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High-Fidelity Point-Based Rendering of Large-Scale 3D Scan Datasets
IEEE Computer Graphics and Applications ( IF 1.7 ) Pub Date : 2020-05-01 , DOI: 10.1109/mcg.2020.2974064
Patric Schmitz 1 , Timothy Blut 1 , Christian Mattes 1 , Leif Kobbelt 1
Affiliation  

Digitalization of three-dimensional (3-D) objects and scenes using modern depth sensors and high-resolution RGB cameras enables the preservation of human cultural artifacts at an unprecedented level of detail. Interactive visualization of these large datasets, however, is challenging without degradation in visual fidelity. A common solution is to fit the dataset into available video memory by downsampling and compression. The achievable reproduction accuracy is thereby limited for interactive scenarios, such as immersive exploration in virtual reality (VR). This degradation in visual realism ultimately hinders the effective communication of human cultural knowledge. This article presents a method to render 3-D scan datasets with minimal loss of visual fidelity. A point-based rendering approach visualizes scan data as a dense splat cloud. For improved surface approximation of thin and sparsely sampled objects, we propose oriented 3-D ellipsoids as rendering primitives. To render massive texture datasets, we present a virtual texturing system that dynamically loads required image data. It is paired with a single-pass page prediction method that minimizes visible texturing artifacts. Our system renders a challenging dataset in the order of 70 million points and a texture size of 1.2 TB consistently at 90 frames per second in stereoscopic VR.

中文翻译:

大规模 3D 扫描数据集的基于点的高保真渲染

使用现代深度传感器和高分辨率 RGB 相机对三维 (3-D) 物体和场景进行数字化,能够以前所未有的细节水平保存人类文化文物。然而,这些大型数据集的交互式可视化在不降低视觉保真度的情况下具有挑战性。一个常见的解决方案是通过下采样和压缩将数据集放入可用的视频内存中。因此,对于交互式场景,例如虚拟现实 (VR) 中的沉浸式探索,可实现的再现精度受到限制。这种视觉现实主义的退化最终阻碍了人类文化知识的有效交流。本文介绍了一种在视觉保真度损失最小的情况下渲染 3-D 扫描数据集的方法。基于点的渲染方法将扫描数据可视化为密集的碎片云。为了改进薄和稀疏采样对象的表面近似,我们建议将定向 3-D 椭球作为渲染图元。为了渲染大量纹理数据集,我们提出了一个动态加载所需图像数据的虚拟纹理系统。它与单遍页面预测方法配对,可最大限度地减少可见的纹理伪影。我们的系统在立体 VR 中以每秒 90 帧的速度持续渲染 7000 万个点和 1.2 TB 纹理大小的具有挑战性的数据集。
更新日期:2020-05-01
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