Numerous efforts have been made to design different low level saliency cues
for the RGBD saliency detection, such as color or depth contrast features,
background and color compactness priors. However, how these saliency cues
interact with each other and how to incorporate these low level saliency cues
effectively to generate a master saliency map remain a challenging problem. In
this paper, we design a new convolutional neural network (CNN) to fuse
different low level saliency cues into hierarchical features for automatically
detecting salient objects in RGBD images. In contrast to the existing works
that directly feed raw image pixels to the CNN, the proposed method takes
advantage of the knowledge in traditional saliency detection by adopting
various meaningful and well-designed saliency feature vectors as input. This
can guide the training of CNN towards detecting salient object more effectively
due to the reduced learning ambiguity. We then integrate a Laplacian
propagation framework with the learned CNN to extract a spatially consistent
saliency map by exploiting the intrinsic structure of the input image.
Extensive quantitative and qualitative experimental evaluations on three
datasets demonstrate that the proposed method consistently outperforms
state-of-the-art methods.

本文提出了一种新的卷积神经网络来融合不同的 RGBD 低级显著性线索，利用 Laplacian 传播框架提取空间一致的显著性图，并在三个数据集上进行了广泛的实验评估，结果表明这种方法始终优于现有的最先进方法。