In this paper, we propose a novel end-to-end relightable neural inverse rendering system that achieves high-quality reconstruction of geometry and material properties, thus enabling high-quality relighting. The cornerstone of our method is a two-stage approach for learning a better factorization of scene parameters. In the first stage, we develop a reflection-aware radiance field using a neural signed distance field (SDF) as the geometry representation and deploy an MLP (multilayer perceptron) to estimate indirect illumination. In the second stage, we introduce a novel information-sharing network structure to jointly learn the radiance field and the physically based factorization of the scene. For the physically based factorization, to reduce the noise caused by Monte Carlo sampling, we apply a split-sum approximation with a simplified Disney BRDF and cube mipmap as the environment light representation. In the relighting phase, to enhance the quality of indirect illumination, we propose a second split-sum algorithm to trace secondary rays under the split-sum rendering framework.Furthermore, there is no dataset or protocol available to quantitatively evaluate the inverse rendering performance for glossy objects. To assess the quality of material reconstruction and relighting, we have created a new dataset with ground truth BRDF parameters and relighting results. Our experiments demonstrate that our algorithm achieves state-of-the-art performance in inverse rendering and relighting, with particularly strong results in the reconstruction of highly reflective objects.

翻译：本文提出了一种新颖的端到端可重光照神经逆向渲染系统，能够高质量地重建几何与材质属性，从而实现高品质的重光照效果。本方法的核心在于采用两阶段策略以学习更优的场景参数分解。第一阶段，我们利用神经符号距离场作为几何表示，构建反射感知的辐射场，并部署多层感知机来估算间接光照。第二阶段，我们引入一种创新的信息共享网络结构，以联合学习辐射场与基于物理的场景分解。在基于物理的分解过程中，为降低蒙特卡洛采样引入的噪声，我们采用基于简化Disney BRDF的分裂求和近似方法，并以立方体贴图mipmap作为环境光表示。在重光照阶段，为提升间接光照质量，我们在分裂求和渲染框架下提出第二种分裂求和算法以追踪次级光线。此外，目前缺乏可用于定量评估光泽物体逆向渲染性能的数据集或评估协议。为评估材质重建与重光照的质量，我们创建了一个包含真实BRDF参数与重光照结果的新数据集。实验表明，我们的算法在逆向渲染与重光照任务中达到了最先进的性能，尤其在高度反射物体的重建方面表现出色。