Omnidirectional lighting provides the foundation for achieving spatially-variant photorealistic 3D rendering, a desirable property for mobile augmented reality applications. However, in practice, estimating omnidirectional lighting can be challenging due to limitations such as partial panoramas of the rendering positions, and the inherent environment lighting and mobile user dynamics. A new opportunity arises recently with the advancements in mobile 3D vision, including built-in high-accuracy depth sensors and deep learning-powered algorithms, which provide the means to better sense and understand the physical surroundings. Centering the key idea of 3D vision, in this work, we design an edge-assisted framework called Xihe to provide mobile AR applications the ability to obtain accurate omnidirectional lighting estimation in real time. Specifically, we develop a novel sampling technique that efficiently compresses the raw point cloud input generated at the mobile device. This technique is derived based on our empirical analysis of a recent 3D indoor dataset and plays a key role in our 3D vision-based lighting estimator pipeline design. To achieve the real-time goal, we develop a tailored GPU pipeline for on-device point cloud processing and use an encoding technique that reduces network transmitted bytes. Finally, we present an adaptive triggering strategy that allows Xihe to skip unnecessary lighting estimations and a practical way to provide temporal coherent rendering integration with the mobile AR ecosystem. We evaluate both the lighting estimation accuracy and time of Xihe using a reference mobile application developed with Xihe's APIs. Our results show that Xihe takes as fast as 20.67ms per lighting estimation and achieves 9.4% better estimation accuracy than a state-of-the-art neural network.
翻译:上向光照明为实现空间变化的光现实3D转换提供了基础,这是移动放大现实应用的可取属性。然而,在实践中,估算全向照明可能具有挑战性,因为存在一些限制,如投影位置部分全景,以及固有的环境照明和移动用户动态等。随着移动3D愿景的进步,最近出现了一个新的机会,包括内置高精密深度感应器和深学习动力算法,这为更好地了解和理解物理环境提供了手段。在3D愿景的关键理念中,在这项工作中,我们设计了一个称为Xihe的边缘辅助准确度框架,以提供移动AR应用程序在实时获得准确的全向光估计的能力。具体地说,我们开发了一个新的取样技术,高效地压缩了移动设备产生的原始点云量输入。这一技术基于我们对最近3D室内数据集的经验分析,并在我们基于视觉的照明管道设计中发挥着关键作用。为了实现实时目标,我们开发了一个名为Shihe的准确度估算结果,我们用一个定制的GPULL网络来提供一个更精确的快速的电路路路路路路段,我们用一个更精确的电路路路路路路路路路路,以显示一个更精确的升级的电路透技术来显示。我们用一个更精确的电路路路路路路段,用来进行升级的电路路段,通过升级的电路路段,通过升级的电路路段的电路段,以显示的电路路路路路段,以显示一个更精确的电路路路路段路路路路路路路段,以显示一个更好的路路路路段,以显示一个升级的电路段。