Abstract

Acquiring a high-accuracy three-dimensional (3D) shape of a large-scale object from multiple uncalibrated camera views remains a big challenge, since a considerable number of images is required to cover the entire surface; the use of multiple images could, however, result in accumulative errors from each processed image. Here error propagation rules in the 3D reconstruction process have been deduced on the basis of the traditional dual-view reconstruction method. We propose a method that can control the accumulative errors by reducing the times of coordinate transformation with common-view-based dual-view reconstruction. This method involves constructing an image network composed of many image groups, each of which contains a common view. A baseline threshold method is introduced to construct a high-quality image network, and the sums or reprojection residual of all the common points is proposed to assess the validity of the solutions of the orientation. Experiments carried out with both synthetic and real images demonstrate that the proposed method can handle the accumulative error problem with robust and highly accurate results.

© 2011 Optical Society of America

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