3D Pointcloud Registration In-the-wild

Abstract

This study assesses two state-of-the-art (SOTA) pointcloud registration approaches on industrially challenging datasets, focusing on two specific cases. The first case involves the application of Lidar-based Simultaneous Localization and Mapping (SLAM) in a tunnel environment, while the second case revolves around aligning RGBD scans from intricately symmetrical cast-iron machine parts within the domain of small-scale industrial production. Our evaluation involves testing state-of-the-art pointcloud registration approaches both with and without fine-tuning, and comparing the results to a classical hand crafted feature extractors. Our experimental findings reveal that existing SOTA models exhibit limited generalization capability when confronted with the more challenging pointcloud data. Moreover, robust generalizable methods beyond training are currently unavailable, highlighting a notable gap in addressing challenges associated with industrial datasetsin pointcloud registration.