Superquadrics-based 3D object representation of automotive parts utilizing part decomposition

Yan Zhang; Andreas Koschan; Mongi A. Abidi

doi:10.1117/12.515043

1 May 2003 Superquadrics-based 3D object representation of automotive parts utilizing part decomposition

Yan Zhang, Andreas Koschan, Mongi A. Abidi

Proceedings Volume 5132, Sixth International Conference on Quality Control by Artificial Vision; (2003) https://doi.org/10.1117/12.515043
Event: Quality Control by Artificial Vision, 2003, Gatlinburg, TE, United States

Abstract

We present a new superquadrics based object representation strategy for automotive parts in this paper. Starting from a 3D watertight surface model, a part decomposition step is first performed to segment the original multi-part objects into their constituent single parts. Each single part is then represented by a superquadric. The originalities of this approach include first, our approach can represent complicated shapes, e.g., multi-part objects, by utilizing part decomposition as a preprocessing step. Second, superquadrics recovered using our approach have the highest confidence and accuracy due to the 3D watertight surfaces utilized. A novel, generic 3D part decomposition algorithm based on curvature analysis is also proposed in this paper. The proposed part decomposition algorithm is generic and flexible due to the popularity of triangle meshes in the 3D computer community. The proposed algorithms were tested on a large set of 3D data and experimental results are presented. The experimental results demonstrate that our proposed part decomposition algorithm can segment complicated shapes, in our case automotive parts, efficiently into meaningful single parts. And our proposed superquadric representation strategy can then represent each part (if possible) of the complicated objects successfully.

Citation Download Citation

Yan Zhang, Andreas Koschan, and Mongi A. Abidi "Superquadrics-based 3D object representation of automotive parts utilizing part decomposition", Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); https://doi.org/10.1117/12.515043

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