Leveraging on recent advances in robust matrix decomposition, we revisit Lambertian photometric stereo as a robust low-rank matrix recovery problem with both missing and corrupted entries, tailoring Grasta and R-GoDec to normal surface estimation. A method to automatically detect shadows is proposed. The performance of different robust matrix completion techniques are analyzed on the challenging DiLiGenT datasets.
A Matrix Decomposition Perspective on Calibrated Photometric Stereo
MAGRI, LUCA
;Toldo, Roberto;Castellani, Umberto;
2017-01-01
Abstract
Leveraging on recent advances in robust matrix decomposition, we revisit Lambertian photometric stereo as a robust low-rank matrix recovery problem with both missing and corrupted entries, tailoring Grasta and R-GoDec to normal surface estimation. A method to automatically detect shadows is proposed. The performance of different robust matrix completion techniques are analyzed on the challenging DiLiGenT datasets.
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