Terrain Geometry from Monocular Image Sequences

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Terrain reconstruction from images is an ill-posed, yet commonly desired Structure from Motion task when compositing visual effects into live-action photography. These surfaces are required for choreography of a scene, casting physically accurate shadows of CG elements, and occlu- sions. We present a novel framework for generating the geometry of landscapes from extremely noisy point cloud datasets obtained via limited resolution techniques, particularly optical flow based vision algorithms applied to live-action video plates. Our contribution is a new statistical approach to remove erroneous tracks (‘outliers’) by employing a unique combination of well estab- lished techniques—including Gaussian Mixture Models (GMMs) for robust parameter estimation and Radial Basis Functions (RBFs) for scattered data interpolation—to exploit the natural con- straints of this problem. Our algorithm offsets the tremendously laborious task of modeling these landscapes by hand, automatically generating a visually consistent, camera position dependent, thin-shell surface mesh within seconds for a typical tracking shot.
Publisher
Korean Institute of Information Scientists and Engineers
Issue Date
2008-03
Language
Korean
Citation

JOURNAL OF COMPUTING SCIENCE AND ENGINEERING, v.2, no.1, pp.98 - 108

ISSN
1976-4677
URI
http://hdl.handle.net/10203/23372
Appears in Collection
GCT-Journal Papers(저널논문)
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