  Camouflaged object detection (COD) aims to segment camouflaged objects hiding
in the environment, which is challenging due to the similar appearance of
camouflaged objects and their surroundings. Research in biology suggests depth
can provide useful object localization cues for camouflaged object discovery.
In this paper, we study the depth contribution for camouflaged object
detection, where the depth maps are generated with existing monocular depth
estimation (MDE) methods. Due to the domain gap between the MDE dataset and our
COD dataset, the generated depth maps are not accurate enough to be directly
used. We then introduce two solutions to avoid the noisy depth maps from
dominating the training process. Firstly, we present an auxiliary depth
estimation branch ("ADE"), aiming to regress the depth maps. We find that "ADE"
is especially necessary for our "generated depth" scenario. Secondly, we
introduce a multi-modal confidence-aware loss function via a generative
adversarial network to weigh the contribution of depth for camouflaged object
detection. Our extensive experiments on various camouflaged object detection
datasets explain that the existing "sensor depth" based RGB-D segmentation
techniques work poorly with "generated depth", and our proposed two solutions
work cooperatively, achieving effective depth contribution exploration for
camouflaged object detection.
