Augmented Reality 3D Discrepancy Check in Industrial Applications

Abstract

Discrepancy check is a well-known task in industrial Augmented Reality (AR). In this paper we present a new approach consisting of three main contributions: First, we propose a new two-step depth mapping algorithm for RGB-D cameras, which fuses depth images with given camera pose in real-time into a consistent 3D model. In a rigorous evaluation with two public benchmarks we show that our mapping outperforms the state-of-the-art in accuracy. Second, we propose a semi-automatic alignment algorithm, which rapidly aligns a reference model to the reconstruction. Third, we propose an algorithm for 3D discrepancy check based on pre-computed distances. In a systematic evaluation we show the superior performance of our approach compared to state-of-the-art 3D discrepancy checks.