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:
this https URL

从大场景中精确重建特定物体的 3D 重建技术进展，提出了一种名为 OMEGAS 的框架，通过分割引导和分析方法，实现了准确的物体分割和背景去除，从而有效地提取 3D 物体网格。