  RGB-D salient object detection (SOD) recently has attracted increasing
research interest and many deep learning methods based on encoder-decoder
architectures have emerged. However, most existing RGB-D SOD models conduct
feature fusion either in the single encoder or the decoder stage, which hardly
guarantees sufficient cross-modal fusion ability. In this paper, we make the
first attempt in addressing RGB-D SOD through 3D convolutional neural networks.
The proposed model, named RD3D, aims at pre-fusion in the encoder stage and
in-depth fusion in the decoder stage to effectively promote the full
integration of RGB and depth streams. Specifically, RD3D first conducts
pre-fusion across RGB and depth modalities through an inflated 3D encoder, and
later provides in-depth feature fusion by designing a 3D decoder equipped with
rich back-projection paths (RBPP) for leveraging the extensive aggregation
ability of 3D convolutions. With such a progressive fusion strategy involving
both the encoder and decoder, effective and thorough interaction between the
two modalities can be exploited and boost the detection accuracy. Extensive
experiments on six widely used benchmark datasets demonstrate that RD3D
performs favorably against 14 state-of-the-art RGB-D SOD approaches in terms of
four key evaluation metrics. Our code will be made publicly available:
https://github.com/PPOLYpubki/RD3D.
