Cui, Y; Liao, Q; Yang, W; Xue, J-H; (2021) RGB Guided Depth Map Super-Resolution with Coupled U-Net. In: Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME). IEEE: Shenzhen, China. (In press).

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Abstract

The depth maps captured by RGB-D cameras usually are of low resolution, entailing recent efforts to develop depth super-resolution (DSR) methods. However, several problems remain in existing DSR methods. First, conventional DSR methods often suffer from unexpected artifacts. Secondly, high-resolution (HR) RGB features and low-resolution (LR) depth features are often fused in shallow layers only. Thirdly, only the last layer of features is used for reconstruction. To address the above problems, we propose Coupled U-Net (CU-Net), a new color image guided DSR method built on two U-Net branches for HR color images and LR depth maps, respectively. The CU-Net embeds a dual skip connection structure to leverage the feature interaction of the two branches, and a multi-scale fusion to fuse the deeper and multi-scale features of two branch decoders for more effective feature reconstruction. Moreover, a channel attention module is proposed to eliminate artifacts. Extensive experiments show that the proposed CU-Net outperforms state-of-the-art methods.

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