A monocular image depth estimation method and system

Abstract

The present invention relates to a monocular image depth estimation method and system, including: constructing a CNN model architecture with only a feature ingestion part as a feature ingestion architecture; setting multiple fully connected layers according to the resolution of the required depth map; connecting the fully connected layers To the feature ingestion architecture to form a primary estimation network model; use the training data set to train the primary estimation network model, and extract the feature maps of each layer of the primary estimation network model; calculate the average relative local geometric structure error of the feature map and its corresponding depth map, and Generate a final estimation model based on the average relative local geometric structure error; use the training data set and combine the loss function to train the final estimation model, and use the final estimation model after training to perform depth prediction on the input image. The present invention trains the CNN based on the average relative local geometric structure error and the loss function, thereby improving the accuracy and definition of the CNN regression depth image, and the generated depth image retains more geometric structure features of the scene.

Description

technical field [0001] The present invention relates to the technical field of 3D image depth estimation, in particular to a monocular image depth estimation method and system. Background technique [0002] At present, most computer vision technologies are proposed on the basis of two-dimensional plane images. However, the image loses part of the information of the real three-dimensional scene during the imaging process, resulting in some inherent defects in some computer vision technologies based on two-dimensional plane images. , For example: the algorithm's understanding of the scene is wrong, and the object recognition is wrong. Therefore, it is a very important technology to extract depth information from two-dimensional plane images or video sequences to predict depth images and reconstruct three-dimensional structures. If the problem of depth estimation can be solved well, it will be of great help to computer vision technology, and it will greatly promote the applica...

AI Technical Summary

Problems solved by technology

Depth estimation algorithms based on SfM technology can usually restore higher-precision scene depth information, but their disadvantages are: subject to special scenes, camera motion must exist; when there are moving objects in the scene, the accuracy of depth calculation It also has a great impact; due to the need to solve the internal and external parameters of the camera, the speed of depth estimation is relatively slow

[0008] In the existing methods of directly estimating depth images based on CNN, a part of the depth cues related to the geometric structure of the image will be lost in the CNN regression process, and the lack of good use of these depth cues leads to the accuracy and clarity of the depth images directly predicted by CNN. limited in degree

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