How far 3D shapes can be understood from 2D silhouettes

• Published in: IEEE transactions on pattern analysis and machine intelligence 1995-02, Vol.17 (2), p.188-195
• Main Author: Laurentini, A.
• Format: Article
• Language: English
• Subjects: Applied sciences; Approximation algorithms; Artificial intelligence; Computer science; control theory; systems; Computer vision; Exact sciences and technology; Gaussian noise; Image reconstruction; Image sensors; Pattern recognition. Digital image processing. Computational geometry; Publishing; Sensor systems; Shape; Surface reconstruction; Target tracking
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/34.368170

Each 2D silhouette of a 3D unknown object O constrains O inside the volume obtained by back-projecting the silhouette from the corresponding viewpoint. A set of silhouettes specifies a boundary volume R obtained by intersecting the volumes due to each silhouette. R more or less closely approximates O, depending on the viewpoints and the object itself. This approach to the reconstruction of 3D objects is usually referred to as volume intersection. This correspondence addresses the problem of inferring the shape of the unknown object O from the reconstructed object R. For doing this, the author divides the points of the surface of R into hard points, which belong to the surface of any possible object originating R, and soft points, which may or may not belong to O. The author considers two cases: In the first case R is the closest approximation of O which can be obtained from its silhouettes, i.e., its visual hull; in the second case, R is a generic reconstructed object. In both cases the author supplies necessary and sufficient conditions for a point to be hard and gives rules for computing the hard surfaces.< >