RISE-SDF：一种用于光泽物体逆向渲染的可重新光照信息共享符号距离场
RISE-SDF: a Relightable Information-Shared Signed Distance Field for Glossy Object Inverse Rendering
AI总结：
本文提出了一种端到端可重新光照神经逆向渲染系统RISE-SDF，采用两阶段方法结合神经SDF与信息共享网络，联合学习辐射场与基于物理的场景分解，并创建新数据集以评估光泽物体的材质重建与重新光照质量，实现了最先进的渲染性能。
摘要：
本文提出了一种新颖的端到端可重新光照神经逆向渲染系统，实现了几何与材质属性的高质量重建，从而实现高质量重新光照。我们方法的基石是用于学习更好的场景参数分解的两阶段方法。在第一阶段，我们使用神经符号距离场（SDF）作为几何表示，开发了一个感知反射的辐射场，并部署MLP（多层感知机）来估计间接光照。在第二阶段，我们引入了一种新颖的信息共享网络结构，以联合学习辐射场和基于物理的场景分解。对于基于物理的分解，为了减少Monte Carlo采样引起的噪声，我们应用了split-sum近似，结合简化的Disney BRDF和cube mipmap作为环境光表示。在重新光照阶段，为了提高间接光照的质量，我们提出了第二种split-sum算法，在split-sum渲染框架下追踪二次射线。此外，目前没有可用的数据集或协议来定量评估光泽物体的逆向渲染性能。为了评估材质重建和重新光照的质量，我们创建了一个带有真实BRDF参数和重新光照结果的新数据集。我们的实验表明，我们的算法在逆向渲染和重新光照方面达到了最先进的性能，在高度反射物体的重建中表现尤为出色。
英文摘要：
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.
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。
