Detailed explanation of depth estimation methods and calculation principles in computer vision

Computer vision depth estimation uses computer vision technology to estimate the distance information of objects in the image, that is, the distance of the object from the camera. Depth estimation has wide applications in fields such as autonomous driving, robot navigation, and virtual reality. This article will introduce the method of depth estimation and the process of calculating depth information.

1. Computer vision depth estimation methods

Computer vision depth estimation methods can be divided into two categories: monocular vision depth estimation and binocular visual depth estimation.

1. Monocular visual depth estimation

Monocular visual depth estimation is to estimate the depth of an object through an image. The main methods are: parallax method, structural method, and learning method.

(1) Geometry-based method: Infer the distance between the object and the camera by calculating the size, position and other geometric information of the object in the image. This method is mainly used for depth estimation in static scenes.

(2) Motion-based method: Infer the distance between the object and the camera through the motion information of the object in the image sequence. This method is mainly used for depth estimation in dynamic scenes.

(3) Deep learning-based method: Depth estimation is achieved by using a deep learning model to learn the mapping relationship between images and depth. This method has been widely used in recent years.

2. Binocular vision depth estimation

Binocular vision depth estimation is to estimate the depth information of an object through two images. The main methods of binocular vision depth estimation are as follows:

(1) Parallax-based method: infer the object by calculating the pixel position difference of the same point in the left and right images Distance from camera. This method requires image correction and matching, but has higher accuracy.

(2) Triangulation-based method: Infer the distance between the object and the camera by calculating the positions of the two cameras and the position of the object in the two images. This method requires precise camera calibration, but can obtain more accurate depth estimation results.

(3) Deep learning-based method: Depth estimation is achieved by using a deep learning model to learn the matching relationship between the left and right images. This method has also been widely used in binocular vision depth estimation.

2. How to calculate depth information

For monocular visual depth estimation, the depth information of an object can be calculated by the following formula:

D=\frac{f\times w}{p}

Where, D represents the depth of the object, f represents the focal length of the camera, w represents the actual width of the object in the image, and p represents the pixel width of the object in the image.

For binocular vision depth estimation, the depth information of the object can be calculated by the following formula:

Z=\frac{B\times f}{d}

Among them, Z represents the depth of the object, B represents the baseline length of the two cameras, f represents the focal length of the camera, and d represents the same point in the left and right images. Parallax size.

It should be noted that camera calibration and image correction are required before calculating depth information to obtain accurate camera parameters and matching relationships. At the same time, the accuracy of depth estimation is also affected by various factors, such as image quality, scene complexity, camera parameters, etc.

In general, computer vision depth estimation is a complex task that requires the comprehensive use of multiple computer vision technologies, such as feature extraction, image matching, deep learning, etc. Different depth estimation methods and calculation formulas are suitable for different scenarios and tasks. We need to choose an appropriate method for depth estimation according to the specific situation to obtain accurate depth information.

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