Depth-Quality-Aware Salient Object Detection

Chenglizhao Chen

Abstract:

The existing fusion-based RGB-D salient object
detection methods usually adopt the bistream structure to strike
a balance in the fusion trade-off between RGB and depth
(D). While the D quality usually varies among the scenes, the
state-of-the-art bistream approaches are depth-quality-unaware,
resulting in substantial difficulties in achieving complementary
fusion status between RGB and D and leading to poor fusion
results for low-quality D. Thus, this paper attempts to integrate
a novel depth-quality-aware subnet into the classic bistream
structure in order to assess the depth quality prior to conducting
the selective RGB-D fusion. Compared to the SOTA bistream
methods, the major advantage of our method is its ability
to lessen the importance of the low-quality, no-contribution,
or even negative-contribution D regions during RGB-D fusion,
achieving a much improved complementary status between RGB
and D.