Training deep learning models for semantic occupancy prediction is challenging due to factors such as a large number of occupancy cells, severe occlusion, limited visual cues, complicated driving scenarios, etc. Recent methods often adopt transformer-based architectures given their strong capability in learning input-conditioned weights and long-range relationships. However, transformer-based networks are notorious for their quadratic computation complexity, seriously undermining their efficacy and deployment in semantic occupancy prediction. Inspired by the global modeling and linear computation complexity of the Mamba architecture, we present the first Mamba-based network for semantic occupancy prediction, termed OccMamba. However, directly applying the Mamba architecture to the occupancy prediction task yields unsatisfactory performance due to the inherent domain gap between the linguistic and 3D domains. To relieve this problem, we present a simple yet effective 3D-to-1D reordering operation, i.e., height-prioritized 2D Hilbert expansion. It can maximally retain the spatial structure of point clouds as well as facilitate the processing of Mamba blocks. Our OccMamba achieves state-of-the-art performance on three prevalent occupancy prediction benchmarks, including OpenOccupancy, SemanticKITTI and SemanticPOSS. Notably, on OpenOccupancy, our OccMamba outperforms the previous state-of-the-art Co-Occ by 3.1% IoU and 3.2% mIoU, respectively. Codes will be released upon publication.

本文解决了语义占用预测中深度学习模型训练面临的挑战，如占用单元数量庞大和视觉线索有限等问题。提出的OccMamba模型灵感来源于Mamba架构，通过3D到1D的重排操作显著提升了占用预测的性能，并在多个基准测试中取得了最先进的结果，尤其在OpenOccupancy数据集上超越了之前的最佳模型。

OccMamba：基于状态空间模型的语义占用预测