论文标题
铁：通过优化光度图像的神经SDF和材料来呈逆渲染
IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images
论文作者
论文摘要
我们提出了一个称为Iron的神经反向渲染管道，该管道在光度图像上运行，并以三角形网格格式和材料纹理的格式输出高质量的3D内容，易于在现有的图形管道中部署。我们的方法在优化过程中采用神经表示几何形状作为签名距离场（SDF）和材料，以享受其灵活性和紧凑性，并具有针对神经SDF的混合优化方案：首先，使用体积射度范围进行优化，使用体积射度范围的磁场方法来恢复正确的拓扑，然后使用基于Edgeaware的基于基于Edgeaware的表面表面材料进行材料和不合同的材料，并进行了材料，并进行了材料，并进行了不断的材料。在第二阶段，我们还从基于网格的可区分渲染中汲取灵感，并为神经SDF设计一种新颖的边缘采样算法，以进一步提高性能。我们表明，与先前的作品相比，我们的铁可以实现逆呈现质量的明显更好。我们的项目页面在这里：https：//kai-46.github.io/iron-website/
We propose a neural inverse rendering pipeline called IRON that operates on photometric images and outputs high-quality 3D content in the format of triangle meshes and material textures readily deployable in existing graphics pipelines. Our method adopts neural representations for geometry as signed distance fields (SDFs) and materials during optimization to enjoy their flexibility and compactness, and features a hybrid optimization scheme for neural SDFs: first, optimize using a volumetric radiance field approach to recover correct topology, then optimize further using edgeaware physics-based surface rendering for geometry refinement and disentanglement of materials and lighting. In the second stage, we also draw inspiration from mesh-based differentiable rendering, and design a novel edge sampling algorithm for neural SDFs to further improve performance. We show that our IRON achieves significantly better inverse rendering quality compared to prior works. Our project page is here: https://kai-46.github.io/IRON-website/
