Official Repo of the Project - RM3D: Robust Data-Efficient 3D Scene Parsing via Traditional and Learnt 3D Descriptors-based Semantic Region Merging
Our codebase of 3D object detection is based on 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], [Part-A^2 net], [PV-RCNN] and [Voxel R-CNN].
- Data-Model separation with unified point cloud coordinate for easily extending to custom datasets:
-
Unified 3D box definition: (x, y, z, dx, dy, dz, heading).
-
Flexible and clear model structure to easily support various 3D detection models:
- Support various models within one framework as:
- Support both one-stage and two-stage 3D object detection frameworks
- Support distributed training & testing with multiple GPUs and multiple machines
- Support multiple heads on different scales to detect different classes
- Support stacked version set abstraction to encode various number of points in different scenes
- Support Adaptive Training Sample Selection (ATSS) for target assignment
- Support RoI-aware point cloud pooling & RoI-grid point cloud pooling
- Support GPU version 3D IoU calculation and rotated NMS
Selected supported methods are shown in the below table. Here we provide the pretrained models which achieves State the 3D detection performance on the val set of KITTI dataset.
- All models are trained with 4 RTX 2080 Ti GPUs and are available for download.
- The training time is measured with 4 2080 Ti GPUs and PyTorch 1.5.
Data Efficient Learning with 3% labels
| training time | Car@R11 | Pedestrian@R11 | Cyclist@R11 | download | |
|---|---|---|---|---|---|
| PointPillar | ~2.66 hours | 65.43 | 45.08 | 51.88 | model_PointPillar |
| SECOND | ~2.75 hours | 69.56 | 43.29 | 56.66 | model_SECOND |
| SECOND-IoU | - | 68.28 | 45.39 | 57.29 | model_SECOND-IoU |
| PointRCNN | ~5.67 hours | 64.70 | 46.62 | 62.16 | model_PointRCNN |
| PointRCNN-IoU | ~6.12 hours | 67.54 | 47.19 | 60.25 | model_PointRCNN-IoU |
| Part-A^2-Free | ~5.98 hours | 65.92 | 57.83 | 63.18 | model_Part-A^2-Free |
| Part-A^2-Anchor | ~7.87 hours | 69.22 | 50.79 | 58.17 | model_Part-A^2-Anchor |
| PV-RCNN | ~8.78 hours | 74.24 | 47.65 | 60.23 | model_PV-RCNN |
| Voxel R-CNN (Car) | ~3.87 hours | 76.23 | - | - | model_Voxel_R-CNN |
| CaDDN | ~19.83 hours | 19.34 | 11.86 | 8.17 | model_CaDDN |
We provide the setting of DATA_CONFIG.SAMPLED_INTERVAL 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 3% data (~4.8k frames) of all the training samples on 4 2080 Ti 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).
| Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 | |
|---|---|---|---|---|---|---|
| SECOND | 57.15/66.38 | 49.42/48.67 | 50.75/40.39 | 42.18/36.65 | 47.65/43.98 | 41.22/40.92 |
| Part-A^2-Anchor | 59.82/58.29 | 53.33/52.82 | 52.15/44.76 | 45.12/40.29 | 56.67/55.21 | 50.29/50.88 |
| PV-RCNN | 59.06/55.38 | 55.67/63.38 | 54.23/43.76 | 44.89/38.28 | 53.15/50.94 | 49.87/48.69 |
We could not provide the above pretrained models due to Waymo Dataset License Agreement, you could easily achieve similar performance by training with the default configs.
More datasets are on the way.
Please refer to INSTALL.md for the installation of OpenPCDet.
Please refer to 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.
Please refer to GETTING_STARTED.md to learn more usage about this project.
OpenPCDet is released under the Apache 2.0 license.
For Questions regarding the 3D object detction code of our RM3D Please contact email ([email protected] or [email protected]).