Awesome
DAOcc
DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction [
arxiv
] <br> Zhen Yang, Heng Wang, Yanpeng Dong <br> Beijing Mechanical Equipment Institute, Beijing, China
This is the official implementation of DAOcc. DAOcc is a novel multi-modal occupancy prediction framework that leverages 3D object detection to assist in achieving superior performance while using a deployment-friendly image encoder and practical input image resolution.
News
- 2024-10-01: Our preprint is available on arXiv.
Experimental results
3D Semantic Occupancy Prediction on Occ3D-nuScenes
Method | Camera <br/> Mask | Image <br/> Backbone | Image <br/> Resolution | mIoU | Config | Model | Log |
---|---|---|---|---|---|---|---|
DAOcc | √ | R50 | 256×704 | 53.82 | config | model | log |
Method | Camera <br/> Mask | Image <br/> Backbone | Image <br/> Resolution | RayIoU | Config | Model | Log |
---|---|---|---|---|---|---|---|
DAOcc | × | R50 | 256×704 | 48.2 | config | model | log |
3D Semantic Occupancy Prediction on SurroundOcc
Method | Image <br/> Backbone | Image <br/> Resolution | IoU | mIoU | Config | Model | Log |
---|---|---|---|---|---|---|---|
DAOcc | R50 | 256×704 | 45.0 | 30.5 | config | model | log |
Getting Started
Citation
@article{yang2024daocc,
title={DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction},
author={Yang, Zhen and Dong, Yanpeng and Wang, Heng},
journal={arXiv preprint arXiv:2409.19972},
year={2024}
}
Acknowledgements
Many thanks to these excellent open-source projects: