In the last decade, the computer vision field has seen significant progress in multimodal data fusion and learning, where multiple sensors, including depth, infrared, and visual, are used to capture the environment across diverse spectral ranges. Despite these advancements, there has been no systematic and comprehensive evaluation of fusing RGB-D and thermal modalities to date. While autonomous driving using LiDAR, radar, RGB, and other sensors has garnered substantial research interest, along with the fusion of RGB and depth modalities, the integration of thermal cameras and, specifically, the fusion of RGB-D and thermal data, has received comparatively less attention. This might be partly due to the limited number of publicly available datasets for such applications. This paper provides a comprehensive review of both, state-of-the-art and traditional methods used in fusing RGB-D and thermal camera data for various applications, such as site inspection, human tracking, fault detection, and others. The reviewed literature has been categorised into technical areas, such as 3D reconstruction, segmentation, object detection, available datasets, and other related topics. Following a brief introduction and an overview of the methodology, the study delves into calibration and registration techniques, then examines thermal visualisation and 3D reconstruction, before discussing the application of classic feature-based techniques as well as modern deep learning approaches. The paper concludes with a discourse on current limitations and potential future research directions. It is hoped that this survey will serve as a valuable reference for researchers looking to familiarise themselves with the latest advancements and contribute to the RGB-DT research field.

翻译：过去十年间，计算机视觉领域在多模态数据融合与学习方面取得了显著进展，其中深度、红外和视觉等多种传感器被用于跨不同光谱范围的环境感知。尽管取得了这些进展，但目前尚无针对RGB-D与热模态融合的系统性综合评价。虽然基于激光雷达、毫米波雷达、RGB及其他传感器的自动驾驶技术，以及RGB与深度模态的融合研究已引起广泛关注，但热成像相机的集成，特别是RGB-D与热数据的融合，受到的关注相对较少。这在一定程度上可能归因于此类应用中公开数据集的有限性。本文对用于现场检测、人体跟踪、故障检测等多种应用的RGB-D与热成像相机数据融合方法进行了全面综述，涵盖前沿与传统技术。所评文献被分类为三维重建、分割、目标检测、可用数据集及其他相关主题等专业技术领域。在简要介绍和方法概述之后，研究深入探讨了标定与配准技术，进而考察了热成像可视化与三维重建，随后讨论了基于经典特征技术与现代深度学习方法的实际应用。文章最后探讨了当前局限性与潜在未来研究方向。本文期望为希望了解最新进展并投身RGB-DT研究领域的研究者提供有价值的参考。