Following Sugihara, we represent the geometric constraints imposed by the line-drawing of a polyhedron as a set of linear equalities and inequalities. Unlike him, we explicitly take into account the uncertainty in vertex position. This allows us to circumvent the superstrictness of the constraints without deleting any of them. For a given error bound, deciding whether a line-drawing is the correct projection of a polyhedron is reduced to linear programming, and SD shape recovery is reduced to optimization under linear constraints. Our method can be used for recovering the shape of polyhedral objects whose reflectance can be modelled accurately. We have implemented it for the Lambertian model and the Lambertian model with interreflections. We present results obtained using real images.
|Title of host publication||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Publisher||Publ by IEEE|
|Number of pages||5|
|ISBN (Print)||0818658274, 9780818658273|
|State||Published - 1994|
|Event||Proceedings of the 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Seattle, WA, USA|
Duration: 21 Jun 1994 → 23 Jun 1994
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||Proceedings of the 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|City||Seattle, WA, USA|
|Period||21/06/94 → 23/06/94|
Bibliographical noteFunding Information:
This work was supported in part by the Beckman Institute and the Center for Advanced Study of the University of Illinois at Urbana-Champaign, by the National Science Foundation under Grant IRI-9224815, and by the National Aeronautics and Space Administration under Grant NAG 1-613.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition