  The use of RGB-D information for salient object detection has been
extensively explored in recent years. However, relatively few efforts have been
put towards modeling salient object detection in real-world human activity
scenes with RGBD. In this work, we fill the gap by making the following
contributions to RGB-D salient object detection. (1) We carefully collect a new
SIP (salient person) dataset, which consists of ~1K high-resolution images that
cover diverse real-world scenes from various viewpoints, poses, occlusions,
illuminations, and backgrounds. (2) We conduct a large-scale (and, so far, the
most comprehensive) benchmark comparing contemporary methods, which has long
been missing in the field and can serve as a baseline for future research. We
systematically summarize 32 popular models and evaluate 18 parts of 32 models
on seven datasets containing a total of about 97K images. (3) We propose a
simple general architecture, called Deep Depth-Depurator Network (D3Net). It
consists of a depth depurator unit (DDU) and a three-stream feature learning
module (FLM), which performs low-quality depth map filtering and cross-modal
feature learning respectively. These components form a nested structure and are
elaborately designed to be learned jointly. D3Net exceeds the performance of
any prior contenders across all five metrics under consideration, thus serving
as a strong model to advance research in this field. We also demonstrate that
D3Net can be used to efficiently extract salient object masks from real scenes,
enabling effective background changing application with a speed of 65fps on a
single GPU. All the saliency maps, our new SIP dataset, the D3Net model, and
the evaluation tools are publicly available at
https://github.com/DengPingFan/D3NetBenchmark.
