Mirror-NeRF: Learning Neural Radiance Fields for Mirrors with Whitted-Style Ray Tracing

TL;DR

Mirror-NeRF introduces a neural rendering framework that accurately models reflections in mirrors by integrating Whitted-Style Ray Tracing, enabling realistic scene reconstruction and manipulation involving mirrors and reflections.

Contribution

It proposes a unified radiance field incorporating reflection probability and ray tracing, improving mirror modeling in neural radiance fields over prior methods.

Findings

Outperforms existing methods on synthetic and real datasets.

Accurately reconstructs mirror geometry and reflections.

Supports scene editing with mirror manipulation.

Abstract

Recently, Neural Radiance Fields (NeRF) has exhibited significant success in novel view synthesis, surface reconstruction, etc. However, since no physical reflection is considered in its rendering pipeline, NeRF mistakes the reflection in the mirror as a separate virtual scene, leading to the inaccurate reconstruction of the mirror and multi-view inconsistent reflections in the mirror. In this paper, we present a novel neural rendering framework, named Mirror-NeRF, which is able to learn accurate geometry and reflection of the mirror and support various scene manipulation applications with mirrors, such as adding new objects or mirrors into the scene and synthesizing the reflections of these new objects in mirrors, controlling mirror roughness, etc. To achieve this goal, we propose a unified radiance field by introducing the reflection probability and tracing rays following the light transport model of Whitted Ray Tracing, and also develop several techniques to facilitate the learning process. Experiments and comparisons on both synthetic and real datasets demonstrate the superiority of our method. The code and supplementary material are available on the project webpage: https://zju3dv.github.io/Mirror-NeRF/.