GPOcc++: Unified Sparse Gaussian Occupancy Prediction with Visual Geometry Priors
Accurate 3D scene understanding is fundamental to embodied intelligence and autonomous driving, where 3D occupancy provides a unified representation of objects, structures, and free space. However, recovering such a complete volumetric representation from visual observations remains challenging, particularly in occluded and unobserved regions. Visual geometry priors offer strong and generalizable geometric cues for addressing this challenge, but their outputs are inherently surface-centric, whereas occupancy prediction requires reasoning about volumetric interiors and free space. To bridge this gap, we introduce GPOcc, which transforms visual geometry priors into occupancy-aware sparse Ga...
| Authors | Changqing Zhou, Yueru Luo, Yulan Guo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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