Complementary Intrinsics from Neural Radiance Fields and CNNs for Outdoor Scene Relighting

Abstract

Relighting an outdoor scene is challenging due to the diverse illuminations and salient cast shadows. Intrinsic image decomposition on outdoor photo collections could partly solve this problem by weakly supervised labels with albedo and normal consistency from multiview stereo. With neural radiance fields (NeRF), editing the appearance code could produce more realistic results without interpreting the outdoor scene image formation explicitly. This paper proposes to complement the intrinsic estimation from volume rendering using NeRF and from inversing the photometric image formation model using convolutional neural networks (CNNs). The former produces richer and more reliable pseudo labels (cast shadows and sky appearances in addition to albedo and normal) for training the latter to predict interpretable and editable lighting parameters via a single-image prediction pipeline. We demonstrate the advantages of our method for both intrinsic image decomposition and relighting for various real outdoor scenes.