The PMD Camboard Picoflexx Time-of-Flight (ToF) camera is evaluated against the Microsoft Kinect V2 to assess its performance in the context of markerless motion capture system for human body kinematics measurements. Various error sources such as the warm-up time, the depth distortion, the amplitude related error, the signal-to-noise ratio, and limitations such as their dependence on the illumination pattern and on the target distance, are studied and compared. The Picoflexx device is also compared to the Kinect V2 in relation to the quality of shape reconstructions, to assess its adequateness in modeling human body segments, and human body kinematics measurements. The final result of this paper is definitely useful to the research community, demonstrating that, even if the Picoflexx performs lower than the Kinect concerning the measurement performances, its behavior in estimating the volume of the body segments, the angles at the joints for human body kinematics, and the angle at the ankle in assisted walking applications is definitely satisfactory. These results are extremely significant to obtain accurate estimates of the parameters of the human body models in markerless motion capture applications, especially in laboratory-free environments, where compactness, lightweight, wireless connection, and low-power consumption are of outmost importance.

Sensor resolution of 3D time-of-flight (ToF) outdoor-capable cameras is strongly limited because of its large pixel dimensions. Computational imaging permits enhancement of the optical system’s resolving power without changing physical sensor properties. Super-resolution (SR) algorithms superimpose several sub-pixel-shifted low-resolution (LR) images to overcome the system’s limited spatial sampling rate. In this paper, we propose a novel opto-mechanical system to implement sub-pixel shifts by moving an optical lens. This method is more flexible in terms of implementing SR techniques than current sensor-shift approaches. In addition, we describe a SR observation model that has been optimized for the use of LR 3D ToF cameras. A state-of-the-art iteratively reweighted minimization algorithm executes the SR process. It is proven that our method achieves nearly the same resolution increase as if the pixel area would be halved physically. Resolution enhancement is measured objectively for amplitude images of a static object scene.

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