DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction [arxiv]
Zhen Yang, Heng Wang, Yanpeng Dong
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.

• 2025-09-09: DAOcc is accepted to TCSVT — cue the confetti! 🎉
• 2025-07-20: We have open-sourced the TensorRT inference code for DAOcc, achieving 54.25 mIoU at 104.9 FPS. Check it out here.
• 2025-07-11: DAOcc achieved 54.33 mIoU on Occ3D-nuScenes without EMA.
• 2025-04-24: Following SparseBEV, we optimized the 2D-to-3D image feature transformation process, achieving substantial reductions in GPU memory consumption while slightly reducing training time. Check the config file.
• 2025-01-31: Release the model weights and the first version of the code.
• 2024-10-01: Our preprint is available on arXiv.

• The * means using exponential moving average (EMA) hook.
• For Occ3D-Waymo, we use only 20% of the training data.

• Installation
• Prepare Dataset
• Train and Eval
• Visualization

@article{yang2025daocc,
  title={Daocc: 3d object detection assisted multi-sensor fusion for 3d occupancy prediction},
  author={Yang, Zhen and Dong, Yanpeng and Wang, Jiayu and Wang, Heng and Ma, Lichao and Cui, Zijian and Liu, Qi and Pei, Haoran and Zhang, Kexin and Zhang, Chao},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2025},
  publisher={IEEE}
}

Many thanks to these excellent open-source projects:

• MMDetection3D
• BEVFusion
• FlashOcc