Robust Multiview Reconstruction

I will speak about my past and current research in automatic 3D reconstruction from images. While the two-view camera calibration is a well studied problem, the multiview camera calibration remains a challenging task. It is also the most crucial step in the scene reconstruction as the quality of the resulting dense 3D model is fundamentally limited by precision of the multiview camera calibration. My PhD thesis studies mainly the problem of multiview camera calibration. The largest difficulty of the problem is sparsity of the data which happens when the images are only sparsely captured (so-called wide baseline stereo, WBS). Then, the scene contains many occlusions, i.e. many points are seen in a few images only. The second difficulty of the problem is handling of incorrect correspondences (mismatches), thanks to which also non-existent pair-wise geometries can be found. Every such geometry must be detected and removed to obtain a correct reconstruction. The main contribution of the thesis is a technique for multiview camera calibration by gluing partial reconstructions. The technique works in practical situations, i.e. the perspective camera, many (99.9%) occlusions in scene and a not entirely exact correspondence algorithm. The importance of such technique lies in that it offers united and elegant way of processing correspondences from WBS and sequences. The presented methods exploit all data known about the scene, namely in the same way and at once. The methods are suited for large-scale reconstructions (thousands of images).