A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Active shape models—their training and application
Computer Vision and Image Understanding
A new Voronoi-based surface reconstruction algorithm
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
The Ball-Pivoting Algorithm for Surface Reconstruction
IEEE Transactions on Visualization and Computer Graphics
Building and Testing a Statistical Shape Model of the Human Ear Canal
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
The space of human body shapes: reconstruction and parameterization from range scans
ACM SIGGRAPH 2003 Papers
Non-rigid registration using distance functions
Computer Vision and Image Understanding - Special issue on nonrigid image registration
SCAPE: shape completion and animation of people
ACM SIGGRAPH 2005 Papers
Anatomically-Aware, Automatic, and Fast Registration of 3D Ear Impression Models
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Geometric modeling in shape space
ACM SIGGRAPH 2007 papers
Journal of Cognitive Neuroscience
Analysis of deformation of the human ear and canal caused by mandibular movement
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
| Hi-index | 0.00 |
3D shape modeling is a crucial component of rapid prototyping systems that customize shapes of implants and prosthetic devices to a patient's anatomy. In this paper, we present a solution to the problem of customized 3D shape modeling using a statistical shape analysis framework. We design a novel method to learn the relationship between two classes of shapes, which are related by certain operations or transformation. The two associated shape classes are represented in a lower dimensional manifold, and the reduced set of parameters obtained in this subspace is utilized in an estimation, which is exemplified by a multivariate regression in this paper. We demonstrate our method with a felicitous application to the estimation of customized hearing aid devices.