Autoencoder Based Deep Clustering of Point Clouds

Abstract:Motivated by a problem in Generative Specification-Producing AIs (SPAIs), we focus on a problem of deep clustering of three-dimensional shapes specified by point clouds. After reviewing clustering and deep clustering, we study the fully unsupervised pipeline in which a FoldingNet autoencoder is first trained for reconstruction and then K-means++ is then applied to the resulting codewords. We report clustering accuracy (ACC), normalized mutual information (NMI) and adjusted Rand index (ARI) on ModelNet10 and ModelNet40 datasets, as a function of the number of clusters K. We also provide t-SNE visualizations of the codewords, and discuss the choice of K.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2611.01594 [cs.LG]

(or arXiv:2611.01594v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2611.01594 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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