Experimental comparison of superquadric fitting objective functions
Pattern Recognition Letters
| Hi-index | 0.00 |
This paper presents a new framework for recovering superquadrics with global deformations from multi-view real range data. The framework aims at improving confidence and accuracy of recovered models by utilizing multi-view information, and consists of the initial superquadricmodel recovery, view registration, view integration, and final model recovery from integrated data. A quadrant analysis technique is proposed to aid the recovery of bending superquadrics. A modified range data registration method based on recovered superquadrics is also proposed to handle tapered superquadrics. Experimental results indicate the proposed framework of multi-view representation significantly improved the accuracy and confidence of recoveredsuperquadrics compared with existing recovery strategies which rely on single-view range images.