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Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection | | Xuehao Wang
; Shuai Li
; Chenglizhao Chen
; Aimin Hao
; Hong Qin
; | | Date: |
7 Aug 2020 | | Abstract: | Previous RGB-D salient object detection (SOD) methods have widely adopted
deep learning tools to automatically strike a trade-off between RGB and D
(depth), whose key rationale is to take full advantage of their complementary
nature, aiming for a much-improved SOD performance than that of using either of
them solely. However, such fully automatic fusions may not always be helpful
for the SOD task because the D quality itself usually varies from scene to
scene. It may easily lead to a suboptimal fusion result if the D quality is not
considered beforehand. Moreover, as an objective factor, the D quality has long
been overlooked by previous work. As a result, it is becoming a clear
performance bottleneck. Thus, we propose a simple yet effective scheme to
measure D quality in advance, the key idea of which is to devise a series of
features in accordance with the common attributes of high-quality D regions. To
be more concrete, we conduct D quality assessments for each image region,
following a multi-scale methodology that includes low-level edge consistency,
mid-level regional uncertainty and high-level model variance. All these
components will be computed independently and then be assembled with RGB and D
features, applied as implicit indicators, to guide the selective fusion.
Compared with the state-of-the-art fusion schemes, our method can achieve a
more reasonable fusion status between RGB and D. Specifically, the proposed D
quality measurement method achieves steady performance improvements for almost
2.0\% in general. | | Source: | arXiv, 2008.04157 | | Services: | Forum | Review | PDF | Favorites | |
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