Neural Radiance Fields (NeRF) have demonstrated exceptional capabilities in reconstructing complex scenes with high fidelity. However, NeRF's view dependency can only handle low-frequency reflections. It falls short when handling complex planar reflections, often interpreting them as erroneous scene geometries and leading to duplicated and inaccurate scene representations. To address this challenge, we introduce a reflection-aware NeRF that jointly models planar reflectors, such as windows, and explicitly casts reflected rays to capture the source of the high-frequency reflections. We query a single radiance field to render the primary color and the source of the reflection. We propose a sparse edge regularization to help utilize the true sources of reflections for rendering planar reflections rather than creating a duplicate along the primary ray at the same depth. As a result, we obtain accurate scene geometry. Rendering along the primary ray results in a clean, reflection-free view, while explicitly rendering along the reflected ray allows us to reconstruct highly detailed reflections. Our extensive quantitative and qualitative evaluations of real-world datasets demonstrate our method's enhanced performance in accurately handling reflections.

该研究解决了现有神经辐射场在处理复杂平面反射时的局限性，尤其是低频反射不足的问题。提出了一种反射感知的神经辐射场，能够同时建模平面反射体并精确捕捉高频反射的源。研究结果表明，该方法显著提高了场景几何的准确性和反射效果的细节展示。

反射感知的平面神经辐射场