Acquisition of a Large Pose-Mosaic Dataset

  • Authors:
  • S. Coorg;N. Master;S. Teller

  • Affiliations:
  • -;-;-

  • Venue:
  • CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • Year:
  • 1998

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Abstract

We describe the generation of a large pose-mosaic dataset: a collection of several thousand digital images, grouped by spatial position into spherical mosaics, each annotated with estimates of the acquiring camera's 6 DOF pose (3 DOF position and 3 DOF orientation) in an absolute coordinate system. The pose-mosaic dataset was generated by acquiring images, grouped by spatial position into nodes (essentially, spherical mosaics). A prototype mechanical pan-tilt head was manually deployed to acquire the data. Manual surveying provided initial position estimates for each node. A back-projecting scheme provided initial rotational estimates. Relative rotations within each node, along with internal camera parameters, were refined automatically by an optimization-correlation scheme. Relative translations and rotations among nodes were refined according to point correspondences, generated automatically and by a human operator. The resulting pose-imagery is self-consistent under a variety of evaluation metrics.Pose-mosaics are useful "first-class" data objects, for example in automatic reconstruction of textured 3D CAD models which represent urban exteriors.