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

• URL: http://arxiv.org/abs/2603.22852v1
• Date: Tue, 24 Mar 2026 06:44:46 GMT
• Title: Gau-Occ: Geometry-Completed Gaussians for Multi-Modal 3D Occupancy Prediction
• Abstract summary: Gau-Occ is a multi-modal framework that bypasses dense volumetric processing.<n>It integrates multi-view image semantics via geometry-aligned 2D sampling and cross-modal alignment.
• Score: 40.5891357994769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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