CaDDN is a monocular-based 3D object detection method. This repository is based off of [OpenPCDet].
Categorical Depth Distribution Network for Monocular 3D Object Detection
Cody Reading, Ali Harakeh, Julia Chae, and Steven L. Waslander
[Paper]
[2021-03-16] CaDDN v0.3.0 is released.
CaDDN is a general PyTorch-based method for 3D object detection from monocular images.
At the time of submission, CaDDN achieved first 1st place among published monocular methods on the Kitti 3D object detection benchmark. We welcome contributions to this project.
We inherit the design pattern from [OpenPCDet].
- 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).
Selected supported methods are shown in the below table. The results are the 3D detection performance of Car class on the val set of KITTI dataset.
- All models are trained with 2 Tesla T4 GPUs and are available for download.
- The training time is measured with 2 Tesla T4 GPUs and PyTorch 1.4.
| training time | Easy@R40 | Moderate@R40 | Hard@R40 | download | |
|---|---|---|---|---|---|
| CaDDN | ~76 hours | 23.77 | 16.07 | 13.61 | model-774M |
Please refer to INSTALL.md for the installation of CaDDN.
Please refer to GETTING_STARTED.md to learn more usage about this project.
CaDDN is released under the Apache 2.0 license.
CaDDN is an open source project for monocular-based 3D scene perception.
We would like to thank the authors of OpenPCDet for their open-source release of their 3D object detection codebase.
If you find this project useful in your research, please consider citing:
@article{CaDDN,
title={Categorical Depth DistributionNetwork for Monocular 3D Object Detection},
author={Cody Reading and
Ali Harakeh and
Julia Chae and
Steven L. Waslander},
journal = {CVPR},
year={2021}
}
Welcome to be a member of the CaDDN development team by contributing to this repo, and feel free to contact us for any potential contributions.