Efficient Interactive 3D Multi-Object Removal

• URL: http://arxiv.org/abs/2501.17636v2
• Date: Thu, 30 Jan 2025 05:18:21 GMT
• Title: Efficient Interactive 3D Multi-Object Removal
• Authors: Jingcheng Ni, Weiguang Zhao, Daniel Wang, Ziyao Zeng, Chenyu You, Alex Wong, Kaizhu Huang,
• Abstract summary: We propose an efficient and user-friendly pipeline for 3D multi-object removal. To ensure object consistency and correspondence across multiple views, we propose a novel mask matching and refinement module. Our method significantly reduces computational costs, achieving processing speeds more than 80% faster than state-of-the-art methods.
• Score: 25.832938786291358
• License:
• Abstract: Object removal is of great significance to 3D scene understanding, essential for applications in content filtering and scene editing. Current mainstream methods primarily focus on removing individual objects, with a few methods dedicated to eliminating an entire area or all objects of a certain category. They however confront the challenge of insufficient granularity and flexibility for real-world applications, where users demand tailored excision and preservation of objects within defined zones. In addition, most of the current methods require kinds of priors when addressing multi-view inpainting, which is time-consuming. To address these limitations, we propose an efficient and user-friendly pipeline for 3D multi-object removal, enabling users to flexibly select areas and define objects for removal or preservation. Concretely, to ensure object consistency and correspondence across multiple views, we propose a novel mask matching and refinement module, which integrates homography-based warping with high-confidence anchor points for segmentation. By leveraging the IoU joint shape context distance loss, we enhance the accuracy of warped masks and improve subsequent inpainting processes. Considering the current immaturity of 3D multi-object removal, we provide a new evaluation dataset to bridge the developmental void. Experimental results demonstrate that our method significantly reduces computational costs, achieving processing speeds more than 80% faster than state-of-the-art methods while maintaining equivalent or higher reconstruction quality.

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