论文标题

部分可观测时空混沌系统的无模型预测

NeARportation: A Remote Real-time Neural Rendering Framework

论文作者

Hiroi, Yuichi, Itoh, Yuta, Rekimoto, Jun

论文摘要

储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。

While the presentation of photo-realistic appearance plays a major role in immersion in an augmented virtuality environment, displaying the photo-realistic appearance of real objects remains a challenging problem. Recent developments in photogrammetry have facilitated the incorporation of real objects into virtual space. However, photo-realistic photogrammetry requires a dedicated measurement environment, and there is a trade-off between measurement cost and quality. Furthermore, even with photo-realistic appearance measurements, there is a trade-off between rendering quality and framerate. There is no framework that could resolve these trade-offs and easily provide a photo-realistic appearance in real-time. Our NeARportation framework combines server-client bidirectional communication and neural rendering to resolve these trade-offs. Neural rendering on the server receives the client's head posture and generates a novel-view image with realistic appearance reproduction, which is streamed onto the client's display. By applying our framework to a stereoscopic display, we confirmed that it could display a high-fidelity appearance on full-HD stereo videos at 35-40 frames-per-second (fps), according to the user's head motion.

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