Twinenet: coupling features for synthesizing volume rendered images via convolutional encoder–decoders and multilayer perceptrons
Author:
Funder
Basic and Applied Basic Research Foundation of Guangdong Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-024-03368-5.pdf
Reference63 articles.
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2. Wang, C., Han, J.: DL4SciVis: a state-of-the-art survey on deep learning for scientific visualization. IEEE Trans. Vis. Comput. Graph. (2022). https://doi.org/10.1109/TVCG.2022.3167896
3. Wang, Q., Chen, Z., Wang, Y., Qu, H.: A survey on ML4VIS: applying machine learning advances to data visualization. IEEE Trans. Vis. Comput. Graph. 28(12), 5134–5153 (2022). https://doi.org/10.1109/TVCG.2021.3106142
4. Strengert, M., Klein, T., Botchen, R., Stegmaier, S., Chen, M., Ertl, T.: Spectral volume rendering using GPU-based raycasting. Vis. Comput. 22(8), 550–561 (2006). https://doi.org/10.1007/s00371-006-0028-0
5. Lopes, D.S., Parreira, P.F., Mendes, A.R., Pires, V.M., Paulo, S.F., Sousa, C., Jorge, J.A.: Explicit design of transfer functions for volume-rendered images by combining histograms, thumbnails, and sketch-based interaction. Vis. Comput. 34(12), 1713–1723 (2018). https://doi.org/10.1007/s00371-017-1448-8
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1. Publisher Correction: Twinenet: coupling features for synthesizing volume rendered images via convolutional encoder–decoders and multilayer perceptrons;The Visual Computer;2024-05-28
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