RGB-D salient object detection:A survey

• 作者: Tao Zhou Deng-Ping Fan Ming-Ming Cheng Jianbing Shen Ling Shao
• 来源期刊: 计算可视媒体（英文版）
• 年，卷（期）: 2021,（1）
• 所属期刊栏目: REVIEW ARTICLE
• 页码范围: 37-69
• 页数: 33页
• 语种: 英文

摘要

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.