Photometric approach to surface reconstruction of artist paintings

Abstract

We propose a method for surface reconstruction of artist paintings. In order to reproduce the appearance of a painting, including color, surface texture, and glossiness, it is essential to acquire the pixel-wise light reflection property and orientation of the surface and render an image under an arbitrary lighting condition. A photometric approach is used to estimate bidirectional reflectance distribution functions (BRDFs) and surface normals from a set of images photographed by a fixed camera with sparsely distributed point light sources. A robust and computationally less expensive nonlinear optimization algorithm is proposed that optimizes the small number of parameters to simultaneously determine all of the specular BRDF, diffuse albedo, and surface normal. The proposed method can be applied to moderately glossy surfaces without separating captured images into diffuse and specular reflections beforehand. Experiments were conducted using oil paintings with different surface glossiness. The effectiveness of the proposed method is validated by comparing captured and rendered images.