3D surface reconstruction from contour line image by a regularization method

Shigeru Muraki; Naokazu Yokoya; Kazuhiko Yamamoto

doi:10.1117/12.2294273

1 August 1990 3D surface reconstruction from contour line image by a regularization method

Shigeru Muraki, Naokazu Yokoya, Kazuhiko Yamamoto

Proceedings Volume 1395, Close-Range Photogrammetry Meets Machine Vision; 13950U (1990) https://doi.org/10.1117/12.2294273
Event: Close-Range Photogrammetry Meets Machine Vision, 1990, Zurich, Switzerland

Abstract

The regularization method, which is performed by minimizing an energy functional of the image, has recently been applied to many ill-posed problems in computer vision. Notably, Grimson(1983) developed a regularization method for surface reconstruction which used a sparse set of known elevation data. We have developed an approach to surface reconstruction using both contour image data and a sparse set of known elevation values. We define a new energy functional which integrates three kinds of constraints : smoothness, fitness, and contour line constraint. These constraints seek to ensure that the reconstructed surface smoothly approximates the known elevation values and has the same height value for all points on a contour line. The energy functional can be minimized by solving a large linear system of simultaneous equations. We have successfully reconstructed a detailed 3D topography by applying this method to contour lines and known sparse elevation data extracted from moire images and topographic maps.

Citation Download Citation

Shigeru Muraki, Naokazu Yokoya, and Kazuhiko Yamamoto "3D surface reconstruction from contour line image by a regularization method", Proc. SPIE 1395, Close-Range Photogrammetry Meets Machine Vision, 13950U (1 August 1990); https://doi.org/10.1117/12.2294273

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