6533b831fe1ef96bd1299a25

RESEARCH PRODUCT

Taking Advantage of Selective Change Driven Processing for 3D Scanning

Jose Antonio BoludaPedro ZuccarelloFernando PardoFrancisco Vegara

subject

Event-based visionLaser scanningComputer scienceTransducers3d scanninglcsh:Chemical technologySensitivity and SpecificityBiochemistryArticleAnalytical Chemistrylaw.inventionPhotometryPhotometry (optics)Imaging Three-DimensionallawInformàticaNyquist–Shannon sampling theoremComputer visionlcsh:TP1-11853D scanningElectrical and Electronic Engineeringhigh-speed visual acquisitionInstrumentationPixelbusiness.industryLasers3D reconstructionReproducibility of ResultsSignal Processing Computer-AssistedEquipment DesignImage EnhancementLaserAtomic and Molecular Physics and OpticsEquipment Failure AnalysisTransducerSemiconductorsCMOSArtificial intelligencebusinessHigh-speed visual acquisitionevent-based vision

description

This article deals with the application of the principles of SCD (Selective Change Driven) vision to 3D laser scanning. Two experimental sets have been implemented: one with a classical CMOS (Complementary Metal-Oxide Semiconductor) sensor, and the other one with a recently developed CMOS SCD sensor for comparative purposes, both using the technique known as Active Triangulation. An SCD sensor only delivers the pixels that have changed most, ordered by the magnitude of their change since their last readout. The 3D scanning method is based on the systematic search through the entire image to detect pixels that exceed a certain threshold, showing the SCD approach to be ideal for this application. Several experiments for both capturing strategies have been performed to try to find the limitations in high speed acquisition/processing. The classical approach is limited by the sequential array acquisition, as predicted by the Nyquist - Shannon sampling theorem, and this has been experimentally demonstrated in the case of a rotating helix. These limitations are overcome by the SCD 3D scanning prototype achieving a significantly higher performance. The aim of this article is to compare both capturing strategies in terms of performance in the time and frequency domains, so they share all the static characteristics including resolution, 3D scanning method, etc., thus yielding the same 3D reconstruction in static scenes.

10.3390/s131013143http://dx.doi.org/10.3390/s131013143