Gamification for road asset inspection from Mobile Mapping System data

References

• Aliev, K.-A., et al., 2020. Neural point-based graphics. In: A. Vedaldi, et al., eds. Computer vision – ECCV 2020. Cham: Springer International Publishing, 696–712.
   Google Scholar
• Amazon, 2022. Use ground truth to label 3D point clouds [Internet]. Available from: https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud.html [Accessed 28 Feb 2023].
   Google Scholar
• Annetta, L.A., 2010. The “I’s” have it: a framework for serious educational game design. Review of General Psychology [Internet], 14(2), 105–113. doi:10.1037/a0018985.
   Web of Science ®Google Scholar
• Balado, J., et al., 2019. Road environment semantic segmentation with deep learning from MLS point cloud data. Sensors, 19 (16), 3466. doi:10.3390/s19163466.
   PubMed Web of Science ®Google Scholar
• Balado, J., et al., 2023. Multi feature-rich synthetic colour to improve human visual perception of point clouds. ISPRS Journal of Photogrammetry and Remote Sensing [Internet], 196, 514–527. doi:10.1016/j.isprsjprs.2023.01.019.
   Web of Science ®Google Scholar
• Behley, J., et al. 2019. SemanticKITTI: a dataset for semantic scene understanding of lidar sequences. In: Proceedings of the IEEE international conference on computer vision, Seoul, Korea, 9297–9307.
   Google Scholar
• Callieri, M., et al., 2012. Meshlab as a complete open tool for the integration of photos and colour with high-resolution 3D geometry data. Computer Applications and Quantitative Methods in Archaeology, 406–416.
   Google Scholar
• Deschaud, J.-E., et al., 2021. Paris-CARLA-3D: a real and synthetic outdoor point cloud dataset for challenging tasks in 3D mapping. Remote Sensing, 13 (22), 4713. doi:10.3390/rs13224713.
   Web of Science ®Google Scholar
• Elhashash, M., Albanwan, H., and Qin, R., 2022. A review of mobile mapping systems: from sensors to applications. Sensors Internet, 22(11), 4262. doi:10.3390/s22114262.
   PubMed Web of Science ®Google Scholar
• Epic Games, 2022. LiDAR point cloud plugin [Internet]. Available from: https://docs.unrealengine.com/4.27/en-US/WorkingWithContent/LidarPointCloudPlugin/ [Accessed 22 Feb 2023].
   Google Scholar
• European Union Road Federation, 2018. Road asset management. [ place unknown].
   Google Scholar
• Feng, D., et al., 2022. Labels are not perfect: inferring spatial uncertainty in object detection. IEEE Transactions on Intelligent Transportation Systems, 23 (8), 9981–9994. doi:10.1109/TITS.2021.3096943.
   Web of Science ®Google Scholar
• Geiger, A., Lenz, P., and Urtasun, R. 2012. Are we ready for autonomous driving? the kitti vision benchmark suite. In: Computer vision and pattern recognition, 2012 IEEE conference on Providence, RI, USA, 3354–3361. doi:10.1109/CVPR.2012.6248074.
   Google Scholar
• González, E., et al., 2022. Realistic correction of sky-coloured points in mobile laser scanning point clouds. Optics & Laser Technology [Internet], 149, 107807. doi:10.1016/j.optlastec.2021.107807.
   Web of Science ®Google Scholar
• Grandio, J., et al., 2022. Point cloud semantic segmentation of complex railway environments using deep learning. Automation in Construction [Internet], 141, 104425. doi:10.1016/j.autcon.2022.104425.
   Google Scholar
• Griffiths, D. and Boehm, J., 2019. A review on deep learning techniques for 3D sensed data classification. Remote Sensing , 11 (12), 1499. doi:10.3390/rs11121499.
   Web of Science ®Google Scholar
• Groening, C. and Binnewies, C., 2019. “Achievement unlocked!” - The impact of digital achievements as a gamification element on motivation and performance. Computers in Human Behavior [Internet], 97, 151–166. doi:10.1016/j.chb.2019.02.026.
