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4D Match Trees for Non-rigid Surface Alignment

Mustafa, Armin, Kim, Hansung and Hilton, Adrian (2016) 4D Match Trees for Non-rigid Surface Alignment In: ECCV'16 The 14th European Conference on Computer Vision, 2016-10-11 - 2016-10-14, Amsterdam, The Netherlands.

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This paper presents a method for dense 4D temporal alignment of partial reconstructions of non-rigid surfaces observed from single or multiple moving cameras of complex scenes. 4D Match Trees are introduced for robust global alignment of non-rigid shape based on the similarity between images across sequences and views. Wide-timeframe sparse correspondence between arbitrary pairs of images is established using a segmentation-based feature detector (SFD) which is demonstrated to give improved matching of non-rigid shape. Sparse SFD correspondence allows the similarity between any pair of image frames to be estimated for moving cameras and multiple views. This enables the 4D Match Tree to be constructed which minimises the observed change in non-rigid shape for global alignment across all images. Dense 4D temporal correspondence across all frames is then estimated by traversing the 4D Match tree using optical flow initialised from the sparse feature matches. The approach is evaluated on single and multiple view images sequences for alignment of partial surface reconstructions of dynamic objects in complex indoor and outdoor scenes to obtain a temporally consistent 4D representation. Comparison to previous 2D and 3D scene flow demonstrates that 4D Match Trees achieve reduced errors due to drift and improved robustness to large non-rigid deformations.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Date : 17 September 2016
DOI : 10.1007/978-3-319-46448-0_13
Copyright Disclaimer : The final publication is available at Springer via
Contributors :
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Depositing User : Symplectic Elements
Date Deposited : 12 Oct 2016 14:17
Last Modified : 16 Jan 2019 17:08

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