title: Accurate estimation of the boundaries of a structured light pattern.
abstract: Depth recovery based on structured light using stripe patterns, especially for a region-based codec, demands accurate estimation of the true boundary of a light pattern captured on a camera image. This is because the accuracy of the estimated boundary has a direct impact on the accuracy of the depth recovery. However, recovering the true boundary of a light pattern is considered difficult due to the deformation incurred primarily by the texture-induced variation of the light reflectance at surface locales. Especially for heavily textured surfaces, the deformation of pattern boundaries becomes rather severe. We present here a novel (to the best of our knowledge) method to estimate the true boundaries of a light pattern that are severely deformed due to the heavy textures involved. First, a general formula that models the deformation of the projected light pattern at the imaging end is presented, taking into account not only the light reflectance variation but also the blurring along the optical passages. The local reflectance indices are then estimated by applying the model to two specially chosen reference projections, all-bright and all-dark. The estimated reflectance indices are to transform the edge-deformed, captured pattern signal into the edge-corrected, canonical pattern signal. A canonical pattern implies the virtual pattern that would have resulted if there were neither the reflectance variation nor the blurring in imaging optics. Finally, we estimate the boundaries of a light pattern by intersecting the canonical form of a light pattern with that of its inverse pattern. The experimental results show that the proposed method results in significant improvements in the accuracy of the estimated boundaries under various adverse conditions.
authors.affiliation: Intelligent Systems Research Center, Sungkyunkwan University, Suwon, South Korea. lsh@ece.skku.ac.kr
authors.forename: Sukhan
authors.initials: S
authors.lastname: Lee
authors.forename: Lam Quang
authors.initials: LQ
authors.lastname: Bui