A bundled-optimization model of multiview dense depth map synthesis for dynamic scene reconstruction

Hybrid profilometry using a single monochromatic multi-frequency pattern

In this paper, we propose a novel profilometry scheme to acquire high quality depth, where only a single shot of a monochromatic pattern is utilized. We design a band-wise pattern consisting of fringe bands spatially modulated with coprime periods. After that, with the designed pattern, depth is obtained in a hybrid manner, where both phase-based profilometry and active stereo are incorporated. To be specific, pixels in smooth regions obtain their depth values through phases analysis. Especially, based on depth smooth property, we propose a novel phase unwrapping algorithm, which avoids the problem of error propagation and yields accurate unwrapping phases. On the other hand, for boundary regions, spatial stereo, which is more robust to depth discontinuities, is utilized to modify incorrect depth values. Both theoretical verification and experimental results demonstrate that the proposed scheme can generate high quality depth maps, even for complex scenes and isolated objects.

Light Field Imaging Based Accurate Image Specular Highlight Removal

Specular reflection removal is indispensable to many computer vision tasks. However, most existing methods fail or degrade in complex real scenarios for their individual drawbacks. Benefiting from the light field imaging technology, this paper proposes a novel and accurate approach to remove specularity and improve image quality. We first capture images with specularity by the light field camera (Lytro ILLUM). After accurately estimating the image depth, a simple and concise threshold strategy is adopted to cluster the specular pixels into “unsaturated” and “saturated” category. Finally, a color variance analysis of multiple views and a local color refinement are individually conducted on the two categories to recover diffuse color information. Experimental evaluation by comparison with existed methods based on our light field dataset together with Stanford light field archive verifies the effectiveness of our proposed algorithm.