GS-2M: 基于多视角光照变化的材质感知高保真网格重建
GS-2M: Material-aware Gaussian Splatting for High-fidelity Mesh Reconstruction
AI总结：
GS-2M通过多视角光照变化的粗糙度监督策略，实现高保真网格重建，有效应对高反射表面的挑战。
摘要：
我们提出了一种基于3D高斯点划的材料感知优化框架，用于从多视角图像中进行高保真的网格重建，称为GS-2M。先前的工作将这些任务分开处理，并在重建高反射表面时遇到困难，通常依赖于外部模型的先验知识来增强分解结果。相反，我们的方法通过联合优化与渲染深度和法线质量相关的属性来解决这两个问题，在保持几何细节的同时对反射表面具有鲁棒性。尽管当前的工作能够有效解决这些任务，但它们通常采用复杂的神经网络组件来学习场景属性，这在大规模场景中限制了性能。为了进一步消除这些神经组件，我们提出了一种基于多视角光照变化的新的粗糙度监督策略。当与精心设计的损失函数和优化过程结合时，我们的统一框架能够产生与最先进的方法相当的重建结果，即使在反射表面上也能生成准确的三角网格。我们通过广泛使用的先前工作数据集和与最先进的表面重建方法的定性比较验证了我们方法的有效性。项目页面：https://ndming.github.io/publications/gs2m/。
英文摘要：
We propose a material-aware optimization framework for high-fidelity mesh reconstruction from multi-view images based on 3D Gaussian Splatting, referred to as GS-2M. Previous works handle these tasks separately and struggle to reconstruct highly reflective surfaces, often relying on priors from external models to enhance the decomposition results. Conversely, our method addresses these two problems by jointly optimizing attributes relevant to the quality of rendered depth and normals, maintaining geometric details while being resilient to reflective surfaces. Although contemporary works effectively solve these tasks together, they often employ sophisticated neural components to learn scene properties, which hinders their performance at scale. To further eliminate these neural components, we propose a novel roughness supervision strategy based on multi-view photometric variation. When combined with a carefully designed loss and optimization process, our unified framework produces reconstruction results comparable to state-of-the-art methods, delivering accurate triangle meshes even for reflective surfaces. We validate the effectiveness of our approach with widely used datasets from previous works and qualitative comparisons with state-of-the-art surface reconstruction methods. Project page: https://ndming.github.io/publications/gs2m/.
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。
