Disparity Estimation with Modeling of Occlusion and ObjectOrientationAndr

1 ABSTRACT Stereo matching is fundamental to applications such as 3-D visual communications and depth measurements. There are several diierent approaches towards this objective, including feature-based methods, 1,2 block-based methods, 3,4 and pixel-based methods. 5 Most approaches use regularization to obtain reliable elds. Generally speaking, when smoothing is applied to the estimated depth eld, it results in a bias towards surfaces that are parallel to the image plane. This is called fronto-parallel bias. 4 Recent pixel-based approaches 5 claim that no disparity smoothing is necessary. In their approach, occlusions and objects are explicitly modeled. But these models interfere each others in the case of slanted objects and result in a fragmented disparity eld. In this paper we propose a disparity estimation algorithm with explicit modeling of object orientation and occlusion. The algorithm incorporates adjustable resolution and accuracy. Smoothing can be applied without introducing the fronto-parallel bias. The experiments show that the algorithm is very promising. 2 INTRODUCTION The estimation of disparity elds is a diicult problem. The disparity eld between a pair of images depends directly on two things: rst, the distance of the objects to the cameras, and second, the geometry of the stereo cameras. Although there are a number of techniques for disparity/depth estimation, many of them require camera calibration. For applications like multiview image generation, 6 stereo image sequence coding, 7 etc., a 2-D image registration method without camera calibration is preferred. To reduce the complexity of disparity estimation, many researchers assume that the cameras are arranged in parallel-axis conngurations so that the pixels on one scanline in the left image are matched to pixels on the same scanline in the right image. In this case, the stereo matching problem is simpliied to an intra-scanline pixel matching problem. One popular approach for solving this intra-scanline matching is the disparity space technique. 1,8,5 This technique involves the computation of a matching error image (disparity space image), the deenition of a matching path model, and an algorithm for minimal error path search. Previous approaches use a simple path model to reduce the size of the search space. 8 In these methods, diierent constraints are used to resolve the problems of multiple possible paths. For example, in Ref 1], inter-scanline matching is used along with intra-scanline