A quality controllable multi-view object reconstruction method for 3D imaging systems

Abstract

This paper addresses a novel multi-view visual hull mesh reconstruction for 3D imaging with a system quality control capability. There are numerous 3D imaging methods including multi-view stereo algorithms and various visual hull/octree reconstruction methods known as modeling from silhouettes. The octree based reconstruction methods are conceptually simple to implement, while encountering a conflict between model accuracy and memory size. Since the tree depth is discrete, the system performance measures (in terms of accuracy, memory size, and computation time) are generally varying rapidly with the pre-specified tree depth. This jumping system performance is not suitable for practical applications; a desirable 3D reconstruction method must have a finer control over the system performance. The proposed method aims at the visual quality control along with better management of memory size and computation time. Furthermore, dynamic object modeling is made possible by the new method. Also, progressive transmission of the reconstructed model from coarse to fine is provided. The reconstruction accuracy of the 3D model acquired is measured by the exclusive OR (XOR) projection error between the pairs of binary images: the reconstructed silhouettes and the true silhouettes in the multiple views. Interesting properties of the new method and experimental comparisons with other existing methods are reported. The performance comparisons are made under either a comparable silhouette inconsistency or a similar triangle number of the mesh model. It is shown that under either condition the new method requires less memory size and less computation time.