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Journal of Visualization

, Volume 22, Issue 5, pp 991–1003 | Cite as

Deep learning-based viewpoint recommendation in volume visualization

  • Changhe Yang
  • Yanda Li
  • Can Liu
  • Xiaoru YuanEmail author
Regular Paper
  • 76 Downloads

Abstract

Viewpoint is vital in guiding the user to understand the volume data. However, a model that can recommend viewpoints conforming to user preference is hard to be represented explicitly. In this work, we propose an implicit model for the best viewpoint recommendation of volume visualization with CNN-based models to learn the traditional scoring method and user preference. Residual structures are applied for reducing overfitting in simple scalar regression and solving the problem of accuracy getting lower as the network getting deeper. Multi-level-based structures are applied to imitate the coarse and fine level in human perception. The detailed experiments of comparison between our model and traditional methods confirm the efficiency of our work. A case of application verifies that our model can flexibly realize a user preference-based best viewpoint selection in volume visualization.

Graphic abstract

Keywords

Scientific visualization Machine learning Computing methodologies 

Notes

Acknowledgements

This work is supported by the National Key Research and Development Program of China (2016QY02D0304), NSFC No. 61672055, and the National Program on Key Basic Research Project (973 Program) No. 2015CB352503.

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Copyright information

© The Visualization Society of Japan 2019

Authors and Affiliations

  1. 1.Key Laboratory of Machine Perception (Ministry of Education), and School of EECSPeking UniversityBeijingChina
  2. 2.National Engineering Laboratory for Big Data Analysis and ApplicationPeking UniversityBeijingChina

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