Monocular unsupervised depth estimation method based on context attention mechanism

Abstract

The invention discloses a monocular unsupervised depth estimation method based on a context attention mechanism, and belongs to the field of image processing and computer vision. According to the method, a depth estimation method based on a hybrid geometry enhancement loss function and a context attention mechanism is adopted, and a depth estimation sub-network, an edge sub-network and a camera attitude estimation sub-network based on a convolutional neural network are adopted to obtain a high-quality depth map. The system is easy to construct, and a corresponding high-quality depth map is obtained from a monocular video in an end-to-end mode by using a convolutional neural network; the program framework is easy to realize. According to the method, the depth information is solved by usingan unsupervised method, the problem that real data is difficult to obtain in a supervised method is avoided, and the algorithm operation speed is high. According to the method, the depth information is solved by using the monocular video, namely the monocular picture sequence, so the problem that the stereoscopic picture pair is difficult to obtain when the monocular picture depth information is solved by using the stereoscopic picture pair is avoided.

Description

technical field [0001] The invention belongs to the field of image processing and computer vision, and relates to a depth estimation sub-network based on a convolutional neural network, an edge sub-network and a camera pose estimation sub-network to jointly obtain a high-quality depth map. Specifically, it involves a monocular unsupervised depth estimation method based on a contextual attention mechanism. Background technique [0002] At this stage, depth estimation, as a basic research task in the field of computer vision, has a wide range of applications in the fields of object detection, automatic driving, and simultaneous localization and map construction. Depth Estimation Especially Monocular Depth Estimation In the absence of geometric constraints and other prior knowledge, predicting a depth map from a single image is an extremely ill-posed problem. So far, deep learning-based monocular depth estimation methods are mainly divided into two categories: supervised metho...

AI Technical Summary

Problems solved by technology

Although the current unsupervised loss function is simple in form, its disadvantage is that it cannot guarantee the sharpness of depth edges and the integrity of the fine structure of the depth map, especially in occluded and low-textured areas, resulting in poor-quality depth estimation maps.

In addition, the current monocular depth estimation method based on deep learning usually cannot obtain the correlation between long-range features, so that better feature expression cannot be obtained, resulting in the loss of details in the estimated depth map.

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