Salient object detection, which simulates human visual perception in locating the most significant object(s) in a scene, has been widely applied to various computer vision tasks. Now, the advent of depth sensors means that depth maps can easily be captured; this additional spatial information can boost the performance of salient object detection. Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years, an in-depth understanding of these models and the challenges in this field remains lacking. In this paper, we provide a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail. Further, as light fields can also provide depth maps, we review salient object detection models and popular benchmark datasets from this domain too. Moreover, to investigate the ability of existing models to detect salient objects, we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models. Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research. All collected models, benchmark datasets, datasets constructed for attribute-based evaluation, and related code are publicly available at https://github.com/taozh2017/RGBD-SODsurvey.

显着对象检测模拟人类视觉感知来定位场景中最重要的对象，已广泛应用于各种计算机视觉任务。现在，深度传感器的出现意味着可以轻松捕获深度图；这些额外的空间信息可以提高显着物体检测的性能。尽管过去几年已经提出了各种具有良好性能的基于 RGB-D 的显着目标检测模型，但仍然缺乏对这些模型和该领域挑战的深入理解。在本文中，我们从各个角度对基于 RGB-D 的显着目标检测模型进行了全面的调查，并详细回顾了相关的基准数据集。此外，由于光场还可以提供深度图，我们也回顾了该领域的显着对象检测模型和流行的基准数据集。此外，为了研究现有模型检测显着目标的能力，我们对几种代表性的基于 RGB-D 的显着目标检测模型进行了基于属性的综合评估。最后，我们讨论了基于 RGB-D 的显着目标检测的几个挑战和未来研究的开放方向。所有收集的模型、基准数据集、为基于属性的评估构建的数据集以及相关代码均可在 https://github.com/taozh2017/RGBD-SODsurvey 上公开获取。