Abstract
This paper presents a novel PDE-based method for floating-point disparity estimation which produces smooth disparity fields with sharp object boundaries for surface reconstruction. In order to avoid the over-segmentation problem of image-driven structure tensor and the blurred boundary problem of field-driven tensor, we propose a new anisotropic diffusivity function controlled by image and disparity gradients. We also embed a bi-directional disparity matching term to control the data term in occluded regions. We evaluate the proposed method on data sets from the Middlebury benchmarking site and real data sets with ground-truth models scanned by a LIDAR sensor.