Recent advances in neural rendering have shown great potential for reconstructing scenes from multiview images. However, accurately representing objects with glossy surfaces remains a challenge for existing methods. In this work, we introduce ENVIDR, a rendering and modeling framework for high-quality rendering and reconstruction of surfaces with challenging specular reflections. To achieve this, we first propose a novel neural renderer with decomposed rendering components to learn the interaction between surface and environment lighting. This renderer is trained using existing physically based renderers and is decoupled from actual scene representations. We then propose an SDF-based neural surface model that leverages this learned neural renderer to represent general scenes. Our model additionally synthesizes indirect illuminations caused by inter-reflections from shiny surfaces by marching surface-reflected rays. We demonstrate that our method outperforms state-of-art methods on challenging shiny scenes, providing high-quality rendering of specular reflections while also enabling material editing and scene relighting.

翻译：近期神经渲染领域的进展展示了从多视图图像重建场景的巨大潜力。然而，现有方法在处理具有光泽表面的物体时仍面临挑战。本文提出ENVIDR——一种面向高质量渲染与高光反射表面重建的渲染建模框架。为实现这一目标，我们首先提出一种具有分解式渲染组件的新型神经渲染器，用于学习表面与环境光照之间的交互作用。该渲染器通过现有基于物理的渲染器进行训练，并与实际场景表征解耦。随后我们提出基于SDF的神经表面模型，借助已学习的神经渲染器来表征通用场景。该模型通过追踪表面反射光线，进一步合成由光泽表面相互反射引起的间接光照。实验表明，本方法在具有挑战性的光泽场景中优于现有技术，不仅能实现高光反射的高质量渲染，还支持材质编辑与场景重光照。