Robust surface matching by integrating edge segments

This paper describes a novel area-based stereo-matching method which aims at reconstructing the shape of objects robustly, correctly, with high precision and with high density. Our goal is to reconstruct correctly the shape of the object by comprising also edges as part of the resulting surface. For this purpose, we need to overcome the problem of how to reconstruct and describe shapes with steep and sharp edges. Area-based matching methods set an image area as a template and search the corresponding match. As a direct consequence of this approach, it becomes not possible to correctly reconstruct the shape around steep edges. Moreover, in the same regions, discontinuities and discrepancies of the shape between the left and right stereo-images increase the difficulties for the matching process. In order to overcome these problems, we propose in this paper the approach of reconstructing the shape of objects by embedding reliable edge line segments into the area-based matching process with parallax estimation. We propose a robust stereo-matching (the extended Edge TIN-LSM) method which integrates edges and which is able to cope with differences in right and left image shape, brightness changes and occlusions. The method consists of the following three steps: (1) parallax estimation, (2) edge-matching, (3) edge-surface matching. In this paper, we describe and explain in detail the process of parallax estimation and the area-based surface-matching with integrated edges; the performance of the proposed method is also validated. The main advantage of this new method is its ability to reconstruct with high precision a 3D model of an object from only two images (for ex. measurement of a tire with 0.14 mm accuracy), thus without the need of a large number of images. For this reason, this approach is intrinsically simple and high-speed.