Resampling Structure from Motion View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

David Hutchison , Takeo Kanade , Josef Kittler , Jon M. Kleinberg , Friedemann Mattern , John C. Mitchell , Moni Naor , Oscar Nierstrasz , C. Pandu Rangan , Bernhard Steffen , Madhu Sudan , Demetri Terzopoulos , Doug Tygar , Moshe Y. Vardi , Gerhard Weikum , Tian Fang , Long Quan

ABSTRACT

This paper proposes a hierarchical framework that resamples 3D reconstructed points to reduce computation cost on time and memory for very large-scale Structure from Motion. The goal is to maintain accuracy and stability similar for different resample rates. We consider this problem in a level-of-detail perspective, from a very large scale global and sparse bundle adjustment to a very detailed and local dense optimization. The dense matching are resampled by exploring the redundancy using local invariant properties, while 3D points are resampled by exploring the redundancy using their covariance and their distribution in both 3D and image space. Detailed experiments on our resample framework are provided. We also demonstrate the proposed framework on large-scale examples. The results show that the proposed resample scheme can produce a 3D reconstruction with the stability similar to quasi dense methods, while the problem size is as neat as sparse methods. More... »

PAGES

1-14

References to SciGraph publications

  • 2000. Reconstruction from Uncalibrated Sequences with a Hierarchy of Trifocal Tensors in COMPUTER VISION - ECCV 2000
  • 2002-04-12. Bundle Adjustment — A Modern Synthesis in VISION ALGORITHMS: THEORY AND PRACTICE
  • 2001. Frame Decimation for Structure and Motion in 3D STRUCTURE FROM IMAGES — SMILE 2000
  • 2008-07. Detailed Real-Time Urban 3D Reconstruction from Video in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Book

    TITLE

    Computer Vision – ECCV 2010

    ISBN

    978-3-642-15551-2
    978-3-642-15552-9

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15552-9_1

    DOI

    http://dx.doi.org/10.1007/978-3-642-15552-9_1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1027488508


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