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Building Rome in a day

Authors:

Sameer Agarwal,

Yasutaka Furukawa,

Steven M. Seitz,

Richard SzeliskiAuthors Info & Claims

Communications of the ACM, Volume 54, Issue 10

Pages 105 - 112

https://doi.org/10.1145/2001269.2001293

Published: 01 October 2011 Publication History

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Abstract

We present a system that can reconstruct 3D geometry from large, unorganized collections of photographs such as those found by searching for a given city (e.g., Rome) on Internet photo-sharing sites. Our system is built on a set of new, distributed computer vision algorithms for image matching and 3D reconstruction, designed to maximize parallelism at each stage of the pipeline and to scale gracefully with both the size of the problem and the amount of available computation. Our experimental results demonstrate that it is now possible to reconstruct city-scale image collections with more than a hundred thousand images in less than a day.

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Cited By

Zhang JWillis A(2024)Bridging Formal Shape Models and Deep Learning: A Novel Fusion for Understanding 3D ObjectsSensors10.3390/s2412387424:12(3874)Online publication date: 15-Jun-2024
https://doi.org/10.3390/s24123874
Cueto Zumaya CCatalano IQueralta J(2024)Building Better Models: Benchmarking Feature Extraction and Matching for Structure from Motion at Construction SitesRemote Sensing10.3390/rs1616297416:16(2974)Online publication date: 14-Aug-2024
https://doi.org/10.3390/rs16162974
Ogawa YNakamura RSato GMaeda HSekimoto Y(2024)End-to-End Framework for the Automatic Matching of Omnidirectional Street Images and Building Data and the Creation of 3D Building ModelsRemote Sensing10.3390/rs1611185816:11(1858)Online publication date: 23-May-2024
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Index Terms

Building Rome in a day
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
  2. Computer graphics
    1. Image manipulation
      1. Image processing
    2. Rendering
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image Communications of the ACM

Communications of the ACM Volume 54, Issue 10

October 2011

126 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/2001269

Issue’s Table of Contents

Copyright © 2011 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: 01 October 2011

Published in CACM Volume 54, Issue 10

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Zhang JWillis A(2024)Bridging Formal Shape Models and Deep Learning: A Novel Fusion for Understanding 3D ObjectsSensors10.3390/s2412387424:12(3874)Online publication date: 15-Jun-2024
https://doi.org/10.3390/s24123874
Cueto Zumaya CCatalano IQueralta J(2024)Building Better Models: Benchmarking Feature Extraction and Matching for Structure from Motion at Construction SitesRemote Sensing10.3390/rs1616297416:16(2974)Online publication date: 14-Aug-2024
https://doi.org/10.3390/rs16162974
Ogawa YNakamura RSato GMaeda HSekimoto Y(2024)End-to-End Framework for the Automatic Matching of Omnidirectional Street Images and Building Data and the Creation of 3D Building ModelsRemote Sensing10.3390/rs1611185816:11(1858)Online publication date: 23-May-2024
https://doi.org/10.3390/rs16111858
Zhang WZhong ZChen HJing N(2024)GeoViewMatch: A Multi-Scale Feature-Matching Network for Cross-View Geo-Localization Using Swin-Transformer and Contrastive LearningRemote Sensing10.3390/rs1604067816:4(678)Online publication date: 14-Feb-2024
https://doi.org/10.3390/rs16040678
Wang Q(2024)Towards Real-Time 3D Terrain Reconstruction from Aerial ImageryGeographies10.3390/geographies40100054:1(66-82)Online publication date: 29-Jan-2024
https://doi.org/10.3390/geographies4010005
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