通过纹理引导联合优化网格与高斯点云，提升多视角三维重建质量。
Improving Multi-View Reconstruction via Texture-Guided Gaussian-Mesh Joint Optimization
- 用高斯引导的可微渲染同步优化网格几何与顶点颜色。
- 在DTU、BlendedMVS等数据集上实现更精确的深度和外观重建。
- 适合需要高质量可编辑3D模型的AR/VR与数字内容创作场景。
从多视角图像重建真实物体对3D编辑、AR/VR及数字内容创作至关重要。现有方法通常侧重几何精度（多视角立体）或逼真渲染（新视角合成），常将几何与外观优化解耦，影响下游编辑任务。本文提出统一处理几何与外观优化的框架，实现无缝的高斯-网格联合优化。具体而言，我们设计一种新方法，通过高斯引导的网格可微渲染，同时优化网格几何（顶点位置与面片）和顶点颜色，利用输入图像的光度一致性以及法向量和深度图的几何正则化。所获高质量3D重建可进一步用于下游编辑任务，如重新照明与形状变形。代码将发布于https://github.com/zhejia01/TexGuided-GS2Mesh。
原文摘要 · Abstract (English)
Reconstructing real-world objects from multi-view images is essential for applications in 3D editing, AR/VR, and digital content creation. Existing methods typically prioritize either geometric accuracy (Multi-View Stereo) or photorealistic rendering (Novel View Synthesis), often decoupling geometry and appearance optimization, which hinders downstream editing tasks. This paper advocates an unified treatment on geometry and appearance optimization for seamless Gaussian-mesh joint optimization. More specifically, we propose a novel framework that simultaneously optimizes mesh geometry (vertex positions and faces) and vertex colors via Gaussian-guided mesh differentiable rendering, leveraging photometric consistency from input images and geometric regularization from normal and depth maps. The obtained high-quality 3D reconstruction can be further exploit in down-stream editing tasks, such as relighting and shape deformation. Our code will be released in https://github.com/zhejia01/TexGuided-GS2Mesh
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