An implicit surface polygonizer
Graphics gems IV
Active shape models—their training and application
Computer Vision and Image Understanding
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
A finite element method for surface restoration with smooth boundary conditions
Computer Aided Geometric Design
Spectral surface reconstruction from noisy point clouds
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
A remeshing approach to multiresolution modeling
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Poisson surface reconstruction
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
On geometric variational models for inpainting surface holes
Computer Vision and Image Understanding
A statistical model of head asymmetry in infants with deformational plagiocephaly
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
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
Markov Random Field Surface Reconstruction
IEEE Transactions on Visualization and Computer Graphics
| Hi-index | 0.00 |
We present a method for surface recovery in partial surface scans based on a statistical model. The framework is based on multivariate point prediction, where the distribution of the points are learned from an annotated data set. The training set consist of surfaces with dense correspondence that are Procrustes aligned. The average shape and point covariances can be estimated from this set. It is shown how missing data in a new given shape can be predicted using the learned statistics. The method is evaluated on a data set of 29 scans of ear canal impressions. By using a leave-one-out approach we reconstruct every scan and compute the point-wise prediction error. The evaluation is done for every point on the surface and for varying hole sizes. Compared to state-of-the art surface reconstruction algorithm, the presented methods gives very good prediction results.