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VCNet: A generative model for volume completion
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DOI：10.1016/j.visinf.2022.04.004.png)
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摘要
En 中文
We present VCNet, a new deep learning approach for volume completion by synthesizing missing subvolumes. Our solution leverages a generative adversarial network (GAN) that learns to complete volumes using the adversarial and volumetric losses. The core design of VCNet features dilated residual block and long-term connection. During training, VCNet first randomly masks basic subvolumes (e.g., cuboids, slices) from complete volumes and learns to recover them. Moreover, we design a twostage algorithm for stabilizing and accelerating network optimization. Once trained, VCNet takes an incomplete volume as input and automatically identifies and fills in the missing subvolumes with high quality. We quantitatively and qualitatively test VCNet with volumetric data sets of various characteristics to demonstrate its effectiveness. We also compare VCNet against a diffusion-based solution and two GAN-based solutions. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword:
Volume visualization
Generative adversarial network
Data completion
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期刊
IF:
3.9
论文数:
242
被引数:
628
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