シェル教師付きガウススプラッティングによる都市の実-to-シム再構築
Shell-Supervised Gaussian Splatting for Urban Real-to-Sim Reconstruction
都市のファサードをビデオから3D再構築する際、ガラスや反射などで表面形状が不安定になる問題を、外部の構造シェルを幾何学的教師として用いることで解決する手法を提案した。
著者: Yuan Yang, Peijun Lu, Fangzhou Lu, Sai Fan, Siqi Yan, Chenyuan Zhang, Haobo Liang, Yichen Wang
分類: cs.CV
原文アブストラクト
Real-to-sim reconstruction for embodied AI requires geometry that is useful for collision reasoning, navigation, and agent-environment interaction, not only photorealistic novel-view synthesis. However, close-range urban facades are difficult for video-to-3D reconstruction: glass, reflections, repeated windows, and weak texture can produce visually plausible renderings with unstable surface geometry. We introduce shell-supervised Gaussian Splatting, a reconstruction-stage framework that uses an external facade structural shell as lightweight geometric supervision for video-driven Gaussian reconstruction. The method aligns an exterior shell to the video reconstruction frame, renders per-view depth, camera-space normal, and valid-mask maps, and applies these cues through mask-gated losses during Gaussian optimization. This design preserves RGB-driven appearance while regularizing only visible shell-supported facade regions. Experiments on anonymized close-range urban facade scenes show improved facade orientation and visible-surface point-cloud consistency over photo-only, monocular-cue, and surface-oriented Gaussian baselines, while maintaining comparable held-out rendering quality.