Teresa C. S. Azevedo

3D Object Reconstruction from Uncalibrated Images Using an Off-the-Shelf Camera

Three-dimensional (3D) objects reconstruction using just bi-dimensional (2D) images has been a major research topic in Computer Vision. However, it is still a hard problem to address, when automation, speed and precision are required and/or the objects have complex shapes or image properties. In this paper, we compare two Active Computer Vision methods frequently used for the 3D reconstruction of objects from image sequences, acquired with a single off-the-shelf CCD camera: Structure From Motion (SFM) and Generalized Voxel Coloring (GVC). SFM recovers the 3D shape of an object based on the relative motion involved, while VC is a volumetric method that uses photo-consistency measures to build the required 3D model. Both methods considered do not impose any kind of restrictions on the relative motion involved.

Three-dimensional reconstruction and characterization of human external shapes from two-dimensional images using volumetric methods

This work presents a volumetric approach to reconstruct and characterise 3D models of external anatomical structures from 2D images. Volumetric methods represent the final volume using a finite set of 3D geometric primitives, usually designed as voxels. Thus, from an image sequence acquired around the object to reconstruct, the images are calibrated and the 3D models of the referred object are built using different approaches of volumetric methods. The final goal is to analyse the accuracy of the obtained models when modifying some of the parameters of the considered volumetric methods, such as the type of voxel projection (rectangular or accurate), the way the consistency of the voxels is tested (only silhouettes or silhouettes and photo-consistency) and the initial size of the reconstructed volume.