Salient object detection (SOD) is a crucial and preliminary task for many
computer vision applications, which have made progress with deep CNNs. Most of
the existing methods mainly rely on the RGB information to distinguish the
salient objects, which faces difficulties in some complex scenarios. To solve
this, many recent RGBD-based networks are proposed by adopting the depth map as
an independent input and fuse the features with RGB information. Taking the
advantages of RGB and RGBD methods, we propose a novel depth-aware salient
object detection framework, which has following superior designs: 1) It only
takes the depth information as training data while only relies on RGB
information in the testing phase. 2) It comprehensively optimizes SOD features
with multi-level depth-aware regularizations. 3) The depth information also
serves as error-weighted map to correct the segmentation process. With these
insightful designs combined, we make the first attempt in realizing an unified
depth-aware framework with only RGB information as input for inference, which
not only surpasses the state-of-the-art performances on five public RGB SOD
benchmarks, but also surpasses the RGBD-based methods on five benchmarks by a
large margin, while adopting less information and implementation
light-weighted. The code and model will be publicly available.

提出了一种新颖的深度感知显著目标检测框架，采用多层深度感知规则全面优化 SOD 特征，并将深度信息作为误差加权映射来修正分割过程，该框架在利用 RGB 信息作为输入进行推断时优于现有的 RGB SOD 方法和 RGBD 方法的性能表现，并具有轻量级的实现方式。