GaussianSeed: 高解像度3D占有予測のための階層的ガウスシーディング
GaussianSeed: Hierarchical Gaussian Seeding for High-Resolution 3D Occupancy Prediction
高解像度の3D占有予測を効率的に行うため、粗密の階層構造を持つガウスプリミティブを用いたフレームワークを提案し、0.1m解像度でリアルタイム推論を実現した。
著者: Xinzhuo Li, Xianghui Pan, Jiayuan Du, Wei Wei, Liuyi Wang, Chengju Liu, Qijun Chen
分類: cs.CV
原文アブストラクト
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. Codes are available at https://github.com/Athameral/GUSD