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Mesh Variational Autoencoders with Edge Contraction Pooling | | Yu-Jie Yuan
; Yu-Kun Lai
; Jie Yang
; Hongbo Fu
; Lin Gao
; | | Date: |
7 Aug 2019 | | Abstract: | 3D shape analysis is an important research topic in computer vision and
graphics. While existing methods have generalized image-based deep learning to
meshes using graph-based convolutions, the lack of an effective pooling
operation restricts the learning capability of their networks. In this paper,
we propose a novel pooling operation for mesh datasets with the same
connectivity but different geometry, by building a mesh hierarchy using mesh
simplification. For this purpose, we develop a modified mesh simplification
method to avoid generating highly irregularly sized triangles. Our pooling
operation effectively encodes the correspondence between coarser and finer
meshes in the hierarchy. We then present a variational auto-encoder structure
with the edge contraction pooling and graph-based convolutions, to explore
probability latent spaces of 3D surfaces. Our network requires far fewer
parameters than the original mesh VAE and thus can handle denser models thanks
to our new pooling operation and convolutional kernels. Our evaluation also
shows that our method has better generalization ability and is more reliable in
various applications, including shape generation, shape interpolation and shape
embedding. | | Source: | arXiv, 1908.2507 | | Services: | Forum | Review | PDF | Favorites | |
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