Robust Surface Reconstruction from Gradients via Adaptive Dictionary Regularization
This work addresses the problem of reconstructing accurate surfaces from noisy gradients for applications in photometric stereo, presenting an incremental improvement through adaptive regularization.
The paper tackles robust surface reconstruction from noisy photometric stereo normal vector maps by introducing an adaptive dictionary learning approach that integrates gradient fields and sparsely represents surface patches, demonstrating improved performance on synthetic and real data with noise robustness.
This paper introduces a novel approach to robust surface reconstruction from photometric stereo normal vector maps that is particularly well-suited for reconstructing surfaces from noisy gradients. Specifically, we propose an adaptive dictionary learning based approach that attempts to simultaneously integrate the gradient fields while sparsely representing the spatial patches of the reconstructed surface in an adaptive dictionary domain. We show that our formulation learns the underlying structure of the surface, effectively acting as an adaptive regularizer that enforces a smoothness constraint on the reconstructed surface. Our method is general and may be coupled with many existing approaches in the literature to improve the integrity of the reconstructed surfaces. We demonstrate the performance of our method on synthetic data as well as real photometric stereo data and evaluate its robustness to noise.