In recent years, the videogame industry has been characterized by a great boost in gesture recognition and motion tracking, following the increasing request of creating immersive game experiences. The Microsoft Kinect sensor allows acquiring RGB, IR and depth images with a high frame rate. Because of the complementary nature of the information provided, it has proved an attractive resource for researchers with very different backgrounds. In summer 2014, Microsoft launched a new generation of Kinect on the market, based on time-of-flight technology. This paper proposes a calibration of Kinect for Xbox One imaging sensors, focusing on the depth camera. The mathematical model that describes the error committed by the sensor as a function of the distance between the sensor itself and the object has been estimated. All the analyses presented here have been conducted for both generations of Kinect, in order to quantify the improvements that characterize every single imaging sensor. Experimental results show that the quality of the delivered model improved applying the proposed calibration procedure, which is applicable to both point clouds and the mesh model created with the Microsoft Fusion Libraries.
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机译：近年来，随着创造沉浸式游戏体验的要求不断提高，视频游戏行业的特征是手势识别和运动跟踪得到了极大的推动。 Microsoft Kinect传感器允许以高帧频获取RGB，IR和深度图像。由于所提供信息的互补性质，对于不同背景的研究人员而言，它已被证明是一种有吸引力的资源。 2014年夏季，微软基于飞行时间技术在市场上推出了新一代的Kinect。本文针对Xbox One成像传感器提出了Kinect的校准，重点是深度相机。已经估计了描述传感器所犯错误的数学模型，该误差是传感器自身与物体之间距离的函数。这里介绍的所有分析都是针对Kinect的两代产品进行的，以量化表征每个单个成像传感器的改进。实验结果表明，采用建议的校准程序可以改善交付模型的质量，该校准过程适用于点云和使用Microsoft Fusion Libraries创建的网格模型。
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