Embedding 3D models in offline physical environments,Computer Animation and Virtual Worlds

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Embedding 3D models in offline physical environments
Computer Animation and Virtual Worlds ( IF 0.9 ) Pub Date : 2020-07-01 , DOI: 10.1002/cav.1959
Egemen Ertugrul ₁ , Han Zhang _{2,

3} , Fang Zhu _{2,

3} , Ping Lu _{2,

3} , Ping Li ₄ , Bin Sheng ₁ , Enhua Wu _{5,

6}

Affiliation

This article introduces a novel approach for embedding 3D models in offline physical environments using quick response (QR) codes. Unlike conventional methods, we consider settings where 3D models cannot be retrieved from a remote server. Our method involves generating octree models from voxelized 3D models and storing them in QR codes using a space‐efficient data structure. This allows storing 3D models that are both intelligible and purposeful on standard QR codes while addressing the major storage constraint that is present in offline situations. Furthermore, we explore 3D convolutional neural networks (CNN) and autoencoders (AE) to compress 3D models with high resolutions where using octrees alone does not suffice. To the best of our knowledge, our AE network is the first to employ octrees to further compress its encoded data. Through user‐friendly desktop and mobile applications, we allow users to encode, decode and visualize 3D models in augmented reality (AR) using QR codes, thus experiment with our methods. The proposed approach enables unique applications and future research in ubiquitous computing, 3D data compression and transmission, 3D AEs, AR and Virtual Reality, low‐cost autonomous robots, and 3D printing.

中文翻译：

在离线物理环境中嵌入 3D 模型

本文介绍了一种使用快速响应 (QR) 代码在离线物理环境中嵌入 3D 模型的新方法。与传统方法不同，我们考虑无法从远程服务器检索 3D 模型的设置。我们的方法涉及从体素化 3D 模型生成八叉树模型，并使用节省空间的数据结构将它们存储在二维码中。这允许在标准 QR 码上存储既清晰又有意义的 3D 模型，同时解决离线情况下存在的主要存储限制。此外，我们探索了 3D 卷积神经网络 (CNN) 和自动编码器 (AE) 来压缩具有高分辨率的 3D 模型，其中单独使用八叉树是不够的。据我们所知，我们的 AE 网络是第一个使用八叉树来进一步压缩其编码数据的网络。通过用户友好的桌面和移动应用程序，我们允许用户使用二维码在增强现实 (AR) 中编码、解码和可视化 3D 模型，从而试验我们的方法。所提出的方法可以在普适计算、3D 数据压缩和传输、3D AE、AR 和虚拟现实、低成本自主机器人和 3D 打印方面实现独特的应用和未来研究。

更新日期：2020-07-01

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