Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CNNs based Viewpoint Estimation for Volume Visualization   Viewpoint estimation from 2D rendered images is helpful in understanding how
users select viewpoints for volume visualization and guiding users to select
better viewpoints based on previous visualizations. In this paper, we propose a
viewpoint estimation method based on Convolutional Neural Networks (CNNs) for
volume visualization. We first design an overfit-resistant image rendering
pipeline to generate the training images with accurate viewpoint annotations,
and then train a category-specific viewpoint classification network to estimate
the viewpoint for the given rendered image. Our method can achieve good
performance on images rendered with different transfer functions and rendering
parameters in several categories. We apply our model to recover the viewpoints
of the rendered images in publications, and show how experts look at volumes.
We also introduce a CNN feature-based image similarity measure for similarity
voting based viewpoint selection, which can suggest semantically meaningful
optimal viewpoints for different volumes and transfer functions.

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The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
2
The categories of this paper are: math
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The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
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The categories of this paper are: hep-ph
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The categories of this paper are: q-fin