セマンティックガイドによるガウススプラッティングを用いた段階的物体除去
Semantic-Guided Progressive Object Removal with Gaussian Splatting
3Dシーンから不要な物体を除去する際、セマンティック情報と段階的リファインメントを統合した新しいフレームワークを提案。DINOv2で他視点の意味情報を活用し、マスク領域を複数サブ領域に分けて品質の低い部分を選択的に改善することで、高品質な除去を実現。
詳しい要約
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著者: Xianliang Huang, Chen Xiao, Yuanxiang Ni, Guanming Liu, Mingkai Liu, Dikai Fan, Xiao Liu, Hao Zhang
分類: cs.RO
原文アブストラクト
Removing unwanted objects from reconstructed 3D scenes is an important task in computer vision, supporting applications in AR/VR, robotics, and digital content creation. Existing methods typically complete the entire masked region in a single step and without effectively utilizing semantic information from other views, leading to difficulties in handling complex geometric details and textures. In this work, we propose a novel framework that integrates Semantic-guided Block Matching (SBM) and Region-Wise Progressive Refinement (RPR) for high-quality 3D object removal. First, we leverage DINOv2 to encode semantic guidance from multi-view observations, and the best match tokens are decoded to complete missing regions in the target view while maintaining cross-view consistency. Second, we introduce a RPR strategy that segments the target mask into multiple subregions and selectively refines those with poor visual quality. Our method is built upon Gaussian Splatting, ensuring high-fidelity scene reconstruction with efficient computation. Experimental results demonstrate that our approach outperforms existing Gaussian-based methods in terms of perceptual quality and coherence in 3D object removal.
