Robust Surface Reconstruction from Gradients via Adaptive Dictionary Regularization

Autor:	Wagenmaker, Andrew J., Moore, Brian E., Nadakuditi, Raj Rao
Rok vydání:	2017
Předmět:	Computer Science - Computer Vision and Pattern Recognition Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: ICIP 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1710.00230 Zobrazit plný text záznamu View this record from Arxiv