  The high computational cost of neural networks has prevented recent successes
in RGB-D salient object detection (SOD) from benefiting real-world
applications. Hence, this paper introduces a novel network, \methodname, which
focuses on efficient RGB-D SOD by using mobile networks for deep feature
extraction. The problem is that mobile networks are less powerful in feature
representation than cumbersome networks. To this end, we observe that the depth
information of color images can strengthen the feature representation related
to SOD if leveraged properly. Therefore, we propose an implicit depth
restoration (IDR) technique to strengthen the feature representation capability
of mobile networks for RGB-D SOD. IDR is only adopted in the training phase and
is omitted during testing, so it is computationally free. Besides, we propose
compact pyramid refinement (CPR) for efficient multi-level feature aggregation
so that we can derive salient objects with clear boundaries. With IDR and CPR
incorporated, \methodname~performs favorably against \sArt methods on seven
challenging RGB-D SOD datasets with much faster speed (450fps) and fewer
parameters (6.5M). The code will be released.
