OmniKinect: real-time dense volumetric data acquisition and applications

Bernhard Kainz, Stefan Hauswiesner, Gerhard Reitmayr, M. Steinberger, R. Grasset, Lukas Gruber, Eduardo Veas, Denis Kalkofen, H. Seichter, D. Schmalstieg
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引用次数: 70

Abstract

Real-time three-dimensional acquisition of real-world scenes has many important applications in computer graphics, computer vision and human-computer interaction. Inexpensive depth sensors such as the Microsoft Kinect allow to leverage the development of such applications. However, this technology is still relatively recent, and no detailed studies on its scalability to dense and view-independent acquisition have been reported. This paper addresses the question of what can be done with a larger number of Kinects used simultaneously. We describe an interference-reducing physical setup, a calibration procedure and an extension to the KinectFusion algorithm, which allows to produce high quality volumetric reconstructions from multiple Kinects whilst overcoming systematic errors in the depth measurements. We also report on enhancing image based visual hull rendering by depth measurements, and compare the results to KinectFusion. Our system provides practical insight into achievable spatial and radial range and into bandwidth requirements for depth data acquisition. Finally, we present a number of practical applications of our system.
OmniKinect:实时密集体积数据采集和应用
真实场景的实时三维采集在计算机图形学、计算机视觉和人机交互等领域有着重要的应用。廉价的深度传感器,如微软的Kinect,可以利用这些应用程序的发展。然而,该技术仍然是相对较新的技术,并且没有详细研究其可扩展性到密集和不依赖于视图的获取的报道。本文讨论的问题是同时使用大量的kinect可以做些什么。我们描述了一种减少干扰的物理设置、校准程序和对KinectFusion算法的扩展,该算法允许从多个kinect产生高质量的体积重建,同时克服深度测量中的系统误差。我们还报告了通过深度测量增强基于图像的视觉船体渲染,并将结果与KinectFusion进行比较。我们的系统提供了可实现的空间和径向范围以及深度数据采集的带宽要求的实用见解。最后,给出了系统的一些实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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