PReP: Efficient context-based shape retrieval for missing parts
This addresses a domain-specific need in circular economy applications by enabling efficient part retrieval without requiring an existing query object, though it is incremental as it builds on existing metric learning and classification techniques.
The paper tackles the problem of retrieving replacement parts for missing components in point cloud shapes by introducing PReP, a pipeline that uses metric learning and classification to assess part suitability based on shape context, achieving the ability to sort through tens of thousands of parts in seconds.
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.