3 Dimensional Shape Recovery from Image Sequences by Non-Linear Optimization

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We have investigated a method for recovering 3 dimensional shapes from image sequence by using non-linear optimization.(1) Improvement of precision for detecting and tracking feature pointsWe have developed a method for detecting and tracking feature points under the affine and depth geometric constraints from the image sequences. First, the affine transform is applied to a pair of images including motion disparity, and then the corresponding points between the images are detected under the affine geometric constraint. After recovering the initial 3D shape, the depth geometric constraint is applied to remove the outlier points and to detect new corresponding points. After several iterations of this procedure, 3D model with 11,277 points was recovered from images of 25 frames in a simulation study, and that with 15,751 points was from 25 frames using real images. These experimental results have shown the effectiveness of our method to recover the 3D dense and precise models from image sequences.(2) Improvement of stability for computing non-linear optimization3D recovery of object shape and camera motion from 2D image sequence is formulated as a non-linear optimization problem, and the initial values are very important to avoid the false solutions called local minima in the search space. We have developed a method to determine the initial values for recovering object whose images are taken by a camera that moves on circular path around the object. We also proposed a procedure to search the optimal solution from the false one. Experimental results with synthetic and real images showed that 3D recovery was stably performed with the initial values given by our method.