KEYWORDS: 3D image processing, Cameras, 3D-TOF imaging, Time of flight cameras, Super resolution, Image resolution, Image processing, 3D metrology, Image sensors
Time-of-flight (ToF) measurement sensor is widely used to measure 3D depth. However, conventional ToF cameras has relatively low resolution compared to the RGB camera. To utilize such depth image of low resolution effectively in various research fields, low resolution depth image of ToF sensor should be increased. Meanwhile, ToF sensor also has problem related saturated pixels and missing pixels. A novel depth completion algorithm is proposed in this paper to improve the 3D depth image of ToF camera in terms of image resolution and abnormal pixels. Specifically, low resolution depth images and relatively high resolution RGB images are fused in machine learning architecture. The performance of this proposed depth completion algorithm is demonstrated under various experimental conditions.
Amplitude-modulated continuous wave (AMCW) time-of-flight (ToF) sensor is widely used to capture 3D information of objects due to its relatively high measurement precision in short range. However, the measurement accuracy of AMCW ToF measurement method is generally sensitive to the reflectivity of object, internal stray light, modulation instability, and external light. Consequently, distance measurement error inevitably occurs even in indoor measurement condition. To compensate such error, a post processing method based on machine learning is proposed in this paper. This data driven correction method is validated under indoor measurement condition. According to the experimental results, the distance measurement error correction method presented in this paper shows the most high accuracy compared to other related research results.
With the increasing demand for 3D depth information in various industrial applications, light detection and ranging (LiDAR) has been emerged as one of the solutions to measure the distance of objects. However, existing AMCW-based indirect ToF sensors have problems with measurement accuracy since the measured depth is sensitive to unwanted error sources such as ambient light, wide-band random noises, and stray light. In this paper, the effects of such stray light i.e. systematic error source are thoroughly analyzed in a cause-and-effect manner in terms of the signal’s amplitude and measured phase changes. Furthermore, a pre-compensation method to remove the effects of stray light is validated under various practical experimental conditions. According to the experimental results, the proposed pre-compensation method improves the measurement accuracy with mm-level depth error.
KEYWORDS: Demodulation, Parallel computing, LIDAR, Signal detection, Signal processing, Sensors, Image processing, Digital signal processing, Digital Light Processing, Avalanche photodiodes
Light detection and ranging (LIDAR) is one of solutions to extract 3D depth image of objects. Especially, as fabrication process of silicon image sensor has been progressed, amplitude-modulated continuous wave (AMCW) time-of-flight (ToF) cameras have been widely used due to their relatively cheap price and high measurement accuracy. However, to estimate ToF, reflected laser signal should be sequentially demodulated using optoelectronic device such as demodulation pixel, which induces relatively long integration and processing times. Additionally, due to the limitation of demodulation contrast and optical fill factor of demodulation pixel, depth measurement accuracy is also limited. To cope with such shortcomings of conventional ToF cameras, a novel AMCW method based on parallel phase-demodulation and related scanning LIDAR prototype are demonstrated in this paper. This scanning AMCW LIDAR prototype has enabled scanning object in extremely short integration time with adaptable field of view (FoV) and resolution to extract precise 3D depth images.
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