NEXT-BEST-VIEW METHOD BASED ON CONSECUTIVE EVALUATION OF TOPOLOGICAL RELATIONS

This work describes an iterative algorithm for estimating optimal viewpoints, so called next-best-views (NBVs). The goal is to incrementally\udconstruct a topological network from the scene during the consecutive acquisition of several views. Our approach is a hybrid\udmethod between a surface-based and a volumetric approach with a continuous model space. Hence, a new scan taken from an optimal\udposition should either cover as much as possible from the unknown object surface in one single scan, or densify the existing data and\udclose possible gaps. Based on the point density, we recover the essential and structural information of a scene based on the Growing\udNeural Gas (GNG) algorithm. From the created graph representation of topological relations, the density of the point cloud at each\udnetwork node is estimated by approximating the volume of Voronoi cells. The NBV Finder selects a network node as NBV, which\udhas the lowest point density. Our NBV method is self-terminating when all regions reach a predefined minimum point density or the\udchange of the GNG error is zero. For evaluation, we use a Buddha statue with a rather simple surface geometry but still some concave\udparts and the Stanford Dragon with a more complex object surface containing occluded and concave parts. We demonstrate that our\udNBV method outperforms a “naive random” approach relying on uniformly distributed sensor positions in terms of efficiency, i.e. our\udproposed method reaches a desired minimum point density up to 20% faster with less scans