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This paper describes a novel, fast template-matching technique, referred to as bounded partial correlation (BPC), based on the normalised cross-correlation (NCC) function. The technique consists in checking at each search position a suitable elimination condition relying on the evaluation of an upper-bound for the NCC function. The check allows for rapidly skipping the positions that cannot provide a better degree of match with respect to the current best-matching one. The upper-bounding function incorporates partial information from the actual cross-correlation function and can be calculated very efficiently using a recursive scheme. We show also a simple improvement to the basic BPC formulation that provides additional computational benefits and renders the technique more robust with respect to the parameters choice. [ABSTRACT FROM AUTHOR]

Integrating an RGB camera into a ToF imaging system has become a significant technique for perceiving the real world. The RGB guided ToF imaging system is crucial to several applications, including face anti-spoofing, saliency detection, and trajectory prediction. Depending on the distance of the working range, the implementation schemes of the RGB guided ToF imaging systems are different. Specifically, ToF sensors with a uniform field of illumination, which can output dense depth but have low resolution, are typically used for close-range measurements. In contrast, LiDARs, which emit laser pulses and can only capture sparse depth, are usually employed for long-range detection. In the two cases, depth quality improvement for RGB guided ToF imaging corresponds to two sub-tasks: guided depth super-resolution and guided depth completion. In light of the recent significant boost to the field provided by deep learning, this paper comprehensively reviews the works related to RGB guided ToF imaging, including network structures, learning strategies, evaluation metrics, benchmark datasets, and objective functions. Besides, we present quantitative comparisons of state-of-the-art methods on widely used benchmark datasets. Finally, we discuss future trends and the challenges in real applications for further research. [ABSTRACT FROM AUTHOR]