← 研踪 PaperTrailarXiv:2611.01594 · v1
Autoencoder Based Deep Clustering of Point Clouds
首次提交 2026-11-01 13:22
版本更新 2026-11-01 13:22
雷达观测 2026-11-04 23:43
摘要
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.
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-01 13:22 · 最新观测版本
Autoencoder Based Deep Clustering of Point Clouds
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.
查看此版本解读 →查看此版本原文 ↗