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.