Abstract
The development and implementation of computer vision methods require an appropriate data-set of images for testing. Capturing real images is time-consuming, and it can be difficult in some applications. Furthermore, a precise measurement of distances and geometric transformations between camera positions for particular images is not always possible. In these cases, synthetic data can be beneficially used. Besides, they allow precisely setting camera positions and simulating various types of camera movements. The method for generation of stereo camera data-set is presented in this paper. It is built on the 3D creation suite Blender. The method is designed for visual odometry and 3D reconstruction testing, and it simulates a stereo camera movement over the captured 3D model. The images are directly inputted to the tested system. Together with the known ground truth of camera positions, they allow testing particular steps of the system. The proposed method was used for testing 3D reconstruction of a camera-based car undercarriages scanner.
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References
Air-sim. https://microsoft.github.io/AirSim/
Autodesk 3ds max. https://www.autodesk.com/products/3ds-max
Blender. https://www.blender.org/
Coppeliasim. https://www.coppeliarobotics.com/
Dörfler, M., Pivoňka, T., Košnar, K., Přeučil, L.: Application of surface reconstruction for car undercarriage inspection. In: 2020 3rd International Conference on Intelligent Robotic and Control Engineering (IRCE), pp. 47–51 (2020). https://doi.org/10.1109/IRCE50905.2020.9199251
Gao, Q., Shen, X., Niu, W.: Large-scale synthetic urban dataset for aerial scene understanding. IEEE Access 8, 42131–42140 (2020). https://doi.org/10.1109/ACCESS.2020.2976686
gazebo. http://gazebosim.org/
Giubilato, R., Masili, A., Chiodini, S., Pertile, M., Debei, S.: Simulation framework for mobile robots in planetary-like environments. In: 2020 IEEE 7th International Workshop on Metrology for AeroSpace (MetroAeroSpace), pp. 594–599 (2020). https://doi.org/10.1109/MetroAeroSpace48742.2020.9160154
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004). ISBN: 0521540518
Lima, M.V.P., Bastos, V.B., Kurka, P.R.G., Araujo, D.C.: vslam experiments in a custom simulated environment. In: 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–7 (2015). https://doi.org/10.1109/IPIN.2015.7346757
Marelli, D., Bianco, S., Celona, L., Ciocca, G.: A blender plug-in for comparing structure from motion pipelines. In: 2018 IEEE 8th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), pp. 1–5 (2018). https://doi.org/10.1109/ICCE-Berlin.2018.8576196
Meshroom. https://alicevision.org/#meshroom
Michels, T., Petersen, A., Palmieri, L., Koch, R.: Simulation of plenoptic cameras. In: 2018–3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), pp. 1–4 (2018). https://doi.org/10.1109/3DTV.2018.8478432
Particke, F., Kalisz, A., Hofmann, C., Hiller, M., Bey, H., Thielecke, J.: Systematic analysis of direct sparse odometry. In: 2018 Digital Image Computing: Techniques and Applications (DICTA), pp. 1–6 (2018). https://doi.org/10.1109/DICTA.2018.8615807
Richardson, A., Strom, J., Olson, E.: AprilCal: assisted and repeatable camera calibration. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1814–1821 (2013). https://doi.org/10.1109/IROS.2013.6696595
Zbrush2021. http://pixologic.com/features/about-zbrush.php
Acknowledgements
The project is solved in collaboration with VOP CZ, s.p. and it is supported by the Ministry of the Interior of the Czech Republic under the project number VI20172020080.
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Pivoňka, T., Přeučil, L. (2021). Stereo Camera Simulation in Blender. In: Mazal, J., Fagiolini, A., Vasik, P., Turi, M. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2020. Lecture Notes in Computer Science(), vol 12619. Springer, Cham. https://doi.org/10.1007/978-3-030-70740-8_13
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DOI: https://doi.org/10.1007/978-3-030-70740-8_13
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