Shape reconstruction incorporating multiple nonlinear geometric constraints

This paper deals with the reconstruction of three-dimensional (3D) geometric shapes based on observed noisy 3D measurements and multiple coupled nonlinear shape constraints. Here a shape could be a complete object, a portion of an object, a part of a building etc. The paper suggests a general incremental framework whereby constraints can be added and integrated in the model reconstruction process, resulting in an optimal trade-off between minimization of the shape fitting error and the constraint tolerances. After defining sets of main constraints for objects containing planar and quadric surfaces, the paper shows that our scheme is well behaved and the approach is valid through application on different real parts. This work is the first to give such a large framework for the integration of numerical geometric relationships in object modeling from range data. The technique is expected to have a great impact in reverse engineering applications and manufactured object modeling where the majority of parts are designed with intended feature relationships.