← 研踪 PaperTrailCVF · CVPR 2026:cvf:ce4882168d360851372fa0fc22bf44f8 · 记录 #1
Gau-Occ: Geometry-Completed Gaussians for Multi-Modal 3D Occupancy Prediction
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 00:22
摘要
3D semantic occupancy prediction is crucial for autonomous driving, yet vision-only approaches suffer from weak geometric cues, and existing multi-modal frameworks often depend on dense voxel or BEV tensors that impose heavy computational cost. We present Gau-Occ, a multi-modal framework that models the scene as a compact collection of semantic 3D Gaussians, enabling geometry-guided fusion without dense volumetric processing. To enhance geometric completeness, a learned LiDAR Completion Diffuser (LCD) trained on real-world priors recovers missing structures from sparse LiDAR, and the completed points are encoded as semantic Gaussian anchors. To further integrate multi-view image semantics, we introduce Gaussian Anchor Fusion (GAF), a geometry-aligned aggregation module that performs anchor-guided 2D sampling, local neighborhood encoding, and cross-modal alignment. By constructing locally aggregated Gaussian descriptors that capture spatial consistency and semantic discriminability, GAF facilitates accurate feature association across modalities. Through anchor-driven refinement of Gaussian attributes, Occ-GS supports detailed 3D occupancy prediction. Extensive experiments across challenging benchmarks demonstrate that Occ-GS achieves state-of-the-art performance.
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记录 #1 · 2026 · 最新观测版本
Gau-Occ: Geometry-Completed Gaussians for Multi-Modal 3D Occupancy Prediction
3D semantic occupancy prediction is crucial for autonomous driving, yet vision-only approaches suffer from weak geometric cues, and existing multi-modal frameworks often depend on dense voxel or BEV tensors that impose heavy computational cost. We present Gau-Occ, a multi-modal framework that models the scene as a compact collection of semantic 3D Gaussians, enabling geometry-guided fusion without dense volumetric processing. To enhance geometric completeness, a learned LiDAR Completion Diffuser (LCD) trained on real-world priors recovers missing structures from sparse LiDAR, and the completed points are encoded as semantic Gaussian anchors. To further integrate multi-view image semantics, we introduce Gaussian Anchor Fusion (GAF), a geometry-aligned aggregation module that performs anchor-guided 2D sampling, local neighborhood encoding, and cross-modal alignment. By constructing locally aggregated Gaussian descriptors that capture spatial consistency and semantic discriminability, GAF facilitates accurate feature association across modalities. Through anchor-driven refinement of Gaussian attributes, Occ-GS supports detailed 3D occupancy prediction. Extensive experiments across challenging benchmarks demonstrate that Occ-GS achieves state-of-the-art performance.
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