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

翻译：近年来，三维重建技术的进步为实现复杂三维场景的高质量实时渲染铺平了道路。尽管取得了这些成就，一个显著的挑战依然存在：难以从大场景中精确重建特定物体。当前的场景重建技术常常导致物体细节纹理的丢失，并且无法重建在视角中被遮挡或不可见的物体部分。为应对这一挑战，我们深入研究了大场景中特定物体的精细化三维重建，并提出了一个名为OMEGAS的框架：基于高斯分割的大场景物体网格提取。具体而言，我们提出了一种基于二维高斯泼溅的新型三维目标分割技术，该技术可在多视角场景图像中分割出三维一致的目标掩码，并生成初步的目标模型。此外，为重建目标的不可见部分，我们提出了一种由大规模生成扩散先验驱动的新型目标补全技术。我们通过定量和定性分析证明，我们的方法能够从大场景中精确重建特定目标。实验表明，OMEGAS在各种场景下均显著优于现有的重建方法。我们的项目页面位于：https://github.com/CrystalWlz/OMEGAS