NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination

TL;DR

NeRFactor is a neural approach that recovers 3D shape, reflectance, and lighting from multi-view images under unknown illumination, enabling realistic relighting and material editing without supervision.

Contribution

It introduces Neural Radiance Factorization (NeRFactor), a method that jointly refines geometry, reflectance, and lighting from images using a neural radiance field representation.

Findings

Outperforms state-of-the-art methods in relighting and shape recovery.

Successfully separates shadows from albedo under arbitrary lighting.

Works on both synthetic and real scenes with convincing results.

Abstract

We address the problem of recovering the shape and spatially-varying reflectance of an object from multi-view images (and their camera poses) of an object illuminated by one unknown lighting condition. This enables the rendering of novel views of the object under arbitrary environment lighting and editing of the object's material properties. The key to our approach, which we call Neural Radiance Factorization (NeRFactor), is to distill the volumetric geometry of a Neural Radiance Field (NeRF) [Mildenhall et al. 2020] representation of the object into a surface representation and then jointly refine the geometry while solving for the spatially-varying reflectance and environment lighting. Specifically, NeRFactor recovers 3D neural fields of surface normals, light visibility, albedo, and Bidirectional Reflectance Distribution Functions (BRDFs) without any supervision, using only a re-rendering loss, simple smoothness priors, and a data-driven BRDF prior learned from real-world BRDF measurements. By explicitly modeling light visibility, NeRFactor is able to separate shadows from albedo and synthesize realistic soft or hard shadows under arbitrary lighting conditions. NeRFactor is able to recover convincing 3D models for free-viewpoint relighting in this challenging and underconstrained capture setup for both synthetic and real scenes. Qualitative and quantitative experiments show that NeRFactor outperforms classic and deep learning-based state of the art across various tasks. Our videos, code, and data are available at people.csail.mit.edu/xiuming/projects/nerfactor/.