research-article

High Performance Photogrammetry for Academic Research

Authors:

Guangchen Ruan,

William ShermanAuthors Info & Claims

PEARC '18: Proceedings of the Practice and Experience on Advanced Research Computing: Seamless Creativity

Article No.: 45, Pages 1 - 8

https://doi.org/10.1145/3219104.3219148

Published: 22 July 2018 Publication History

Abstract

Photogrammetry is the process of computationally extracting a three-dimensional surface model from a set of two-dimensional photographs of an object or environment. It is used to build models of everything from terrains to statues to ancient artifacts. In the past, the computational process was done on powerful PCs and could take weeks for large datasets. Even relatively small objects often required many hours of compute time to stitch together. With the availability of parallel processing options in the latest release of state-of-the-art photogrammetry software, it is possible to leverage the power of high performance computing systems on large datasets. In this paper we present a particular implementation of a high performance photogrammetry service. Though the service is currently based on a specific software package (Agisoft's PhotoScan), our system architecture is designed around a general photogrammetry process that can be easily adapted to leverage other photogrammetry tools. In addition, we report on an extensive performance study that measured the relative impacts of dataset size, software quality settings, and processing cluster size. Furthermore, we share lessons learned that are useful to system administrators looking to establish a similar service, and we describe the user-facing support components that are crucial for the success of the service.

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Cited By

Kolarek BGamulin LDavidović D(2024)Cultural Heritage on HPC - Creating High Resolution 3D Models Using Photogrammetry2024 47th MIPRO ICT and Electronics Convention (MIPRO)10.1109/MIPRO60963.2024.10569784(1121-1127)Online publication date: 20-May-2024
https://doi.org/10.1109/MIPRO60963.2024.10569784
Vilchis CPerez-Guerrero CMendez-Ruiz MGonzalez-Mendoza M(2023)A survey on the pipeline evolution of facial capture and tracking for digital humansMultimedia Systems10.1007/s00530-023-01081-229:4(1917-1940)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s00530-023-01081-2
Bhatt MKalyanam RNishida GHe LMay CNiyogi DAliaga D(2020)Design and Deployment of Photo2Building: A Cloud-based Procedural Modeling Tool as a ServicePractice and Experience in Advanced Research Computing 2020: Catch the Wave10.1145/3311790.3396670(132-138)Online publication date: 26-Jul-2020
https://dl.acm.org/doi/10.1145/3311790.3396670
Show More Cited By

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High Performance Photogrammetry for Academic Research

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cover image ACM Other conferences

PEARC '18: Proceedings of the Practice and Experience on Advanced Research Computing: Seamless Creativity

July 2018

652 pages

ISBN:9781450364461

DOI:10.1145/3219104

General Chair:
Sergiu Sanielevici
Pittsburgh Supercomputing Center

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 July 2018

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PEARC '18

PEARC '18: Practice and Experience in Advanced Research Computing

July 22 - 26, 2018

PA, Pittsburgh, USA

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PEARC '18 Paper Acceptance Rate 79 of 123 submissions, 64%;

Overall Acceptance Rate 133 of 202 submissions, 66%

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Cited By

Kolarek BGamulin LDavidović D(2024)Cultural Heritage on HPC - Creating High Resolution 3D Models Using Photogrammetry2024 47th MIPRO ICT and Electronics Convention (MIPRO)10.1109/MIPRO60963.2024.10569784(1121-1127)Online publication date: 20-May-2024
https://doi.org/10.1109/MIPRO60963.2024.10569784
Vilchis CPerez-Guerrero CMendez-Ruiz MGonzalez-Mendoza M(2023)A survey on the pipeline evolution of facial capture and tracking for digital humansMultimedia Systems10.1007/s00530-023-01081-229:4(1917-1940)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s00530-023-01081-2
Bhatt MKalyanam RNishida GHe LMay CNiyogi DAliaga D(2020)Design and Deployment of Photo2Building: A Cloud-based Procedural Modeling Tool as a ServicePractice and Experience in Advanced Research Computing 2020: Catch the Wave10.1145/3311790.3396670(132-138)Online publication date: 26-Jul-2020
https://dl.acm.org/doi/10.1145/3311790.3396670
Ruan GZhang HTuna EWernert E(2020)Service Provisioning through High Level, Complexity Hiding Interfaces2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9377922(3288-3294)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9377922

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