Robust Photometric Stereo via Dictionary Learning

TL;DR

This paper introduces a dictionary learning-based approach to photometric stereo that enhances robustness in normal vector reconstruction, especially for objects with complex reflectance, outperforming existing methods on synthetic and real datasets.

Contribution

The paper proposes two novel dictionary learning formulations for robust photometric stereo, including handling non-Lambertian reflectance, which improves reconstruction accuracy over prior techniques.

Findings

Achieves state-of-the-art results on benchmark datasets.

Effectively models non-Lambertian reflectance.

Demonstrates robustness to image non-idealities.

Abstract

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.