  Benefiting from color independence, illumination invariance and location
discrimination attributed by the depth map, it can provide important
supplemental information for extracting salient objects in complex
environments. However, high-quality depth sensors are expensive and can not be
widely applied. While general depth sensors produce the noisy and sparse depth
information, which brings the depth-based networks with irreversible
interference. In this paper, we propose a novel multi-task and multi-modal
filtered transformer (MMFT) network for RGB-D salient object detection (SOD).
Specifically, we unify three complementary tasks: depth estimation, salient
object detection and contour estimation. The multi-task mechanism promotes the
model to learn the task-aware features from the auxiliary tasks. In this way,
the depth information can be completed and purified. Moreover, we introduce a
multi-modal filtered transformer (MFT) module, which equips with three
modality-specific filters to generate the transformer-enhanced feature for each
modality. The proposed model works in a depth-free style during the testing
phase. Experiments show that it not only significantly surpasses the
depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts
a high-quality depth map and salient contour at the same time. And, the
resulted depth map can help existing RGB-D SOD methods obtain significant
performance gain. The source code will be publicly available at
https://github.com/Xiaoqi-Zhao-DLUT/MMFT.
