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A divide-and-conquer approach to large scene reconstruction with interactive scene analysis and segmentation

Published:17 November 2013Publication History

ABSTRACT

3D reconstruction of real world objects is a fundamental problem in computer vision and computer graphics. It is a challenge for high fidelity reconstruction with consumer-grade depth camera, e.g. Microsoft Kinect on large scale scene. A divide-and-conquer approach could both preserve local geometry details of scene objects and global consistency of the whole scene, thus is superior to existing GPU based real-time/global optimization based offline methods. In this paper, we extend the state-of-the-art in two folds. First, more fine grit scene segmentation is conducted for better local details preservation. Second, a scene analysis procedure is proposed for more reasonable global error spread. Preliminary experiments show that our method achieves more convincing reconstruction results.

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  1. A divide-and-conquer approach to large scene reconstruction with interactive scene analysis and segmentation

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      • Published in

        cover image ACM Conferences
        VRCAI '13: Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
        November 2013
        325 pages
        ISBN:9781450325905
        DOI:10.1145/2534329

        Copyright © 2013 Owner/Author

        Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 17 November 2013

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        • research-article

        Acceptance Rates

        VRCAI '13 Paper Acceptance Rate35of75submissions,47%Overall Acceptance Rate51of107submissions,48%

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