MergeOcc: Bridge the Domain Gap between Different LiDARs for Robust Occupancy Prediction
<|reference_start|>MergeOcc: Bridge the Domain Gap between Different LiDARs for Robust Occupancy Prediction: LiDAR-based 3D occupancy prediction evolved rapidly alongside the emergence of large datasets. Nevertheless, the potential of existing diverse datasets remains underutilized as they kick in individually. Models trained on a specific dataset often suffer considerable performance degradation when deployed to real-world scenarios or datasets involving disparate LiDARs. This paper aims to develop a generalized model called MergeOcc, to simultaneously handle different LiDARs by leveraging multiple datasets. The gaps among LiDAR datasets primarily manifest in geometric disparities and semantic inconsistencies. Thus, MergeOcc incorporates a novel model featuring a geometric realignment module and a semantic label mapping module to enable multiple datasets training (MDT). The effectiveness of MergeOcc is validated through experiments on two prominent datasets for autonomous vehicles: OpenOccupancy-nuScenes and SemanticKITTI. The results demonstrate its enhanced robustness and remarkable performance across both types of LiDARs, outperforming several SOTA multi-modality methods. Notably, despite using an identical model architecture and hyper-parameter set, MergeOcc can significantly surpass the baseline due to its exposure to more diverse data. MergeOcc is considered the first cross-dataset 3D occupancy prediction pipeline that effectively bridges the domain gap for seamless deployment across heterogeneous platforms.<|reference_end|>
@article{xu2024mergeocc:,
    title={MergeOcc: Bridge the Domain Gap between Different LiDARs for Robust
  Occupancy Prediction},
    author={Zikun Xu, Jianqiang Wang, Shaobing Xu},
    journal={arXiv preprint arXiv:2403.08512},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08512},
    primaryClass={cs.CV}
}