ABSTRACT

Gamification techniques have been proven effective in various fields such as education and industry. In this paper, we introduce a novel approach that applies gamification techniques to the identification of road assets in Mobile Laser Scanning (MLS) data. Our method utilises three gamification techniques: avatar (vehicle), point cloud segmentation into levels, and scoring. We implemented these techniques in Unreal Engine and evaluated their performance using three real-world case studies. We also compared two ways of point cloud visualisation: mesh-based and point-based. Our results demonstrate that our gamification approach improves the handling and visualisation of point clouds when compared to other free software such as Cloud Compare. Specifically, the point-based visualisation method provides a more accurate representation of the road environment and the input point cloud and is easier to import into Unreal Engine. However, this method requires more computational resources for visualisation. On the other hand, level segmentation ensures a constant frame rate of 60 frames per second. Furthermore, our gamification approach enhances the experience of road asset identification, making it more enjoyable for the user. However, we acknowledge that the quality of the point cloud remains the primary factor affecting the accuracy of asset identification, regardless of the software used. Overall, our proposed gamification approach offers a promising solution for improving the identification of road assets in MLS data and has the potential to be applied to other fields beyond road asset identification.

摘要

游戏化技术已在教育和工业等各个领域被证明是有效的。在本文中，我们介绍了一种新颖的方法，该方法将游戏化技术应用于移动激光扫描（MLS）数据中的道路资产识别。我们的方法利用三种游戏化技术：头像（车辆）、点云分割为级别和评分。我们在虚幻引擎中实现了这些技术，并使用三个真实案例研究评估了它们的性能。我们还比较了点云可视化的两种方式：基于网格和基于点。我们的结果表明，与 Cloud Compare 等其他免费软件相比，我们的游戏化方法改进了点云的处理和可视化。具体来说，基于点的可视化方法可以更准确地表示道路环境和输入点云，并且更容易导入到虚幻引擎中。然而，这种方法需要更多的计算资源来进行可视化。另一方面，级别分段可确保每秒 60 帧的恒定帧速率。此外，我们的游戏化方法增强了道路资产识别的体验，使用户更加享受。然而，我们承认，无论使用何种软件，点云的质量仍然是影响资产识别准确性的主要因素。总体而言，我们提出的游戏化方法为改进 MLS 数据中道路资产的识别提供了一种有前途的解决方案，并且有潜力应用于道路资产识别以外的其他领域。