79.21/73.03 | 70.42/64.72 | 71.46/70.27 | 68.95/67.79 | | |
| | [PV-RCNN++](tools/cfgs/waymo_models/pv_rcnn_plusplus.yaml) | 79.10/78.63 | 70.34/69.91 | 80.62/74.62 | 71.86/66.30 | 73.49/72.38 | 70.70/69.62 | | |
| | [PV-RCNN++ (ResNet)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet.yaml) | 79.25/78.78 | 70.61/70.18 | 81.83/76.28 | 73.17/68.00 | 73.72/72.66 | 71.21/70.19 | | |
| | [DSVT-Pillar](tools/cfgs/waymo_models/dsvt_pillar.yaml) | 79.44/78.97 | 71.24/70.81 | 83.00/77.22 | 75.45/69.95 | 76.70/75.70 | 73.83/72.86 | | |
| | [DSVT-Voxel](tools/cfgs/waymo_models/dsvt_voxel.yaml) | 79.77/79.31 | 71.67/71.25 | 83.75/78.92 | 76.21/71.57 | 77.57/76.58 | 74.70/73.73 | | |
| | [PV-RCNN++ (ResNet, 2 frames)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet_2frames.yaml) | 80.17/79.70 | 72.14/71.70 | 83.48/80.42 | 75.54/72.61 | 74.63/73.75 | 72.35/71.50 | | |
| | [MPPNet (4 frames)](docs/guidelines_of_approaches/mppnet.md) | 81.54/81.06 | 74.07/73.61 | 84.56/81.94 | 77.20/74.67 | 77.15/76.50 | 75.01/74.38 | | |
| | [MPPNet (16 frames)](docs/guidelines_of_approaches/mppnet.md) | 82.74/82.28 | 75.41/74.96 | 84.69/82.25 | 77.43/75.06 | 77.28/76.66 | 75.13/74.52 | | |
| We could not provide the above pretrained models due to [Waymo Dataset License Agreement](https://waymo.com/open/terms/), | |
| but you could easily achieve similar performance by training with the default configs. | |
| ### NuScenes 3D Object Detection Baselines | |
| All models are trained with 8 GPUs and are available for download. For training BEVFusion, please refer to the [guideline](docs/guidelines_of_approaches/bevfusion.md). | |
| | | mATE | mASE | mAOE | mAVE | mAAE | mAP | NDS | download | | |
| |----------------------------------------------------------------------------------------------------|-------:|:------:|:------:|:-----:|:-----:|:-----:|:------:|:--------------------------------------------------------------------------------------------------:| | |
| | [PointPillar-MultiHead](tools/cfgs/nuscenes_models/cbgs_pp_multihead.yaml) | 33.87 | 26.00 | 32.07 | 28.74 | 20.15 | 44.63 | 58.23 | [model-23M](https://drive.google.com/file/d/1p-501mTWsq0G9RzroTWSXreIMyTUUpBM/view?usp=sharing) | | |
| | [SECOND-MultiHead (CBGS)](tools/cfgs/nuscenes_models/cbgs_second_multihead.yaml) | 31.15 | 25.51 | 26.64 | 26.26 | 20.46 | 50.59 | 62.29 | [model-35M](https://drive.google.com/file/d/1bNzcOnE3u9iooBFMk2xK7HqhdeQ_nwTq/view?usp=sharing) | | |
| | [CenterPoint-PointPillar](tools/cfgs/nuscenes_models/cbgs_dyn_pp_centerpoint.yaml) | 31.13 | 26.04 | 42.92 | 23.90 | 19.14 | 50.03 | 60.70 | [model-23M](https://drive.google.com/file/d/1UvGm6mROMyJzeSRu7OD1leU_YWoAZG7v/view?usp=sharing) | | |
| | [CenterPoint (voxel_size=0.1)](tools/cfgs/nuscenes_models/cbgs_voxel01_res3d_centerpoint.yaml) | 30.11 | 25.55 | 38.28 | 21.94 | 18.87 | 56.03 | 64.54 | [model-34M](https://drive.google.com/file/d/1Cz-J1c3dw7JAWc25KRG1XQj8yCaOlexQ/view?usp=sharing) | | |
| | [CenterPoint (voxel_size=0.075)](tools/cfgs/nuscenes_models/cbgs_voxel0075_res3d_centerpoint.yaml) | 28.80 | 25.43 | 37.27 | 21.55 | 18.24 | 59.22 | 66.48 | [model-34M](https://drive.google.com/file/d/1XOHAWm1MPkCKr1gqmc3TWi5AYZgPsgxU/view?usp=sharing) | | |
| | [VoxelNeXt (voxel_size=0.075)](tools/cfgs/nuscenes_models/cbgs_voxel0075_voxelnext.yaml) | 30.11 | 25.23 | 40.57 | 21.69 | 18.56 | 60.53 | 66.65 | [model-31M](https://drive.google.com/file/d/1IV7e7G9X-61KXSjMGtQo579pzDNbhwvf/view?usp=share_link) | | |
