Being different from the most methods of guided depth map enhancement based on deep convolutional neural network which focus on increasing the depth of networks, this paper is to improve the effectiveness of intensity guidance when the network goes deep. Overall, the proposed network upsamples the low-resolution depth maps from coarse to fine. Within each refinement stage of certain-scale depth features, the current-scale and all coarse-scales of the guidance features are revisited by dense connection. Therefore, the multi-scale guidance is efficiently maintained as the propagation of features. Furthermore, the proposed network maintains the intensity features in the high-resolution domain from which the multi-scale guidance is directly extracted. This design further improves the quality of intensity guidance. In addition, the shallow depth features upsampled via transposed convolution layer are directly transferred to the final depth features for reconstruction, which is called global residual learning in feature domain. Similarly, the global residual learning in pixel domain learns the difference between the depth ground truth and the coarsely upsampled depth map. Also, the local residual learning is to maintain the low frequency within each refinement stage and progressively recover the high frequency. The proposed method is tested for noise-free and noisy cases which compares against 16 state-of-the-art methods. Our experimental results show the improved performances based on the qualitative and quantitative evaluations.

中文翻译：

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通过在粗到细阶段重新审视多尺度强度指导来增强深度图

与大多数基于深度卷积神经网络的引导深度图增强方法侧重于增加网络深度不同，本文旨在提高网络深度时强度引导的有效性。总体而言，所提出的网络将低分辨率深度图从粗到细上采样。在特定尺度深度特征的每个细化阶段，通过密集连接重新访问引导特征的当前尺度和所有粗尺度。因此，多尺度引导作为特征的传播被有效地维护。此外，所提出的网络在高分辨率域中保持强度特征，从中直接提取多尺度引导。这种设计进一步提高了强度引导的质量。此外，将通过转置卷积层上采样的浅深度特征直接转移到最终的深度特征进行重构，这在特征域中称为全局残差学习。类似地，像素域中的全局残差学习学习深度地面实况和粗略上采样的深度图之间的差异。此外，局部残差学习是在每个细化阶段保持低频并逐步恢复高频。所提出的方法针对无噪声和有噪声的情况进行了测试，与 16 种最先进的方法进行了比较。我们的实验结果显示了基于定性和定量评估的改进性能。这被称为特征域中的全局残差学习。类似地，像素域中的全局残差学习学习深度地面实况和粗略上采样的深度图之间的差异。此外，局部残差学习是在每个细化阶段保持低频并逐步恢复高频。所提出的方法针对无噪声和有噪声的情况进行了测试，与 16 种最先进的方法进行了比较。我们的实验结果显示了基于定性和定量评估的改进性能。这被称为特征域中的全局残差学习。类似地，像素域中的全局残差学习学习深度地面实况和粗略上采样的深度图之间的差异。此外，局部残差学习是在每个细化阶段保持低频并逐步恢复高频。所提出的方法针对无噪声和有噪声的情况进行了测试，与 16 种最先进的方法进行了比较。我们的实验结果显示了基于定性和定量评估的改进性能。局部残差学习是在每个细化阶段保持低频并逐步恢复高频。所提出的方法针对无噪声和有噪声的情况进行了测试，与 16 种最先进的方法进行了比较。我们的实验结果显示了基于定性和定量评估的改进性能。局部残差学习是在每个细化阶段保持低频并逐步恢复高频。所提出的方法针对无噪声和有噪声的情况进行了测试，与 16 种最先进的方法进行了比较。我们的实验结果显示了基于定性和定量评估的改进性能。