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Parallel and scalable heat methods for geodesic distance computation

Tao, Jiong, Zhang, Juyong, Deng, Bailin, Fang, Zheng, Peng, Yue and He, Ying 2019. Parallel and scalable heat methods for geodesic distance computation. IEEE Transactions on Pattern Analysis and Machine Intelligence 10.1109/TPAMI.2019.2933209

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Abstract

In this paper, we propose a parallel and scalable approach for geodesic distance computation on triangle meshes. Our key observation is that the recovery of geodesic distance with the heat method from [Crane et al. 2013] can be reformulated as optimization of its gradients subject to integrability, which can be solved using an efficient first-order method that requires no linear system solving and converges quickly. Afterward, the geodesic distance is efficiently recovered by parallel integration of the optimized gradients in breadth-first order. Moreover, we employ a similar breadth-first strategy to derive a parallel Gauss-Seidel solver for the diffusion step in the heat method. To further lower the memory consumption from gradient optimization on faces, we also propose a formulation that optimizes the projected gradients on edges, which reduces the memory footprint by about 50%. Our approach is trivially parallelizable, with a low memory footprint that grows linearly with respect to the model size. This makes it particularly suitable for handling large models. Experimental results show that it can efficiently compute geodesic distance on meshes with more than 200 million vertices on a desktop PC with 128GB RAM, outperforming the original heat method and other state-of-the-art geodesic distance solvers.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 0162-8828
Date of First Compliant Deposit: 5 August 2019
Date of Acceptance: 31 July 2019
Last Modified: 18 Oct 2019 20:45
URI: http://orca.cf.ac.uk/id/eprint/124675

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