关键词: 深度估计, 单目视觉, 人工智能, 神经网络

Abstract:

Recovering depth information from monocular vision is a classical problem in the field of computer vision. The deep learning method combined with traditional algorithms is a hot research topic in recent years, but there are still limitations in algorithm fusion of neural networks, reference calibration and application scenarios. This paper proposes a two-way fusion depth estimation neural network structure, which trains the network based on the semantic information of depth gradient and depth gradient respectively, then trains the final detailed features again after feature fusion, and solves the problem of heavy workload of real reference calibration by single calibration method. The network structure can infer detailed depth information from a single RGB image. The network model is based on KITTI depth map data set training. Experiments include KITTI test set and some actual scene atlas. The results show that the method is superior to the contrast depth estimation scheme in depth information detail reconstruction and in large field of view scenario with good robustness.

Key words: depth estimation, monocular vision, artificial intelligence, neural network