Robust Photometric Stereo via Dictionary Learning
This work addresses robust 3D reconstruction for objects with complex reflectance patterns, representing an incremental improvement over existing robust photometric stereo methods.
The paper tackles the problem of reconstructing normal vectors from images under varying lighting in photometric stereo, which is limited by diffuse surface models and sensitivity to non-idealities, by proposing a dictionary learning approach that achieves state-of-the-art performance on synthetic and real benchmark datasets.
Photometric stereo is a method that seeks to reconstruct the normal vectors of an object from a set of images of the object illuminated under different light sources. While effective in some situations, classical photometric stereo relies on a diffuse surface model that cannot handle objects with complex reflectance patterns, and it is sensitive to non-idealities in the images. In this work, we propose a novel approach to photometric stereo that relies on dictionary learning to produce robust normal vector reconstructions. Specifically, we develop two formulations for applying dictionary learning to photometric stereo. We propose a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertian reflectance model. We then generalize this model to explicitly model non-Lambertian objects. We investigate both approaches through extensive experimentation on synthetic and real benchmark datasets and observe state-of-the-art performance compared to existing robust photometric stereo methods.