   Web of Science ®Google Scholar
• Guo, Y., et al., 2020. Deep learning for 3D point clouds: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1. doi:10.1109/TPAMI.2020.3005434.
   Web of Science ®Google Scholar
• Hackel, T., et al. 2017. Semantic3D.net: a new large-scale point cloud classification benchmark [Internet]. Available from: https://doi.org/10.48550/arxiv.1704.03847 [Accessed 28 Feb 2023].
   Google Scholar
• Kapp, K.M., 2012. The gamification of learning and instruction: game-based methods and strategies for training and education. USA: John Wiley & Sons.
   Google Scholar
• Kazhdan, M. and Hoppe, H., 2013. Screened poisson surface reconstruction. ACM Transactions on Graphics [Internet], 32(3), 1–13. doi:10.1145/2487228.2487237.
   Web of Science ®Google Scholar
• Li, Y., et al., 2021. Deep learning for LiDAR point clouds in autonomous driving: a review. IEEE Transactions on Neural Networks and Learning Systems, 32 (8), 3412–3432. doi:10.1109/TNNLS.2020.3015992.
   PubMed Web of Science ®Google Scholar
• Lin, J. and Zhang, F. 2022. R3LIVE: a Robust, real-time, RGB-colored, LiDAR-inertial-visual tightly-coupled state estimation and mapping package. In: 2022 international conference on robotics and automation (ICRA). Philadelphia, PA, 10672–10678. doi:10.1109/ICRA46639.2022.9811935.
   Google Scholar
• Liu, L., et al. 2020. Deep-learning and depth-map based approach for detection and 3-D localization of small traffic signs. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi:10.1109/JSTARS.2020.2966543.
   Google Scholar
• Liu, Z., et al., 2022. Detection and reconstruction of static vehicle-related ground occlusions in point clouds from mobile laser scanning. Automation in Construction [Internet], 141, 104461. doi:10.1016/j.autcon.2022.104461.
   Web of Science ®Google Scholar
• Li, Y., Wu, B., and Ge, X., 2019. Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density. ISPRS Journal of Photogrammetry and Remote Sensing [Internet], 153, 151–165. doi:10.1016/j.isprsjprs.2019.05.007.
   Web of Science ®Google Scholar
• Ma, L., et al., 2018. Mobile laser scanned point-clouds for road object detection and extraction: a review. Remote Sensing , 10 (10), 1531. doi:10.3390/rs10101531.
   Web of Science ®Google Scholar
• Markström, I., 2019. Comparing normal estimation methods for the rendering of unorganized point clouds.
   Google Scholar
• Matlab, 2022. Label lidar point clouds for object detection [Internet]. Available from: https://es.mathworks.com/help/driving/ug/label-lidar-point-clouds-for-object-detection.html [Accessed 28 Feb 2023].
   Google Scholar
• mgear, 2022. Point cloud viewer and tools [Internet]. Available from: https://assetstore.unity.com/packages/tools/utilities/point-cloud-viewer-and-tools-16019 [Accessed 28 Feb 2023].
   Google Scholar
• Ministerio de Transportes Movilidad y Agenda Urbana, 2020. Normativa técnica [Internet]. Available from: https://www.mitma.gob.es/carreteras/normativa-tecnica [Accessed 15 Feb 2023].
   Google Scholar
• Navares-Vázquez, J.C., et al., 2022. Virtual seeds for indoor point cloud segmentation with hololens 2. Available at SSRN 4322378.
   Google Scholar
• OECD, 2001. Asset management for the roads sector. [ place unknown].
   Google Scholar
• Park, J.-I., Park, J., and Kim, K.-S., 2020. Fast and accurate desnowing algorithm for LiDAR point clouds. IEEE Access, 8, 160202–160212. doi:10.1109/ACCESS.2020.3020266.
