3D object reconstruction from multiple views using neural networks

Automatic determination of 3D information from digital images is a fundamental problem in digital photogrammetry and computer vision. The most difficult task in the problem is finding conjugate points in images. Despite the wealth information contained in stereo images, factors such as occlusions and discontinuities challenge several algorithms implemented to match conjugate points. On the other hand, multi-image matching has gained considerable attention recently since it accounts for changes in illumination conditions, camera positions, and surrounding features. Currently, a wide variety of techniques have been developed for multi-image matching. However, most of these techniques are based on one of two approaches: grouping outputs from stereo matching or looking for corresponding points in all views. The first approach results in scenarios where several false matches could be grouped. The second approach is limited to cases where points appear in all views with comparable intensity values. This paper introduces an alternative algorithm to match image points across several views. Our approach is based on locating all sets of corresponding points in all image triplets using neural networks then grouping those triplet sets that represent the same ground point. Radiometric and geometric properties are fed into a feedforward neural network designed to detect valid matches from invalid ones in all image triplets simultaneously. The detection rate of the neural network is more than 98 % and the false alarm rate is less than 2 %. Triplet matching sets are then combined if they share the same image point and their image rays have a valid intersection as measured through the colinearity equation. In order to assess the geometrical accuracy of the 3D points, the image coordinates are manually measured in the original images and object coordinates are computed and found comparable to those computed through the proposed algorithm. © Società Italiana di Fotogrammetria e Topografia (SIFET) 2013.