MIND - Multi-Feature Implicit Neural Descriptors for Robotic Surface Processing of 3D Objects with Variations in Geometry
Anish Pratheepkumar, Christian Hartl-Nesic, Markus Ikeda, Fabian Widmoser, Andreas Pichler, Markus Vincze
AI summary
Problem
Robotic surface processing struggles to generalize across objects with high geometric variation in high-mix manufacturing, as existing methods rely on CAD models or require tedious reprogramming.
Approach
The authors introduce MIND, a neural implicit descriptor that fuses local geometry, normals, signed distance functions, and sparse keypoints to establish dense 3D correspondences across category-level objects, allowing direct trajectory transfer from raw point clouds.
Key results
- Novel multi-feature implicit neural descriptor architecture
- CAD-free one-shot trajectory and process knowledge transfer across geometric variations
- Quantitative and qualitative outperformance of state-of-the-art dense correspondence methods
- Real-world validation on robotic surface processing of geometry-varying basin molds
Why it matters
Provides a scalable, CAD-free solution for flexible robotic automation in high-mix manufacturing environments.
Abstract
Therecentshiftfrommassproductiontomassperson- alization 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 sig- nificant 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 multi-feature implicit neural descriptor (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.