OccFusion: Depth Estimation Free Multi-sensor Fusion for 3D Occupancy Prediction
<|reference_start|>OccFusion: Depth Estimation Free Multi-sensor Fusion for 3D Occupancy Prediction: 3D occupancy prediction based on multi-sensor fusion,crucial for a reliable autonomous driving system, enables fine-grained understanding of 3D scenes. Previous fusion-based 3D occupancy predictions relied on depth estimation for processing 2D image features. However, depth estimation is an ill-posed problem, hindering the accuracy and robustness of these methods. Furthermore, fine-grained occupancy prediction demands extensive computational resources. To address these issues, we propose OccFusion, a depth estimation free multi-modal fusion framework. Additionally, we introduce a generalizable active training method and an active decoder that can be applied to any occupancy prediction model, with the potential to enhance their performance. Experiments conducted on nuScenes-Occupancy and nuScenes-Occ3D demonstrate our framework's superior performance. Detailed ablation studies highlight the effectiveness of each proposed method.<|reference_end|>
@article{zhang2024occfusion:,
    title={OccFusion: Depth Estimation Free Multi-sensor Fusion for 3D Occupancy
  Prediction},
    author={Ji Zhang, Yiran Ding and Zixin Liu},
    journal={arXiv preprint arXiv:2403.05329},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05329},
    primaryClass={cs.CV}
}