Skip to Main content Skip to Navigation
New interface
Book sections

Statistical Shape Spaces for 3D Data: A Review

Abstract : Methods and systems for capturing 3D geometry are becoming increasingly commonplace–and with them a plethora of 3D data. Much of this data is unfortunately corrupted by noise, missing data, occlusions or other outliers. However, when we are interested in the shape of a particular class of objects, such as human faces or bodies, we can use machine learning techniques, applied to clean, registered databases of these shapes, to make sense of raw 3D point clouds or other data. This has applications ranging from virtual change rooms to motion and gait analysis to surgical planning depending on the type of shape. In this chapter, we give an overview of these techniques, a brief review of the literature, and comparative evaluation of two such shape spaces for human faces.
Complete list of metadata

Cited literature [65 references]  Display  Hide  Download
Contributor : Stefanie Wuhrer Connect in order to contact the contributor
Submitted on : Thursday, February 11, 2016 - 5:44:49 PM
Last modification on : Friday, February 4, 2022 - 3:25:22 AM
Long-term archiving on: : Thursday, May 12, 2016 - 5:32:29 PM


Files produced by the author(s)




Alan Brunton, Augusto Salazar, Timo Bolkart, Stefanie Wuhrer. Statistical Shape Spaces for 3D Data: A Review. Chi Hau Chen. Handbook of Pattern Recognition and Computer Vision 5th Edition, pp.217-238, 2016, 978-981-4656-52-8. ⟨10.1142/9789814656535_0012⟩. ⟨hal-01205998⟩



Record views


Files downloads