Correspondence chaining for enhanced dense 3D reconstruction

Within the computer vision community, the reconstruction of rigid 3D objects is a well known task in current research. Many existing algorithms provide a dense 3D reconstruction of a rigid object from sequences of 2D images. Commonly, an iterative registration approach is applied for these images, relying on pairwise dense matches between images, which are then triangulated. To minimize redundant and imprecisely reconstructed 3D points, we present and evaluate a new approach, called Correspondence Chaining, to fuse existing dense twoview 3D reconstruction algorithms to a multi-view reconstruction, where each 3D point is estimated from multiple images. This leads to an enhanced precision and reduced redundancy. The algorithm is evaluated with three different representative datasets. With Correspondence Chaining the mean error of the reconstructed pointclouds related to ground truth data, acquired with a laser scanner, can be reduced by up to 40%, whereas the root mean square error is even reduced by up to 56%. The reconstructed 3D models contain much less 3D points, while keeping details like fine structures, the file size is reduced by up to 78% and the computation time of the involved parts is decreased by up to 42%.