   Web of Science ®Google Scholar
• Qi, C.R., et al. 2017. PointNet++: deep hierarchical feature learning on point sets in a metric space. CoRR [Internet]. abs/1706 0. abs/1706 0: http://arxiv.org/abs/1706.02413.
   Google Scholar
• Quach, M., et al., 2022. Survey on deep learning-based point cloud compression. Frontiers in Signal Processing [Internet], 2. https://www.frontiersin.org/articles/10.3389/frsip.2022.846972.
   Google Scholar
• Rashdi, R., et al. 2023. Comparative study of road and urban object classification based on mobile laser scanners. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences [Internet]. XLVIII1/W12023. [Internet]. XLVIII1/W12023. Padua, Italy, 423–429. https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-423-2023.
   Google Scholar
• Robson, K., et al., 2015. Is it all a game? Understanding the principles of gamification. Business Horizons [Internet], 58(4), 411–420. doi:10.1016/j.bushor.2015.03.006.
   Web of Science ®Google Scholar
• Segments.ai, 2022. 3D point cloud labeling tool [Internet]. Available from: https://segments.ai/point-cloud-labeling [Accessed 28 Feb 2023].
   Google Scholar
• Sigala, M., 2015. Gamification for crowdsourcing marketing practices: applications and benefits in tourism. In: F.J. Garrigos-Simon, I. Gil-Pechuán, and S. Estelles-Miguel, eds. Advances in crowdsourcing [Internet]. Cham: Springer International Publishing, 129–145. doi:10.1007/978-3-319-18341-1_11.
   Google Scholar
• Tan, W., et al. 2020. Toronto-3D: a large-scale mobile LiDAR dataset for semantic segmentation of urban roadways. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Seattle, WA. doi:10.1109/cvprw50498.2020.00109.
   Google Scholar
• Thomas, H., et al. 2019. KPConv: flexible and deformable convolution for point clouds. In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV). Seoul, Korea (South).
   Google Scholar
• Virtanen, J.-P., et al., 2020. Interactive dense point clouds in a game engine. ISPRS Journal of Photogrammetry and Remote Sensing [Internet], 163, 375–389. doi:10.1016/j.isprsjprs.2020.03.007.
   Web of Science ®Google Scholar
• Wen, C., et al., 2019. A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing [Internet], 147, 178–192. doi:10.1016/j.isprsjprs.2018.10.007.
   Web of Science ®Google Scholar
• Werbach, K. and Hunter, D., 2015. The gamification toolkit: dynamics, mechanics, and components for the win. USA: University of Pennsylvania Press.
   Google Scholar
• World Road Association, 2014. The importance of road maintenance. place unknown.
   Google Scholar
• Xie, Y., Tian, J., and Zhu, XX., 2020. Linking points with labels in 3D: a review of point cloud semantic segmentation. IEEE Geoscience and Remote Sensing Magazine, 8 (4), 38–59. doi:10.1109/MGRS.2019.2937630.
   Web of Science ®Google Scholar
• Xu, Q., et al. 2020. Grid-GCN for fast and scalable point cloud learning. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR). Seattle, WA, 5660–5669. doi:10.1109/CVPR42600.2020.00570.
   Google Scholar
• Zainuddin, Z., et al., 2020. The impact of gamification on learning and instruction: a systematic review of empirical evidence. Educational Research Review [Internet], 30, 100326. doi:10.1016/j.edurev.2020.100326.
   Web of Science ®Google Scholar
• Zhang, J., et al., 2019. A review of deep learning-based semantic segmentation for point cloud. IEEE Access, 7, 179118–179133. doi:10.1109/ACCESS.2019.2958671.
   Web of Science ®Google Scholar
• Zhang, Z., Dai, Y., and Sun, J., 2020. Deep learning based point cloud registration: an overview. Virtual Reality & Intelligent Hardware [Internet], 2(3), 222–246. doi:10.1016/j.vrih.2020.05.002.
   Google Scholar