Abstract

Research and development of smart vessels has progressed significantly in recent years, and ships have become high-value technology-intensive resources. These ships entail high production costs and long-life cycles. Thus, modernized technical design, professional training, and aggressive maintenance are important factors in the efficient management of ships. With the continuing digital revolution, the industrial shipbuilding applicability of augmented reality (AR) and virtual reality (VR) technologies as well as related 3D system modeling and processes has increased. However, resolving the differences between AR/VR and real-world models remains burdensome. This problem is particularly evident when mapping various texture characteristics to virtual objects. To mitigate the burden and improve the performance of such technologies, it is necessary to directly define various texture characteristics or to express them using expensive equipment. The use of deep-learning-based CycleGAN, however, has gained attention as a method of learning and automatically mapping real-object textures. Thus, we seek to use CycleGAN to improve the immersive capacities of AR/VR models and to reduce production costs for shipbuilding. However, when applying CycleGAN’s textures to pipe structures, the performance is insufficient for direct application to industrial piping networks. Therefore, this study investigates an improved CycleGAN algorithm that can be specifically applied to the shipbuilding industry by combining a modified object-recognition algorithm with a double normalization method. Thus, we demonstrate that basic knowledge on the production of AR industrial pipe models can be applied to virtual models through machine learning to deliver low-cost and high-quality textures. Our results provide an on-ramp for future CycleGAN studies related to the shipbuilding industry.

中文翻译：

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使用对象识别传递的2D纹理生成容器管道的3D纹理模型☆

摘要

近年来，智能船舶的研究和开发取得了长足的进步，船舶已成为高价值的技术密集型资源。这些船需要高昂的生产成本和较长的使用寿命。因此，现代化的技术设计，专业的培训和积极的维护是有效管理船舶的重要因素。随着持续的数字革命，增强现实（AR）和虚拟现实（VR）技术以及相关3D系统建模和过程在工业造船中的适用性不断提高。然而，解决AR / VR和现实模型之间的差异仍然很麻烦。当将各种纹理特征映射到虚拟对象时，此问题尤其明显。为了减轻此类技术的负担并提高其性能，有必要直接定义各种纹理特征或使用昂贵的设备来表达它们。但是，基于深度学习的CycleGAN的使用已作为一种学习和自动映射真实对象纹理的方法而受到关注。因此，我们寻求使用CycleGAN来提高AR / VR模型的沉浸能力并降低造船的生产成本。但是，将CycleGAN的纹理应用于管道结构时，其性能不足以直接应用于工业管道网络。因此，本研究研究了一种改进的CycleGAN算法，该算法可以通过将改进的对象识别算法与双重归一化方法相结合而专门应用于造船业。因此，我们证明，有关AR工业管道模型生产的基础知识可以通过机器学习应用于虚拟模型，以提供低成本和高质量的纹理。我们的研究结果为未来CycleGAN与造船业相关的研究提供了依据。