Monocular depth estimation, which provides a critical method for understanding 3D scene geometry, is an ill-posed problem. Recent research studies have achieved significant progress by reliable network architecture and optimized constraints, such as spatial propagation network and depth metrics. We propose an effective generative adversarial network for fast and accurate monocular depth estimation. Our approach demonstrates the feasibility of applying a dense-connected UNet for reducing information transmission loss and then fine-tuning the blur depth by the high-order convolutional spatial propagation network (CSPN) that used a modified loss function of discriminator. Furthermore, we modify the loss function of discriminator by adding the correlation loss that is used to measure the similarity of real and fake labels. Compared with the original CSPN, the high-order CSPN reduces the computation complexity and accelerates the convergence of the generator network by increasing the order of kernel, which emphasizes the weight of kernels in the update formula. With these modifications, our generative adversarial second-order convolutional spatial propagation network (GA-CSPN) achieves more accurate results against state-of-the-art methods in both indoor and outdoor scenes on Make3D, KITTI 2015, and NYUv2 datasets.

中文翻译：

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GA-CSPN：具有二阶卷积空间传播网络的生成对抗性单目深度估计

单目深度估计为理解 3D 场景几何提供了一种关键方法，是一个不适定的问题。最近的研究通过可靠的网络架构和优化的约束（例如空间传播网络和深度度量）取得了重大进展。我们提出了一种有效的生成对抗网络，用于快速准确的单目深度估计。我们的方法证明了应用密集连接的 UNet 来减少信息传输损失，然后通过使用改进的鉴别器损失函数的高阶卷积空间传播网络 (CSPN) 微调模糊深度的可行性。此外，我们通过添加用于衡量真假标签相似度的相关损失来修改判别器的损失函数。与原来的CSPN相比，高阶CSPN通过增加核的阶数来降低计算复杂度并加速生成器网络的收敛，强调了更新公式中核的权重。通过这些修改，我们的生成对抗二阶卷积空间传播网络 (GA-CSPN) 在 Make3D、KITTI 2015 和 NYUv2 数据集的室内和室外场景中与最先进的方法相比获得了更准确的结果。