3D Landmark Localization in Point Clouds for the Human Ear

3D landmark localization plays an important role in many aspects of 3D data processing, from morphometric analysis to the initialization of mesh registration algorithms. In this work we address the problem of landmark localization in 3D point clouds by extending leading 2D landmark localization algo...

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Bibliographic Details
Published in:2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) pp. 402 - 406
Main Authors: Sullivan, Eimear O', Zafeiriou, Stefanos
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2020
Subjects:
Ear
Feature extraction
Human ear
Landmark localisation
Location awareness
PCA
Point cloud
Point convolution
Shape
Solid modeling
Three-dimensional displays
Two dimensional displays
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Summary:3D landmark localization plays an important role in many aspects of 3D data processing, from morphometric analysis to the initialization of mesh registration algorithms. In this work we address the problem of landmark localization in 3D point clouds by extending leading 2D landmark localization algorithms to the 3D domain. By leveraging the PointNet++ architecture, we can construct an architecture that is invariant to the ordering of the data. Input point clouds are segmented into background and landmark regions, and offset vectors are calculated within the landmark regions to refine predicted landmark locations. We demonstrate a high landmark localization accuracy, even as the number of points in the input point cloud decreases. By making use of a 3D morphable model as a novel means of data augmentation, improved landmark localization accuracy and consistency can be obtained. We present our results for landmark localization on the human ear.
DOI:10.1109/FG47880.2020.00095