Using Near-Field Light Sources to Separate Illumination from BRDF

Abstract

Simultaneous estimation of lighting and BRDF (Bidirectional Reflectance Distribution Function) from multi-view images is an interesting problem in computer vision. It allows for exciting applications, such as flexible relighting in post-production, without recapturing the scene. The ability to alter scenes after they have been filmed has the potential to greatly reduce the number of costly recapturing iterations. Unfortunately, the estimation problem is made difficult because lighting and BRDF have closely entangled effects in the input images. This paper presents an algorithm to support both the estimation of distant and near-field illumination. Previous techniques are limited to distant lighting. We contribute by proposing an additional factorization of the lighting, while keeping the rendering efficient and additional data compactly stored in the wavelet domain. We reduce complexity by clustering the scene geometry into a few groups of important emitters and calculate the emitting powers by alternately solving for illumination and reflectance. We demonstrate our work on a synthetic and real datasets and show that a clean separation of distant and near-field illumination leads to a more accurate estimation and separation of lighting and BRDF.