Paper ID: 2304.02766

Shape complexity estimation using VAE

Markus Rothgaenger, Andrew Melnik, Helge Ritter

In this paper, we compare methods for estimating the complexity of two-dimensional shapes and introduce a method that exploits reconstruction loss of Variational Autoencoders with different sizes of latent vectors. Although complexity of a shape is not a well defined attribute, different aspects of it can be estimated. We demonstrate that our methods captures some aspects of shape complexity. Code and training details will be publicly available.

Submitted: Apr 5, 2023