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3D shapes provide substantially more information than 2D images. However, theacquisition of 3D shapes is sometimes very difficult or even impossible incomparison with acquiring 2D images, making it necessary to derive the 3D shapefrom 2D images. Although this is, in general, a mathematically ill-posedproblem, it might be solved by constraining the problem formulation using priorinformation. Here, we present a new approach based on Kendall's shape space toreconstruct 3D shapes from single monocular 2D images. The work is motivated byan application to study the feeding behavior of the basking shark, anendangered species whose massive size and mobility render 3D shape data nearlyimpossible to obtain, hampering understanding of their feeding behaviors andecology. 2D images of these animals in feeding position, however, are readilyavailable. We compare our approach with state-of-the-art shape-basedapproaches, both on human stick models and on shark head skeletons. Using asmall set of training shapes, we show that the Kendall shape space approach issubstantially more robust than previous methods and results in plausibleshapes. This is essential for the motivating application in which specimens arerare and therefore only few training shapes are available.

A Kendall Shape Space Approach to 3D Shape Estimation from 2D Landmarks