| | [TransFusion-L*](tools/cfgs/nuscenes_models/transfusion_lidar.yaml) | 27.96 | 25.37 | 29.35 | 27.31 | 18.55 | 64.58 | 69.43 | [model-32M](https://drive.google.com/file/d/1cuZ2qdDnxSwTCsiXWwbqCGF-uoazTXbz/view?usp=share_link) | | |
| | [BEVFusion](tools/cfgs/nuscenes_models/bevfusion.yaml) | 28.03 | 25.43 | 30.19 | 26.76 | 18.48 | 67.75 | 70.98 | [model-157M](https://drive.google.com/file/d/1X50b-8immqlqD8VPAUkSKI0Ls-4k37g9/view?usp=share_link) | | |
| *: Use the fade strategy, which disables data augmentations in the last several epochs during training. | |
| ### ONCE 3D Object Detection Baselines | |
| All models are trained with 8 GPUs. | |
| | | Vehicle | Pedestrian | Cyclist | mAP | | |
| | ------------------------------------------------------ | :-----: | :--------: | :-----: | :----: | | |
| | [PointRCNN](tools/cfgs/once_models/pointrcnn.yaml) | 52.09 | 4.28 | 29.84 | 28.74 | | |
| | [PointPillar](tools/cfgs/once_models/pointpillar.yaml) | 68.57 | 17.63 | 46.81 | 44.34 | | |
| | [SECOND](tools/cfgs/once_models/second.yaml) | 71.19 | 26.44 | 58.04 | 51.89 | | |
| | [PV-RCNN](tools/cfgs/once_models/pv_rcnn.yaml) | 77.77 | 23.50 | 59.37 | 53.55 | | |
| | [CenterPoint](tools/cfgs/once_models/centerpoint.yaml) | 78.02 | 49.74 | 67.22 | 64.99 | | |
| ### Argoverse2 3D Object Detection Baselines | |
| All models are trained with 4 GPUs. | |
| | | mAP | download | | |
| |---------------------------------------------------------|:----:|:--------------------------------------------------------------------------------------------------:| | |
| | [VoxelNeXt](tools/cfgs/argo2_models/cbgs_voxel01_voxelnext.yaml) | 30.5 | [model-32M](https://drive.google.com/file/d/1YP2UOz-yO-cWfYQkIqILEu6bodvCBVrR/view?usp=share_link) | | |
| ### Other datasets | |
| Welcome to support other datasets by submitting pull request. | |
| ## Installation | |
| Please refer to [INSTALL.md](docs/INSTALL.md) for the installation of `OpenPCDet`. | |
| ## Quick Demo | |
| Please refer to [DEMO.md](docs/DEMO.md) for a quick demo to test with a pretrained model and | |
| visualize the predicted results on your custom data or the original KITTI data. | |
| ## Getting Started | |
| Please refer to [GETTING_STARTED.md](docs/GETTING_STARTED.md) to learn more usage about this project. | |
| ## License | |
| `OpenPCDet` is released under the [Apache 2.0 license](LICENSE). | |
| ## Acknowledgement | |
| `OpenPCDet` is an open source project for LiDAR-based 3D scene perception that supports multiple | |
| LiDAR-based perception models as shown above. Some parts of `PCDet` are learned from the official released codes of the above supported methods. | |
| We would like to thank for their proposed methods and the official implementation. | |
| We hope that this repo could serve as a strong and flexible codebase to benefit the research community by speeding up the process of reimplementing previous works and/or developing new methods. | |
| ## Citation | |
| If you find this project useful in your research, please consider cite: | |
| ``` | |
| @misc{openpcdet2020, | |
| title={OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds}, | |
| author={OpenPCDet Development Team}, | |
| howpublished = {\url{https://github.com/open-mmlab/OpenPCDet}}, | |
| year={2020} | |
| } | |
| ``` | |
| ## Contribution | |
| Welcome to be a member of the OpenPCDet development team by contributing to this repo, and feel free to contact us for any potential contributions. | |