Efficient Photometric Stereo Using Kernel Regression.

Photometric stereo estimates surface normals from multiple images captured under different light directions using a fixed camera. To deal with non-Lambertian reflections, the recent photometric stereo methods employ iterative or optimization frameworks that are computationally expensive. This paper proposes an efficient photometric stereo method using kernel regression, which can be transformed to an eigendecomposition problem.

Photometric Stereo for General BRDFs via Reflection Sparsity Modeling.

This paper proposes a pixelwise photometric stereo method for object surfaces with general bidirectional reflectance distribution functions (BRDFs) via appropriate reflection modeling. The modeling is based on three general characteristics of reflection components, i.e.