用形状上下文匹配缺损部件，快速从成千上万备件中找合适替代品
PReP: Efficient context-based shape retrieval for missing parts
- 基于形状上下文和度量学习，无需查询对象即可匹配缺失部件
- 在十万级备件库中仅需数秒完成检索，参数少、计算开销低
- 适用于循环经济场景，特别适合工业设备维修与零件重用
本文研究点云领域中的形状部件检索问题。现有方法依赖于已有查询对象，但当所需部件缺失时如何解决？我们提出部件检索流水线（PReP），通过度量学习与训练好的分类模型，评估数据库中潜在替换部件的适配性，服务于循环经济应用场景。采用逐步增加难度的创新训练策略，仅凭形状上下文即可识别合适部件。由于参数量小、计算需求低，可在数秒内完成对数十万备件的筛选。我们还建立了对比基线，系统阐述该任务的独特挑战及应对设计。
原文摘要 · Abstract (English)
In this paper we study the problem of shape part retrieval in the point cloud domain. Shape retrieval methods in the literature rely on the presence of an existing query object, but what if the part we are looking for is not available? We present Part Retrieval Pipeline (PReP), a pipeline that creatively utilizes metric learning techniques along with a trained classification model to measure the suitability of potential replacement parts from a database, as part of an application scenario targeting circular economy. Through an innovative training procedure with increasing difficulty, it is able to learn to recognize suitable parts relying only on shape context. Thanks to its low parameter size and computational requirements, it can be used to sort through a warehouse of potentially tens of thousand of spare parts in just a few seconds. We also establish an alternative baseline approach to compare against, and extensively document the unique challenges associated with this task, as well as identify the design choices to solve them.
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