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Incremental Reconstruction of 3D Scenes from Multiple Complex Images

Published

Author(s)

Martin Herman, T Kanade

Abstract

The 3D Mosaic system is a vision system that incrementally reconstructs complex 3D scenes from a sequence of images obtained from multiple viewpoints. The system encompasses several levels of the vision process, starting with images and ending with symbolic scene descriptions. This paper describes the various components of the system, including stereo analysis, monocular analysis, and constructing and updating the scene model. In addition, the representation of the scene model is described. This model is intended for tasks such as matching, display generation, planning paths through the scene, and making other decisions about the scene environment. Examples showing how the system is used to interpret complex aerial photographs of urban scenes are presented. Each view of the scene, which may be either a single image or a stereo pair, undergoes analysis which results in a 3D wire-frame description that represents portions of edges and vertices of objects. The model is a surface-based description constructed from the wire frames. With each successive view, the model is incrementally updated and gradually becomes more accurate and complete. Task-specific knowledge, involving block-shaped objects in an urban scene, is used to extract the wire frames and construct and update the model.
Proceedings Title
Proceedings of the Third IEEE International Symposium on Intelligent Control
Conference Dates
August 24-26, 1988
Conference Location
Arlington, VA, USA
Conference Title
Third IEEE International Symposium on Intelligent Control

Keywords

3-D Reconstruction

Citation

Herman, M. and Kanade, T. (1986), Incremental Reconstruction of 3D Scenes from Multiple Complex Images, Proceedings of the Third IEEE International Symposium on Intelligent Control, Arlington, VA, USA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=820192 (Accessed February 27, 2024)
Created August 25, 1986, Updated October 12, 2021