  The existing fusion based RGB-D salient object detection methods usually
adopt the bi-stream structure to strike the fusion trade-off between RGB and
depth (D). The D quality usually varies from scene to scene, while the SOTA
bi-stream approaches are depth quality unaware, which easily result in
substantial difficulties in achieving complementary fusion status between RGB
and D, leading to poor fusion results in facing of low-quality D. Thus, this
paper attempts to integrate a novel depth quality aware subnet into the classic
bi-stream structure, aiming to assess the depth quality before conducting the
selective RGB-D fusion. Compared with the SOTA bi-stream methods, the major
highlight of our method is its ability to lessen the importance of those
low-quality, no-contribution, or even negative-contribution D regions during
the RGB-D fusion, achieving a much improved complementary status between RGB
and D.
