Ruttle, Jonathan and Arellano, Claudia and Dahyot, Rozenn (2014) Robust shape from depth images with GR2T. Pattern Recognition Letters, 50 (1). pp. 43-54. ISSN 0167-8655

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Abstract

This paper proposes to infer accurately a 3D shape of an object captured by a depth camera from multiple view points. The Generalised Relaxed Radon Transform (GR2T) [1] is used here to merge all depth images in a robust kernel density estimate that models the surface of an object in the 3D space. The kernel is tailored to capture the uncertainty associated with each pixel in the depth images. The resulting cost function is suitable for stochastic exploration with gradient ascent algorithms when the noise of the observations is modelled with a differentiable distribution. When merging several depth images captured from several view points, extrinsic camera parameters need to be known accurately, and we extend GR2T to also estimate these nuisance parameters. We illustrate qualitatively the performance of our modelling and we assess quantitatively the accuracy of our 3D shape reconstructions computed from depth images captured with a Kinect camera.

Item Type:	Article
Keywords:	Shape from depth; Generalised Relaxed Radon Transform; GR2T; Noise modelling;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15109
Identification Number:	https://doi.org/10.1016/j.patrec.2014.01.016
Depositing User:	Rozenn Dahyot
Date Deposited:	07 Dec 2021 16:41
Journal or Publication Title:	Pattern Recognition Letters
Publisher:	Elsevier
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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