NRDF - Neural Region Descriptor Fields as Implicit ROI Representation for Robotic 3D Surface Processing

Abstract

To automate 3D surface processing across diverse category-level objects it is imperative to represent process-related region of interest (P-ROI), which is not obtained with conventional keypoint or semantic part correspondences. To resolve this issue, we propose Neural Region Descriptor Fields (NRDF) for achieving unsupervised dense 3D surface region correspondence such that arbitrary ROI is retrieved for a new instance of a known category of object. We utilize the NRDF representation as a medium to facilitate one-shot P-ROI level process knowledge transfer. Recent developments in implicit 3D object representations have focused on keypoint or part correspondences, which have resulted in applications like robotic grasping and manipulation. However, explicit one-shot P-ROI correspondence, and its application for 3D surface process knowledge transfer, is treated for the first time in this work, to the best of our knowledge. The evaluation results show that the proposed approach outperforms the dense correspondence baselines in implicit shape representation and the capacity to retrieve matching arbitrary ROIs. In addition, we validate the practicality of our proposed system in a real-world robotic surface processing application. Our code is available at https://github.com/Profactor/Neural-Region-Descriptor-Fields.