We formulate stereo matching as an extremal surface extraction problem. This is made possible by embedding the disparity surface inside a volume where the surface is composed of voxels with locally maximal similarity values. This formulation naturally implements the coherence principle, and allows us to incorporate most known global constraints. Time efficiency is achieved by executing the algorithm in a coarse-to-fine fashion, and only populating the full volume at the coarsest level. To make the system more practical, we present a rectification algorithm based on the fundamental matrix, avoiding full camera calibration. We present results on standard stereo pairs, and on our own data set. The results are qualitatively evaluated in terms of both the generated disparity maps and the 3-D models.

We propose an efficient solution to the general M-view projective reconstruction problem, using matrix factorization and iterative least squares. The method can accept input with missing data, meaning that not all points are necessarily visible in all views. It runs much faster than the often-used non-linear minimization method, while preserving the accuracy of the latter. The key idea is to convert the minimization problem into a series of weighted least squares sub-problems with drastically reduced matrix sizes. Additionally, we show that good initial values can always be obtained. Experimental results on both synthetic and real data are presented. Potential applications are also demonstrated.

We present a practical technique for building 3-D human face models from two photographs. Rather than using expensive 3-D scanners, we show that frontal face models can be faithfully reconstructed with unsophisticated digital cameras in a totally non-invasive setup. We propose a rectification algorithm based on the fundamental matrix by computing the dual of the point transformation matrix. The image matching problem is converted into a maximal surface extraction problem which is then solved efficiently. Finally, the Euclidean approximation is achieved with the help of a novel factorization method for perspective cameras. Two examples are presented.

The authors present an image synthesis methodology and a system built around it. Given a sparse set of photographs taken from unknown viewpoints, the system generates images from new, different viewpoints with correct perspective, and handles occlusion. It achieves this without requiring any knowledge about the 3D structure of the scene nor the intrinsic camera parameters. The photo-realistic rendering process is polygon based and can be potentially implemented as real time texture mapping. The system is robust to noise by taking advantage of duplicate information from multiple views. They present results on several example scenes.