OMEGAS: Object Mesh Extraction from Large Scenes Guided by Gaussian Segmentation

• URL: http://arxiv.org/abs/2404.15891v4
• Date: Tue, 27 Aug 2024 22:09:19 GMT
• Title: OMEGAS: Object Mesh Extraction from Large Scenes Guided by Gaussian Segmentation
• Authors: Lizhi Wang, Feng Zhou, Bo yu, Pu Cao, Jianqin Yin,
• Abstract summary: It is difficult to precisely reconstruct specific objects from large scenes. Current scene reconstruction techniques frequently result in the loss of object detail textures. We propose a framework termed OMEGAS: Object Extraction from Large Scenes Guided by Gaussian. We demonstrate that our method can accurately reconstruct specific targets from large scenes, both quantitatively and qualitatively.
• Score: 15.833273340802311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in 3D reconstruction technologies have paved the way for high-quality and real-time rendering of complex 3D scenes. Despite these achievements, a notable challenge persists: it is difficult to precisely reconstruct specific objects from large scenes. Current scene reconstruction techniques frequently result in the loss of object detail textures and are unable to reconstruct object portions that are occluded or unseen in views. To address this challenge, we delve into the meticulous 3D reconstruction of specific objects within large scenes and propose a framework termed OMEGAS: Object Mesh Extraction from Large Scenes Guided by Gaussian Segmentation. Specifically, we proposed a novel 3D target segmentation technique based on 2D Gaussian Splatting, which segments 3D consistent target masks in multi-view scene images and generates a preliminary target model. Moreover, to reconstruct the unseen portions of the target, we propose a novel target replenishment technique driven by large-scale generative diffusion priors. We demonstrate that our method can accurately reconstruct specific targets from large scenes, both quantitatively and qualitatively. Our experiments show that OMEGAS significantly outperforms existing reconstruction methods across various scenarios. Our project page is at: https://github.com/CrystalWlz/OMEGAS

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