Planar Reflection-Aware Neural Radiance Fields

TL;DR

This paper introduces a reflection-aware NeRF that explicitly models planar reflectors and their sources, significantly improving the rendering of high-frequency reflections and scene geometry accuracy.

Contribution

It proposes a novel NeRF extension that jointly models planar reflectors and their sources, using a sparse edge regularization to improve reflection rendering and scene reconstruction.

Findings

Enhanced reflection rendering accuracy

Improved scene geometry reconstruction

Better handling of high-frequency reflections

Abstract

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.