MergeOcc: Bridge the Domain Gap between Different LiDARs for Robust Occupancy Prediction

• URL: http://arxiv.org/abs/2403.08512v2
• Date: Mon, 19 Aug 2024 02:46:26 GMT
• Title: MergeOcc: Bridge the Domain Gap between Different LiDARs for Robust Occupancy Prediction
• Authors: Zikun Xu, Jianqiang Wang, Shaobing Xu,
• Abstract summary: MergeOcc is developed to simultaneously handle different LiDARs by leveraging multiple datasets. The effectiveness of MergeOcc is validated through experiments on two prominent datasets for autonomous vehicles.
• Score: 8.993992124170624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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