Non-Lambertian Surface Shape and Reflectance Reconstruction Using Concentric Multi-Spectral Light Field
This addresses the challenge of 3D reconstruction for surfaces with arbitrary materials in computer vision, representing a novel method rather than an incremental improvement.
The paper tackles the problem of recovering shape and reflectance for non-Lambertian surfaces, which is challenging due to view-dependent appearance, by introducing a concentric multi-spectral light field system that achieves this in one shot, outperforming state-of-the-art light field-based techniques in non-Lambertian scenes.
Recovering the shape and reflectance of non-Lambertian surfaces remains a challenging problem in computer vision since the view-dependent appearance invalidates traditional photo-consistency constraint. In this paper, we introduce a novel concentric multi-spectral light field (CMSLF) design that is able to recover the shape and reflectance of surfaces with arbitrary material in one shot. Our CMSLF system consists of an array of cameras arranged on concentric circles where each ring captures a specific spectrum. Coupled with a multi-spectral ring light, we are able to sample viewpoint and lighting variations in a single shot via spectral multiplexing. We further show that such concentric camera/light setting results in a unique pattern of specular changes across views that enables robust depth estimation. We formulate a physical-based reflectance model on CMSLF to estimate depth and multi-spectral reflectance map without imposing any surface prior. Extensive synthetic and real experiments show that our method outperforms state-of-the-art light field-based techniques, especially in non-Lambertian scenes.