Three-dimensional modeling from two-dimensional video

This paper presents the surface-based factorization method to recover three-dimensional (3-D) structure, i.e., the 3-D shape and 3-D motion, of a rigid object from a two-dimensional (2-D) video sequence. The main ingredients of our approach are as follows: 1) we describe the unknown shape of the 3-D rigid object by polynomial patches; 2) projections of these patches in the image plane move according to parametric 2-D motion models; 3) we recover the parameters describing the 3-D shape and 3-D motion from the 2-D motion parameters by factorizing a matrix that is rank 1 in a noiseless situation. Our method is simultaneously an extension and a simplification of the original factorization method of Tomasi and Kanade (1992). We track regions where the 2-D motion in the image plane is described by a single set of parameters, avoiding the need to track a large number of pointwise features, in general, a difficult task. Then our method estimates the parameters describing the 3-D structure by factoring a rank 1 matrix, not rank 3 as in Tomasi and Kanade. This allows the use of fast iterative algorithms to compute the 3-D structure that best fits the data. Experimental results with real-life video sequences illustrate the good performance of our approach.