PReP: Efficient context-based shape retrieval for missing parts

TL;DR

This paper introduces PReP, a fast and efficient shape part retrieval pipeline that uses metric learning and classification to find suitable replacement parts in large databases, aiding circular economy efforts.

Contribution

PReP is a novel pipeline that employs a unique training procedure and shape context analysis for shape part retrieval without requiring the target part to be present.

Findings

PReP can sort through tens of thousands of parts in seconds.

It outperforms baseline approaches in shape part retrieval tasks.

The method is computationally efficient with low parameter requirements.

Abstract

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.