Monocular depth estimation is an especially important task in robotics and autonomous driving, where 3D structural information is essential. However, extreme lighting conditions and complex surface objects make it difficult to predict depth in a single image. Therefore, to generate accurate depth maps, it is important for the model to learn structural information about the scene. We propose a novel Patch-Wise EdgeConv Module (PEM) and EdgeConv Attention Module (EAM) to solve the difficulty of monocular depth estimation. The proposed modules extract structural information by learning the relationship between image patches close to each other in space using edge convolution. Our method is evaluated on two popular datasets, the NYU Depth V2 and the KITTI Eigen split, achieving state-of-the-art performance. We prove that the proposed model predicts depth robustly in challenging scenes through various comparative experiments.

翻译：在3D结构信息至关重要的机器人和自主驱动中,单心深度估算是一项特别重要的任务。然而,极端的照明条件和复杂的表面物体使得难以在单一图像中预测深度。因此,为了生成准确的深度地图,模型必须了解场景的结构信息。我们提出了一个新的补丁-电磁共振模块(PEM)和电磁共振关注模块(EAM),以解决单心深度估算的难度。拟议模块通过学习图像在使用边缘共变的宇宙中彼此相近的关系来提取结构信息。我们的方法在两个流行数据集(NYU深度V2和KITTI Eigen)上进行了评估,实现了最先进的性能。我们证明拟议的模型通过各种比较实验在具有挑战性的场景中强有力地预测了深度。