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Müller, Simone ORCID logoORCID: https://orcid.org/0000-0001-5830-8655 und Kranzlmüller, Dieter ORCID logoORCID: https://orcid.org/0000-0002-8319-0123 (2021): Dynamic Sensor Matching for Parallel Point Cloud Data Acquisition. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, Pilzen, 2021. Computer Science Research Notes. [PDF, 4MB]

Abstract

Based on depth perception of individual stereo cameras, spatial structures can be derived as point clouds. The quality of such three-dimensional data is technically restricted by sensor limitations, latency of recording, and insufficient object reconstructions caused by surface illustration. Additionally external physical effects like lighting conditions, material properties, and reflections can lead to deviations between real and virtual object perception. Such physical influences can be seen in rendered point clouds as geometrical imaging errors on surfaces and edges. We propose the simultaneous use of multiple and dynamically arranged cameras. The increased information density leads to more details in surrounding detection and object illustration. During a pre-processing phase the collected data are merged and prepared. Subsequently, a logical analysis part examines and allocates the captured images to three-dimensional space. For this purpose, it is necessary to create a new metadata set consisting of image and localisation data. The post-processing reworks and matches the locally assigned images. As a result, the dynamic moving images become comparable so that a more accurate point cloud can be generated. For evaluation and better comparability we decided to use synthetically generated data sets. Our approach builds the foundation for dynamic and real-time based generation of digital twins with the aid of real sensor data.

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