Implicit representations like Neural Radiance Fields (NeRF) showed impressive results for photorealistic rendering of complex scenes with fine details. However, ideal or near-perfectly specular reflecting objects such as mirrors, which are often encountered in various indoor scenes, impose ambiguities and inconsistencies in the representation of the reconstructed scene leading to severe artifacts in the synthesized renderings. In this paper, we present a novel reflection tracing method tailored for the involved volume rendering within NeRF that takes these mirror-like objects into account while avoiding the cost of straightforward but expensive extensions through standard path tracing. By explicitly modeling the reflection behavior using physically plausible materials and estimating the reflected radiance with Monte-Carlo methods within the volume rendering formulation, we derive efficient strategies for importance sampling and the transmittance computation along rays from only few samples. We show that our novel method enables the training of consistent representations of such challenging scenes and achieves superior results in comparison to previous state-of-the-art approaches.

翻译：隐式表示如神经辐射场（NeRF）在具备精细细节的复杂场景的光照真实感渲染中展现了令人印象深刻的结果。然而，室内场景中常见的理想或近乎完美的镜面反射物体（如镜子）会在重建场景的表示中引入歧义和不一致性，导致合成渲染中出现严重伪影。本文提出一种针对NeRF体渲染过程定制的反射追踪新方法，该方法在兼顾镜面类物体的同时，避免了通过标准路径追踪进行直接但昂贵的扩展成本。通过使用物理合理材质显式建模反射行为，并在体渲染框架内采用蒙特卡洛方法估算反射辐射度，我们推导出仅需少量样本即可实现重点采样与沿射线透射率计算的高效策略。实验表明，我们的新方法能够训练此类挑战性场景的一致表示，并在与先前最先进方法的对比中取得更优结果。