MIND - Multi-Feature Implicit Neural Descriptors for Robotic Surface Processing of 3D Objects With Variations in Geometry

Abstract

The recent shift from mass production to mass personalization leads to a production environment in which workpieces have a high degree of geometric variations. The robotic process automation in such high-mix low-volume environments poses significant challenges since predetermined robot programs are not viable anymore. In this letter, we consider the automation of surface processing for category-level objects with significant variations in geometry by operating on point clouds without relying on CAD models. To achieve this, we present a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-feature implicit neural descriptor</i> (MIND) representation which leverages dense correspondence to generalize across diverse objects, enabling a one-shot transfer of process trajectories and associated process knowledge. The quantitative and qualitative evaluation shows that MIND outperforms other state-of-the-art dense correspondence approaches. A real-world application case study of robotic surface processing on geometry-varying basin molds validates the efficacy of the proposed approach.