RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods

TL;DR

This survey reviews deep learning methods for RGB guided ToF imaging, focusing on network designs, learning strategies, and benchmarks, highlighting recent advances and future challenges in depth super-resolution and completion.

Contribution

It provides a comprehensive overview of deep learning-based techniques for RGB guided ToF imaging, including analysis of network architectures, datasets, and evaluation metrics.

Findings

Deep learning significantly enhances depth quality in RGB guided ToF systems.

State-of-the-art methods show improved performance on benchmark datasets.

Future research directions include addressing real-world application challenges.

Abstract

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.