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Fast and Seamless Large-scale Aerial 3D Reconstruction using Graph Framework

Published: 24 February 2018 Publication History

Abstract

Large-scale 3D reconstruction for aerial photography is achallenging. For aerial image dataset, large scale means that the amount and resolution of images are enormous, which brings a huge amount of computation in Structure from Motion (SfM) pipeline, especially on the process of feature detection, feature matching and bundle adjustment (BA). In this paper, we present a novel method to solve the large-scale 3D reconstruction in parallel to accelerate the process. It could be generalized as the process of Divide-Reconstruct-Optimize-Fuse. We propose an effective graph-based framework that could robustly conduct aerial images grouping task and optimize parameters to fuse sub-models seamless. Experimental results on large-scale aerial datasets demonstrate the efficiency and robustness of the proposed method.

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Cited By

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  • (2022)Heuristics for optimizing 3D mapping missions over swarm-powered ad-hoc cloudsJournal of Heuristics10.1007/s10732-022-09502-728:4(539-582)Online publication date: 1-Aug-2022
  • (2020)FloorVLoc: A Modular Approach to Floorplan Monocular LocalizationRobotics10.3390/robotics90300699:3(69)Online publication date: 10-Sep-2020
  • (2020)Boundary-Aware 3D Building Reconstruction From a Single Overhead Image2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR42600.2020.00052(438-448)Online publication date: Jun-2020

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  1. Fast and Seamless Large-scale Aerial 3D Reconstruction using Graph Framework

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    cover image ACM Other conferences
    ICIGP '18: Proceedings of the 2018 International Conference on Image and Graphics Processing
    February 2018
    183 pages
    ISBN:9781450363679
    DOI:10.1145/3191442
    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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    • Wuhan Univ.: Wuhan University, China

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 24 February 2018

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    Author Tags

    1. Graph Framework
    2. Large-scale Aerial 3D Reconstruction
    3. Seamless Fusion

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    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • ShenZhen Science and Technology Foundation
    • The National Natural Science Foundation of China

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    ICIGP 2018

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    View all
    • (2022)Heuristics for optimizing 3D mapping missions over swarm-powered ad-hoc cloudsJournal of Heuristics10.1007/s10732-022-09502-728:4(539-582)Online publication date: 1-Aug-2022
    • (2020)FloorVLoc: A Modular Approach to Floorplan Monocular LocalizationRobotics10.3390/robotics90300699:3(69)Online publication date: 10-Sep-2020
    • (2020)Boundary-Aware 3D Building Reconstruction From a Single Overhead Image2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR42600.2020.00052(438-448)Online publication date: Jun-2020

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