Abstract
In this paper we set out to find a new technical and commercial solution to easily acquire a virtual model of existing machinery for visualisation in a VR environment. To this end we introduce an image-based scanning approach with an initial focus on a monocular (handheld) capturing device such as a portable camera. Poses of the camera will be estimated with a Simultaneous Localisation and Mapping technique. Depending on the required quality offline calibration is incorporated by means of ArUco markers placed within the captured scene. Once the images are captured, they are compressed in a format that allows rapid low-latency streaming and decoding on the GPU. Finally, upon viewing the model in a VR environment, an optical flow method is used to interpolate between the triangulisation of the captured viewpoints to deliver a smooth VR experience. We believe our tool will facilitate the capturing of machinery into VR providing a wide range of benefits such as doing marketing, providing offsite help and performing remote maintenance.
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Acknowledgements
This research was partially supported by Flanders Make, the strategic research centre for the manufacturing industry, in view of the Flanders Make FLEXAS_VR project.
We also gratefully express our gratitude to the European Fund for Regional Development (ERDF) and the Flemish Government, which are kindly funding part of the research at the Expertise Centre for Digital Media.
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Put, J., Michiels, N., Di Fiore, F., Van Reeth, F. (2018). Capturing Industrial Machinery into Virtual Reality. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2018. Lecture Notes in Computer Science(), vol 10945. Springer, Cham. https://doi.org/10.1007/978-3-319-94544-6_5
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DOI: https://doi.org/10.1007/978-3-319-94544-6_5
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