Volumetric Stereo And Silhouette Fusion For Image-based Modeling

At present the art of computing complex and high quality 3D models has large and wide applications in computer graphics, medical imaging, 3D animation, electronic games, etc. In practice, image-based modeling technique is an efficient and convenient method to acquire models of real word object. This dissertation presents a novel algorithm for acquiring high quality models from multiple calibrated photographs by stereo and silhouette fusion.The proposed algorithm starts by computing visual hull using a volumetric method in which a novel projection test method is proposed for visual hull octree construction. Then, the depth map of each image is estimated by an expansion-based approach that returns a 3D point cloud with noisy and redundant information. After generating an oriented point cloud from stereo by filtering to reject outlier and reduce scale, and estimating the surface normal for the depth maps, another oriented point cloud from silhouette is added by carving the visual hull octree structure using the point cloud from stereo to restore the textureless and occluded surfaces. Finally, Poisson Surface Reconstruction approach is applied to convert the oriented point cloud both from stereo and silhouette into a complete and accurate triangulated mesh model. The proposed approach has been implemented and the performance of the approach is demonstrated on several real datasets, along with qualitative comparisons with the state-of-the-art image-based modeling techniques by reconstructing the datasets provided by the Middlebury benchmark.