{"ID":5937617,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-08T07:02:32.62247399Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04144","arxiv_id":"2607.04144","title":"Semantic-Guided Progressive Object Removal with Gaussian Splatting","abstract":"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.","short_abstract":"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,...","url_abs":"https://arxiv.org/abs/2607.04144","url_pdf":"https://arxiv.org/pdf/2607.04144v1","authors":"[\"Xianliang Huang\",\"Chen Xiao\",\"Yuanxiang Ni\",\"Guanming Liu\",\"Mingkai Liu\",\"Dikai Fan\",\"Xiao Liu\",\"Hao Zhang\"]","published":"2026-07-05T07:11:48Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}