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
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Regularized Bundle-Adjustment to Model Heads from Image Sequences without Calibration Data
International Journal of Computer Vision
Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Face Identification by Fitting a 3D Morphable Model Using Linear Shape and Texture Error Functions
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A Feature Registration Framework Using Mixture Models
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Regularized 3D Morphable Models
HLK '03 Proceedings of the First IEEE International Workshop on Higher-Level Knowledge in 3D Modeling and Motion Analysis
Efficient, Robust and Accurate Fitting of a 3D Morphable Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
The Journal of Machine Learning Research
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Morphable Face Reconstruction with Multiple Images
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
A 3D Face Model for Pose and Illumination Invariant Face Recognition
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Self-organizing mixture models
Neurocomputing
Model-based stereo with occlusions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
High resolution passive facial performance capture
ACM SIGGRAPH 2010 papers
Audio query by example using similarity measures between probability density functions of features
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on scalable audio-content analysis
Fusion of range and color images for denoising and resolution enhancement with a non-local filter
Computer Vision and Image Understanding
Morphable Face Reconstruction with Multiple Views
IHMSC '10 Proceedings of the 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 02
Point Set Registration: Coherent Point Drift
IEEE Transactions on Pattern Analysis and Machine Intelligence
Realtime performance-based facial animation
ACM SIGGRAPH 2011 papers
Automatic reconstruction of personalized avatars from 3D face scans
Computer Animation and Virtual Worlds
Robust Point Set Registration Using Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A space-time depth super-resolution scheme for 3D face scanning
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
KinectFusion: Real-time dense surface mapping and tracking
ISMAR '11 Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality
Fast Global Kernel Density Mode Seeking: Applications to Localization and Tracking
IEEE Transactions on Image Processing
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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.