Boutsi Argyro-Maria, Ioannidis Charalabos, Verykokou Styliani
Laboratory of Photogrammetry, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15780 Athens, Greece.
Sensors (Basel). 2023 Aug 3;23(15):6885. doi: 10.3390/s23156885.
In the context of web augmented reality (AR), 3D rendering that maintains visual quality and frame rate requirements remains a challenge. The lack of a dedicated and efficient 3D format often results in the degraded visual quality of the original data and compromises the user experience. This paper examines the integration of web-streamable view-dependent representations of large-sized and high-resolution 3D models in web AR applications. The developed cross-platform prototype exploits the batched multi-resolution structures of the Nexus.js library as a dedicated lightweight web AR format and tests it against common formats and compression techniques. Built with AR.js and Three.js open-source libraries, it allows the overlay of the multi-resolution models by interactively adjusting the position, rotation and scale parameters. The proposed method includes real-time view-dependent rendering, geometric instancing and 3D pose regression for two types of AR: natural feature tracking (NFT) and location-based positioning for large and textured 3D overlays. The prototype achieves up to a 46% speedup in rendering time compared to optimized glTF models, while a 34 M vertices 3D model is visible in less than 4 s without degraded visual quality in slow 3D networks. The evaluation under various scenes and devices offers insights into how a multi-resolution scheme can be adopted in web AR for high-quality visualization and real-time performance.
在网络增强现实(AR)的背景下,保持视觉质量和帧率要求的3D渲染仍然是一个挑战。缺乏专用且高效的3D格式往往会导致原始数据的视觉质量下降,并影响用户体验。本文研究了大型高分辨率3D模型的网络可流式视图相关表示在网络AR应用中的集成。所开发的跨平台原型利用Nexus.js库的批处理多分辨率结构作为一种专用的轻量级网络AR格式,并将其与常见格式和压缩技术进行测试。它基于AR.js和Three.js开源库构建,允许通过交互式调整位置、旋转和缩放参数来叠加多分辨率模型。所提出的方法包括针对两种类型AR的实时视图相关渲染、几何实例化和3D姿态回归:自然特征跟踪(NFT)以及用于大型纹理3D叠加的基于位置的定位。与优化后的glTF模型相比,该原型在渲染时间上最多可加快46%,而在慢速3D网络中,一个具有3400万个顶点的3D模型在不到4秒内即可可见,且视觉质量不会下降。在各种场景和设备下的评估为如何在网络AR中采用多分辨率方案以实现高质量可视化和实时性能提供了见解。