GaussianSeed: Hierarchical Gaussian Seeding for High-Resolution 3D Occupancy Prediction

2026-07-22

AI Summary

Existing high-resolution 3D occupancy prediction methods are hindered by substantial computational costs and memory bottlenecks, making it challenging to achieve both accuracy and real-time performance. This work proposes a progressive multi-scale Gaussian occupancy prediction framework that overcomes the memory limitations of dense voxel representations through a hierarchical Gaussian seeding mechanism, which constructs a coarse-to-fine structure of Gaussian primitives. By integrating hierarchical Gaussian modeling, multi-scale feature fusion, and a hybrid sparse-dense representation, our approach enables end-to-end trainable and highly efficient inference. To support this research, we introduce TJScenes, the first panoramic six-camera dataset with fine-grained 0.1-meter occupancy annotations. Evaluated on both Occ3D-nuScenes and TJScenes, our method achieves state-of-the-art geometry reconstruction accuracy while significantly reducing inference latency, establishing a new Pareto frontier in the trade-off between efficiency and precision.

Abstract

Vision-centric 3D occupancy prediction provides dense scene representations essential for autonomous driving and robotic navigation, yet existing methods struggle to scale to high voxel resolutions due to prohibitive computational costs. To address this, we introduce GaussianSeed, a progressive multi-scale Gaussian occupancy prediction framework that organizes primitives into a coarse-to-fine hierarchy. Benefiting from this hierarchical design, GaussianSeed effectively circumvents the memory bottlenecks inherent in dense representations, successfully scaling to a $0.1\text{m}$ spatial resolution while maintaining real-time inference capabilities. To comprehensively evaluate high-resolution geometric perception, we further construct TJScenes, a panoramic six-camera occupancy dataset with highly detailed $0.1\text{m}$ annotations. Extensive experiments on Occ3D-nuScenes and TJScenes demonstrate that GaussianSeed delivers the lowest latency among all evaluated methods while maintaining highly competitive accuracy, advancing the efficiency-quality frontier of high-resolution 3D occupancy prediction.

Problem

• 3D occupancy prediction
• high-resolution
• computational cost
• real-time inference
• dense scene representation

Innovation

• Gaussian seeding
• hierarchical representation
• 3D occupancy prediction
• high-resolution
• real-time inference