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FanNet: A mesh convolution operator for learning dense maps
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DOI：10.1016/j.cag.2025.104320.png)
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Abstract
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• We introduce FanNet as a simple and novel mesh convolution operator. • FanNet is based on a neighborhood concept in which neighbors of a vertex are gathered as the spokes of a fan. • FanNet is applied to the problem of shape correspondence. • FanNet works well in both supervised and unsupervised settings. • FanNet provides on-par or better performance than the state-of-the-art.
Keywords:
FanNet
mesh convolution
shape correspondence
neighborhood concept
supervised learning
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4.2
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1.5K
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3.3K
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Cited Papers
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