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. OMEGAS employs a multi-step approach, grounded in several excellent off-the-shelf methodologies. Specifically, initially, we utilize the Segment Anything Model (SAM) to guide the segmentation of 3D Gaussian Splatting (3DGS), thereby creating a basic 3DGS model of the target object. Then, we leverage large-scale diffusion priors to further refine the details of the 3DGS model, especially aimed at addressing invisible or occluded object portions from the original scene views. Subsequently, by re-rendering the 3DGS model onto the scene views, we achieve accurate object segmentation and effectively remove the background. Finally, these target-only images are used to improve the 3DGS model further and extract the definitive 3D object mesh by the SuGaR model. In various scenarios, our experiments demonstrate that OMEGAS significantly surpasses existing scene reconstruction methods. Our project page is at: https://github.com/CrystalWlz/OMEGAS

翻译：近期三维重建技术的进展为复杂三维场景的高质量实时渲染铺平了道路。然而，尽管取得这些成就，仍存在一个显著挑战：从大场景中精确重建特定物体非常困难。当前的场景重建技术常导致物体细节纹理丢失，且无法重建视野中被遮挡或不可见的物体部分。为应对这一挑战，我们深入研究了大规模场景中特定物体的精细三维重建，并提出名为OMEGAS（Object Mesh Extraction from Large Scenes Guided by GAussian Segmentation）的框架。OMEGAS采用基于多种优秀现成方法的多步骤策略。具体而言，首先利用分割一切模型（SAM）指导三维高斯溅射（3DGS）的分割，从而构建目标物体的基础3DGS模型。然后，借助大规模扩散先验进一步细化3DGS模型的细节，尤其针对原始场景视图中不可见或被遮挡的物体部分。随后，通过将3DGS模型重新渲染至场景视图，实现精确的物体分割并有效移除背景。最后，利用这些仅包含目标的图像进一步优化3DGS模型，并通过SuGaR模型提取最终的三维物体网格。在多种场景下的实验表明，OMEGAS显著超越了现有的场景重建方法。我们的项目页面位于：https://github.com/CrystalWlz/OMEGAS