GPOcc++: Unified Sparse Gaussian Occupancy Prediction with Visual Geometry Priors

2026-07-15

AI Summary

Reconstructing complete 3D occupancy scenes from visual observations in the presence of occlusions and unseen regions remains a significant challenge. This work proposes a sparse Gaussian occupancy representation that, for the first time, effectively integrates surface-centric visual geometric priors into voxel-level occupancy prediction. The method establishes a unified framework that jointly models multi-view and temporal information, enabling coherent scene understanding across both space and time. Evaluated on multiple indoor and outdoor benchmarks, the approach achieves state-of-the-art performance while maintaining computational efficiency, demonstrating strong generalization capabilities and consistent temporal coherence during inference.

Abstract

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 Gaussian representations for efficient and expressive volumetric scene modeling. Building on GPOcc, GPOcc++ models multi-view observations and temporal sequences within a unified framework, allowing spatial and temporal evidence to be handled through the same representation. We further extend GPOcc++ from indoor scenes to outdoor occupancy prediction. Extensive experiments on both indoor and outdoor benchmarks demonstrate consistently strong performance across both multi-view and temporal settings, together with favorable efficiency and generalization. Code will be released at https://github.com/JuIvyy/GPOcc.

Problem

• 3D occupancy prediction
• visual geometry priors
• occluded regions
• volumetric representation
• scene understanding

Innovation

• Sparse Gaussian Representation
• Visual Geometry Priors
• 3D Occupancy Prediction
• Unified Multi-view and Temporal Modeling
• Volumetric Scene Understanding