Can We Speed up 3D Scanning? A Cognitive and Geometric Analysis

Abstract

The paper propose a cognitive inspired change detection method for the detection and localization of shape variations on point clouds. A well defined pipeline is introduced by proposing a coarse to fine approach: i) shape segmentation, ii) fine segment registration using attention blocks. Shape segmentation is obtained using covariance based method and fine segment registration is carried out using gravitational registration algorithm. In particular the introduction of this partition-based approach using visual attention mechanism improves the speed of deformation detection and localization. Some results are shown on synthetic data of house and aircraft models. Experimental results shows that this simple yet effective approach designed with an eye to scalability can detect and localize the deformation in a faster manner. A real world car use case is also presented with some preliminary promising results useful for auditing and insurance claim tasks.