利用原始网格增强3D Gaussian Splatting以实现建筑物的照片级真实重建
Enhancement of 3D Gaussian Splatting using Raw Mesh for Photorealistic Recreation of Architectures
AI总结：
本文提出利用现有原始3D模型引导3D Gaussian Splatting捕捉建筑基本形状，在非系统化拍摄下改善纹理与细节的视觉质量，提升建筑设计领域的3D重建效果。
摘要：
建筑场景的照片级真实重建与渲染在电影、游戏和交通等行业有广泛应用。它在城市规划、建筑设计及城市推广中也发挥重要作用，特别是在保护历史文物方面。由于优于NeRF的性能，3D Gaussian Splatting已成为3D重建的主流技术。其唯一输入是一组图像，但严重依赖SfM过程计算的几何参数。同时，现有大量原始3D模型虽能为特定建筑的结构感知提供信息，却无法被应用。本文提出一种直接方法，利用这些原始3D模型引导3D Gaussians捕捉建筑基本形状，并在非系统化拍摄照片时改善纹理与细节的视觉质量。这一探索为提升建筑设计领域3D重建技术的有效性开辟了新可能。
英文摘要：
The photorealistic reconstruction and rendering of architectural scenes have extensive applications in industries such as film, games, and transportation. It also plays an important role in urban planning, architectural design, and the city's promotion, especially in protecting historical and cultural relics. The 3D Gaussian Splatting, due to better performance over NeRF, has become a mainstream technology in 3D reconstruction. Its only input is a set of images but it relies heavily on geometric parameters computed by the SfM process. At the same time, there is an existing abundance of raw 3D models, that could inform the structural perception of certain buildings but cannot be applied. In this paper, we propose a straightforward method to harness these raw 3D models to guide 3D Gaussians in capturing the basic shape of the building and improve the visual quality of textures and details when photos are captured non-systematically. This exploration opens up new possibilities for improving the effectiveness of 3D reconstruction techniques in the field of architectural design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
