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.

翻译：神经辐射场（NeRF）在重建复杂场景方面展现出卓越的高保真能力。然而，NeRF的视角依赖性仅能处理低频反射。当面对复杂的平面反射时，其表现不足，常将其误判为错误的场景几何结构，导致场景表示出现重复且不准确的问题。为解决这一挑战，我们提出了一种反射感知的NeRF，它联合建模平面反射体（如窗户），并显式投射反射光线以捕捉高频反射的来源。我们通过查询单一辐射场来渲染主颜色及反射源。我们提出了一种稀疏边缘正则化方法，以帮助利用真实的反射源来渲染平面反射，而非在主光线相同深度处创建重复内容。由此，我们获得了准确的场景几何结构。沿主光线渲染可得到清晰无反射的视图，而沿反射光线显式渲染则使我们能够重建高度细节化的反射。我们在真实世界数据集上进行的大量定量与定性评估表明，本方法在准确处理反射方面具有显著提升的性能。