Name
Abstract | ||
|---|---|---|
We propose to fit automatically a 3D morphable face model to a point cloud captured with a RGB-D sensor. Both data sets, the shape model and the target point cloud are modelled as two probability density functions (pdfs). Rigid registration (rotation and translation) and reconstruction on the model is performed by minimising the Euclidean distance between these two pdfs augmented with a multivariate Gaussian prior. Our resulting process is robust and it does not require point to point correspondence. Experimental results on synthetic and real data illustrates the performance of this novel approach. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2534008.2534013 | CVMP |
Keywords | Field | DocType |
target point cloud,rgb-d sensor,morphable face model,euclidean distance,multivariate gaussian,robust bayesian fitting,morphable model,shape model,novel approach,divergence,computer vision | Computer vision,Data set,Pattern recognition,Computer science,Euclidean distance,Multivariate normal distribution,RGB color model,Artificial intelligence,Point-to-point,Point cloud,Probability density function,Bayesian probability | Conference |
Citations | PageRank | References |
1 | 0.35 | 38 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudia Arellano | 1 | 9 | 1.82 |
| Rozenn Dahyot | 2 | 340 | 32.62 |