Object reconstruction from slices for vision

Abstract

A method for reconstructing 3D models from cross-sectional signals is presented. This is specifically useful for low-resolution images (e.g. seismic or ultrasound images). The method is not voxel-based, and is faster and uses less memory than methods that are. The algorithm starts by pre-processing the images by thresholding, segmenting, contour finding, point sampling and then triangulating. The main objective of this algorithm is to produce the smoothest possible model for branching bodies, taking into consideration the fact that the slices might be far apart in reality, and consequently a novel branching/interpolation technique is used that proved superior to existing methods for this purpose. At the same time, it did not need the involvement of the user for objects within defined classes. Seismic simulations and CT data have been used to test the algorithm. We succeeded in reconstructing the tomb of the Egyptian Pharaoh Tutankhamen from simulated seismic data.