Non-rigid Reconstruction of Casting Process with Temperature Feature

Non-rigid Reconstruction of Casting Process with Temperature Feature Off-line reconstruction of rigid scene has made a great progress in the past decade. However, the on-line reconstruction of non-rigid scene is still a very challenging task. The casting process is a non-rigid reconstruction problem, it is a high-dynamic molding process lacking of geometric features. In order to reconstruct the casting process robustly, an on-line fusion strategy is proposed for dynamic reconstruction of casting process. Firstly, the geometric and flowing feature of casting are parameterized in manner of TSDF (truncated signed distance field) which is a volumetric block, parameterized casting guarantees real-time tracking and optimal deformation of casting process. Secondly, data structure of the volume grid is extended to have temperature value, the temperature interpolation function is build to generate the temperature of each voxel. This data structure allows for dynamic tracking of temperature of casting during deformation stages. Then, the sparse RGB features is extracted from casting scene to search correspondence between geometric representation and depth constraint. The extracted color data guarantees robust tracking of flowing motion of casting. Finally, the optimal deformation of the target space is transformed into a nonlinear regular variational optimization problem. This optimization step achieves smooth and optimal deformation of casting process. The experimental results show that the proposed method can reconstruct the casting process robustly and reduce drift in the process of non-rigid reconstruction of casting. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png 3D Research Springer Journals

Non-rigid Reconstruction of Casting Process with Temperature Feature

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Publisher
3D Display Research Center
Copyright
Copyright © 2017 by 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany
Subject
Engineering; Signal,Image and Speech Processing; Computer Imaging, Vision, Pattern Recognition and Graphics; Optics, Lasers, Photonics, Optical Devices
eISSN
2092-6731
D.O.I.
10.1007/s13319-017-0143-x
Publisher site
See Article on Publisher Site

Abstract

Off-line reconstruction of rigid scene has made a great progress in the past decade. However, the on-line reconstruction of non-rigid scene is still a very challenging task. The casting process is a non-rigid reconstruction problem, it is a high-dynamic molding process lacking of geometric features. In order to reconstruct the casting process robustly, an on-line fusion strategy is proposed for dynamic reconstruction of casting process. Firstly, the geometric and flowing feature of casting are parameterized in manner of TSDF (truncated signed distance field) which is a volumetric block, parameterized casting guarantees real-time tracking and optimal deformation of casting process. Secondly, data structure of the volume grid is extended to have temperature value, the temperature interpolation function is build to generate the temperature of each voxel. This data structure allows for dynamic tracking of temperature of casting during deformation stages. Then, the sparse RGB features is extracted from casting scene to search correspondence between geometric representation and depth constraint. The extracted color data guarantees robust tracking of flowing motion of casting. Finally, the optimal deformation of the target space is transformed into a nonlinear regular variational optimization problem. This optimization step achieves smooth and optimal deformation of casting process. The experimental results show that the proposed method can reconstruct the casting process robustly and reduce drift in the process of non-rigid reconstruction of casting.

Journal

3D ResearchSpringer Journals

Published: Aug 21, 2017

References

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