PDR-Net: Progressive depth reconstruction network for color guided depth map super-resolution

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Low spatial resolution is a common problem for depth maps captured by low-cost consumer depth cameras. Depth map super-resolution (DMSR) can improve the quality of depth maps, but it is an ill-posed problem with many challenges. This paper proposes a progressive depth reconstruction network (PDR-Net) to further enhance the performance of DMSR. Specifically, we design an adaptive feature recombination module to recombine depth and color guidance features. We generate sufficient information from the recombined features with the proposed multi-scale feature fusion module, in which multi-scale feature distillation and joint attention mechanism are employed. We learn high frequency compensations for each up-interpolating and reconstruct corresponding high resolution depth maps in the proposed progressive depth reconstruction module. Experimental results with benchmark datasets verified the proposed method’s superiority over the state-of-the-art DMSR methods.