A Machine-Learning Approach to Keypoint Detection and Landmarking on 3D Meshes

Abstract

We address the problem of automatically detecting a sparse set of 3D mesh vertices, likely to be good candidates for determining correspondences, even on soft organic objects. We focus on 3D face scans, on which single local shape descriptor responses are known to be weak, sparse or noisy. Our machine-learning approach consists of computing feature vectors containing $$D$$ different local surface descriptors. These vectors are normalized with respect to the learned distribution of those descriptors for some given target shape (landmark) of interest. Then, an optimal function of this vector is extracted that best separates this particular target shape from its surrounding region within the set of training data. We investigate two alternatives for this optimal function: a linear method, namely Linear Discriminant Analysis, and a non-linear method, namely AdaBoost. We evaluate our approach by landmarking 3D face scans in the FRGC v2 and Bosphorus 3D face datasets. Our system achieves state-of-the-art performance while being highly generic.

DOI: 10.1007/s11263-012-0605-9

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@article{Creusot2012AMA, title={A Machine-Learning Approach to Keypoint Detection and Landmarking on 3D Meshes}, author={Clement Creusot and Nick Pears and Jim Austin}, journal={International Journal of Computer Vision}, year={2012}, volume={102}, pages={146-179} }