RGB-D Salient Object Detection: A Survey
- URL: http://arxiv.org/abs/2008.00230v4
- Date: Thu, 14 Jul 2022 11:47:17 GMT
- Title: RGB-D Salient Object Detection: A Survey
- Authors: Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng, Jianbing Shen, and Ling Shao
- Abstract summary: We provide a comprehensive survey of RGB-D based SOD models from various perspectives.
We also review SOD models and popular benchmark datasets from this domain.
We discuss several challenges and open directions of RGB-D based SOD for future research.
- Score: 195.83586883670358
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Salient object detection (SOD), which simulates the human visual perception
system to locate the most attractive object(s) in a scene, has been widely
applied to various computer vision tasks. Now, with the advent of depth
sensors, depth maps with affluent spatial information that can be beneficial in
boosting the performance of SOD, can easily be captured. Although various RGB-D
based SOD models with promising performance have been proposed over the past
several years, an in-depth understanding of these models and challenges in this
topic remains lacking. In this paper, we provide a comprehensive survey of
RGB-D based SOD models from various perspectives, and review related benchmark
datasets in detail. Further, considering that the light field can also provide
depth maps, we review SOD models and popular benchmark datasets from this
domain as well. Moreover, to investigate the SOD ability of existing models, we
carry out a comprehensive evaluation, as well as attribute-based evaluation of
several representative RGB-D based SOD models. Finally, we discuss several
challenges and open directions of RGB-D based SOD for future research. All
collected models, benchmark datasets, source code links, datasets constructed
for attribute-based evaluation, and codes for evaluation will be made publicly
available at https://github.com/taozh2017/RGBDSODsurvey
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