We introduce the first work on benchmarking and evaluating deep clustering algorithms on large-scale non-categorical 3D CAD models. We first propose a workflow to allow expert mechanical engineers to efficiently annotate 252,648 carefully sampled pairwise CAD model similarities, from a subset of the ABC dataset with 22,968 shapes. Using seven baseline deep clustering methods, we then investigate the fundamental challenges of evaluating clustering methods for non-categorical data. Based on these challenges, we propose a novel and viable ensemble-based clustering comparison approach. This work is the first to directly target the underexplored area of deep clustering algorithms for 3D shapes, and we believe it will be an important building block to analyze and utilize the massive 3D shape collections that are starting to appear in deep geometric computing.

介绍了针对大规模非分类 3D CAD 模型的深度聚类算法的基准测试和评估工作。使用七种基准深度聚类方法，提出了一种新颖且可行的基于集成的聚类比较方法，以解决非分类数据的聚类方法评估所面临的挑战。这项工作是首次直接针对用于 3D 形状的深度聚类算法的未开发领域，我们相信这将成为分析和利用正在出现的大规模 3D 形状收藏的重要基础。

评估非分类别三维 CAD 模型上的深度聚类算法