Stereo-Based Three-Dimensional Model Acquisition and Motion Detection.

摘要

Deformable models have a long tradition in computer graphics and computer vision. This thesis looks at the capture of surface deformation based on stereo vision. In recent years, 3D reconstruction and motion detection has attracted great attention. In this thesis a framework for 3D reconstruction from mutli-view images followed by isometry-based motion detection is proposed. For 3D reconstruction, the thesis proposes a multi-view stereo algorithm based on well-known window-based matching combined with fusion of multiple matching results. To improve the matching result, some low-level image processing algorithms, camera calibration and background detection are utilized. For window-based matching, a new hybrid matching method is introduced by combining both, a measure of intensity difference and intensity distribution difference. Multiple MVS pointclouds from different reference views are fused with two new fusion strategies to generate a better final reconstruction. To characterize the performance of our matching method and fusion strategies, an evaluation based on the quality of reconstruction is given in the thesis. Based on 3D pointclouds of object surface obtained with stereo, the deformation of the surface is captured. To generate dense motion vectors over a deformed surface, a simple window-based 3D flow method is applied by using isometry of the observed surface as its primary matching constraint. The method uses feature points as anchoring references of the surface deformation. Given a set of matched features no other intensity information is used and hence the method can tolerate intensity changes over time. The approach is shown to work well on two example scenes which capture non-rigid isometric and general deformations. The thesis also presents experiments demonstrating the stability of the geodesic approximation employed in the isometry-based matching when the 3D pointclouds are sparse.