A method and system for image depth estimation based on CNN and depth filter

Abstract

The invention discloses an image depth estimation method and system based on CNN and a depth filter. The method includes: obtaining an image depth estimation value based on CNN, extracting local feature points from the image, and establishing a minimum photometric error equation for the local feature points to solve Camera relative pose; optimize camera pose based on epipolar search and matching feature points, select image blocks centered on feature points, search for image block epipolar lines to get the best match, and construct beam adjustment equations to optimize relative poses based on the best match Pose: Depth value filtering based on camera pose, using Gaussian fusion to filter the depth value until the depth value converges; the invention overcomes the problem of inaccurate image depth and the problem of absolute scale loss of monocular vision, and can be used for 3D scene reconstruction , indoor positioning and augmented reality and many other fields.

Description

technical field [0001] The invention belongs to the field of three-dimensional vision, and in particular relates to an image depth estimation method and system based on a CNN (convolutional neural network) and a depth filter. Background technique [0002] Most common images in reality are color images. Color images are obtained by compressing a three-dimensional scene into a two-dimensional plane. The depth information is lost during the imaging process, and the loss of depth information makes many visual tasks difficult. For example, Due to the lack of depth values, the reconstruction of 3D scenes will be difficult. Therefore, it is of great significance to recover image depth values ​​from color images. At present, the mainstream image depth acquisition methods are divided into three categories. One is to obtain the depth value through special hardware equipment, mainly RGB-D cameras, and its principle is generally structured light or time-of-flight method. Used in robot...

AI Technical Summary

Problems solved by technology

The disadvantage is that due to its computational complexity, dense depth reconstruction is generally done offline, which takes a long time

Moreover, the motion recovery structure method has strong flaws - the loss of absolute scale and the problem that pure rotation cannot be calculated

[0005] It can be seen that due to various reasons, the existing image depth estimation technology still has problems such as narrow application range, low precision, and low efficiency.

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