Self-supervised learning of depth from monocular videos has recently drawn attention as it has notable advantages over supervised ones in a training framework. We propose a self-supervised monocular depth estimation method with a set of improvements, which together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods. Our architecture amends current deep convolutional neural network backbone combined with attention mechanism to boost depth estimation performance. Additionally, for addressing moving objects and occlusion, we propose a learnable outlier-masking technique to exclude invalid pixels in photometric error map. Extensive experiments show the effectiveness of the proposed improvements. Our proposed model achieves state-of-the-art performance on KITTI dataset compared with other competing methods.

中文翻译：

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MonodepthPlus：使用软注意力和可学习的离群值掩盖的自我监督式单眼深度估计

单眼视频对深度的自我监督学习最近引起了人们的注意，因为它在训练框架中比有监督的视频具有明显的优势。我们提出了一种具有一系列改进的自监督式单眼深度估计方法，与竞争性自监督式方法相比，这些方法共同导致了定量和质量上改进的深度图。我们的架构修改了目前的深度卷积神经网络主干网，并结合了注意力机制来提高深度估计性能。此外，为解决运动物体和遮挡问题，我们提出了一种可学习的离群值遮罩技术，以排除光度误差图中的无效像素。大量的实验证明了所提出的改进的有效性。