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arXiv · 2603.22852

Gau-Occ: Geometry-Completed Gaussians for Multi-Modal 3D Occupancy Prediction

Chengxin Lv·Yihui Li·Hongyu Yang·YunHong Wang
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Abstract

3D semantic occupancy prediction is crucial for autonomous driving. While multi-modal fusion improves accuracy over vision-only methods, it typically relies on computationally expensive dense voxel or BEV tensors. We present Gau-Occ, a multi-modal framework that bypasses dense volumetric processing by modeling the scene as a compact collection of semantic 3D Gaussians. To ensure geometric completeness, we propose a LiDAR Completion Diffuser (LCD) that recovers missing structures from sparse LiDAR to initialize robust Gaussian anchors. Furthermore, we introduce Gaussian Anchor Fusion (GAF), which efficiently integrates multi-view image semantics via geometry-aligned 2D sampling and cross-modal alignment. By refining these compact Gaussian descriptors, Gau-Occ captures both spatial consistency and semantic discriminability. Extensive experiments across challenging benchmarks demonstrate that Gau-Occ achieves state-of-the-art performance with significant computational efficiency.

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Chengxin Lv, Yihui Li, Hongyu Yang, YunHong Wang. 2026-03-24. Gau-Occ: Geometry-Completed Gaussians for Multi-Modal 3D Occupancy Prediction. https://arxiv.org/abs/2603.22852

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