This branch contains the code for our initial MS3D framework for vehicle-only auto-labeling as presented in MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection
Please refer to INSTALL.md for the installation of MS3D.
- Please refer to DATASET_PREPARATION.md to prepare the datasets.
- Please refer to GETTING_STARTED.md to learn more about how to use MS3D.
- Please refer to PARAMETERS.md on a guide of how to tune MS3D parameters.
- Please refer to VISUALIZATION.md to learn how to use our visualization tools.
For all tables below, "GT-FT" refers to fine-tuning the pre-trained detector using ground-truth labels from the target domain. Results are reported at IoU=0.7 evaluated at 40 recall levels (R40). Refer to our paper for detailed results.
Models for target-nuscenes can be downloaded here. We also provide MS3D results for fine-tuning with multi-frame detection as is common on nuScenes models to demonstrate that we can further boost performance. All models below use SECOND-IoU.
| Method | Source | Vehicle (BEV) | Vehicle (3D) |
|---|---|---|---|
| MS3D | Waymo | 42.23 | 24.76 |
| MS3D | Lyft | 41.64 | 23.46 |
| MS3D (10 frame) | Waymo | 47.35 | 27.18 |
| GT-FT | Waymo | 44.39 | 29.46 |
| GT-FT (10 frame) | Waymo | 50.05 | 33.32 |
Models for target-lyft can be downloaded here. Similarly to nuScenes we show multi-frame detection results for MS3D. All models below use SECOND-IoU.
| Method | Source | Vehicle (BEV) | Vehicle (3D) |
|---|---|---|---|
| SN | nuScenes | 63.11 | 39.60 |
| SN | Waymo | 71.61 | 56.13 |
| ST3D | nuScenes | 67.33 | 41.82 |
| ST3D | Waymo | 73.86 | 56.33 |
| MS3D | nuScenes | 75.02 | 59.01 |
| MS3D | Waymo | 77.05 | 60.17 |
| MS3D (3 frame) | Waymo | 76.89 | 63.12 |
| GT-FT | Waymo | 81.10 | 66.76 |
| GT-FT (3 frame) | Waymo | 83.58 | 69.44 |
Due to the Waymo Dataset License Agreement we do not provide links to models that are trained on waymo data. You can train your own model using our provided configs.
If you want to download the models, please send me an email with your name, institute, a screenshot of the Waymo dataset registration confirmation mail and your intended usage. Please note that Waymo open dataset is under strict non-commercial license, so we are not allowed to share the model with you if it will use for any profit-oriented activities.
All models below use SECOND-IoU.
| Method | Source | Vehicle (BEV) | Vehicle (3D) |
|---|---|---|---|
| SN | Lyft | 53.39 | 39.22 |
| SN | nuScenes | 50.69 | 28.86 |
| ST3D | Lyft | 56.06 | 39.17 |
| ST3D | nuScenes | 55.67 | 28.83 |
| MS3D | Lyft | 61.25 | 42.88 |
| MS3D | nuScenes | 61.39 | 42.76 |
| GT-FT | Lyft | 66.76 | 52.50 |
We provide models trained on source-domain data used in our experiments.
nuScenes pre-trained models can be downloaded here
Lyft pre-trained models can be downloaded here
For Waymo, please send me an email if you would like to download the source-trained models we used.
MS3D is released under the Apache 2.0 license.
If you find this project useful in your research, please give us a star and consider citing:
@article{tsai2023ms3d,
title={MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection},
author={Tsai, Darren and Berrio, Julie Stephany and Shan, Mao and Nebot, Eduardo and Worrall, Stewart},
journal={arXiv preprint arXiv:2304.02431},
year={2023}
}
