Continuous material reflectance map for deep photometric stereo.

Solving calibrated photometric stereo under a sparse set of lights is of great interest for real-world applications. Since neural networks show advantages in dealing with material appearance, this paper proposes a bidirectional reflectance distribution function (BRDF) representation, which is based on reflectance maps for a sparse set of lights and can handle various types of BRDFs. We discuss the optimal way to compute these BRDF-based photometric stereo maps regarding the shape, size, and resolution, and experimentally investigate the contribution of these maps to normal map estimation.

Accurate image quantization adapted to multisource photometric reconstruction for rough textured surface analysis.

In classical photometric stereo (PS), a Lambertian surface is illuminated from three distant light sources to recover one normal direction per pixel of the input image. In continuous noiseless cases, PS allows us to reconstruct the textured surfaces in three-dimensions with a high degree of accuracy and a high resolution. In the real world, an image is a digital quantization, a limited and noisy representation of a surface.

Specularity and shadow detection for the multisource photometric reconstruction of a textured surface.

Textured surface analysis is essential for many applications. In this paper, we present a three-dimensional (3D) recovery approach for real textured surfaces based on photometric stereo. The aim is to be able to reconstruct the textured surfaces in 3D with a high degree of accuracy.

Photometric reconstruction of a dynamic textured surface from just one color image acquisition.

Textured surface analysis is essential for many applications. We present a three-dimensional recovery approach for real textured surfaces based on photometric stereo. The aim is to be able to measure the textured surfaces with a high degree of accuracy.