IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images

Differentiable Rendering of Neural SDFs through Reparameterization

We present a method to automatically compute correct gradients with respect to geometric scene parameters in neural SDF renderers. Recent physically-based differentiable rendering techniques for meshes have used edge-sampling to handle discontinuities, particularly at object silhouettes, but SDFs do not have a simple parametric form amenable to sampling. Instead, our approach builds on area-sampling techniques and develops a continuous warping function for SDFs to account for these discontinuities. Our method leverages the distance to surface encoded in an SDF and uses quadrature on sphere tra…

ARAH: Animatable Volume Rendering of Articulated Human SDFs

Combining human body models with differentiable rendering has recently enabled animatable avatars of clothed humans from sparse sets of multi-view RGB videos. While state-of-the-art approaches achieve realistic appearance with neural radiance fields (NeRF), the inferred geometry often lacks detail due to missing geometric constraints. Further, animating avatars in out-of-distribution poses is not yet possible because the mapping from observation space to canonical space does not generalize faithfully to unseen poses. In this work, we address these shortcomings and propose a model to create ani…

一种对SDF进行可微渲染的简单方法

A Simple Approach to Differentiable Rendering of SDFs

本文提出一种基于SDF的简单可微渲染算法，通过将边界积分扩展为薄带区域，以接受非零偏差换取低方差和架构简洁性，在端到端逆渲染任务中取得有竞争力或更优的结果。

BakedSDF：为实时视图合成将神经SDF网格化

BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis

提出BakedSDF方法，通过优化混合神经体积-表面表示并烘焙为高质量三角网格，结合球面高斯外观模型，实现高精度、低功耗的实时新视图合成，并支持外观编辑与物理仿真。

利用三维生成对抗网络学习有效的NeRF与SDF表示以实现三维物体生成

Learning Effective NeRFs and SDFs Representations with 3D Generative Adversarial Networks for 3D Object Generation

本文提出一种基于几何感知三维GAN并结合标签嵌入与颜色映射的三维物体生成方法，同时学习NeRF和SDF表示，仅需少量图像即可跨类别训练，并在ICCV 2023 OmniObject3D挑战赛中进入前三。

SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization

We propose SDFDiff, a novel approach for image-based shape optimization using differentiable rendering of 3D shapes represented by signed distance functions (SDFs). Compared to other representations, SDFs have the advantage that they can represent shapes with arbitrary topology, and that they guarantee watertight surfaces. We apply our approach to the problem of multi-view 3D reconstruction, where we achieve high reconstruction quality and can capture complex topology of 3D objects. In addition, we employ a multi-resolution strategy to obtain a robust optimization algorithm. We further demonst…