The development of machine vision field has made the use of visual methods to solve the problem of deep extraction a major topic in computer vision research. The scene image acquired by the monocular vision system is the projection of the 3D space in the 2D plane. The depth information is lost during this transformation. In turn

the process of extracting depth from 2D images involves the acquisition of monocular depth information. The acquisition of depth information based on monocular vision is the least costly and the most difficult means of non-contact 3D measurement technology. For a long time

the basic method is to analyze the surface brightness of the object under different light sources by use of the brightness equation to solve the surface normal and the 3D reconstruction of the surface of the object. Contrary to the proposed method

photometric techniques typically require multiple light sources

which are generally limited to a wide range of scenarios. The use of photometric 3D technology for 3D reconstruction of the surface of a single object to restore the effect is accurate. Thus

most existing photometric stereoscopic techniques assume that incident light is parallel to light and that light intensity does not change with distance to simplify the calculation process of the surface normal. To achieve this condition

the actual application must be light intensity

distance light source

or a large area of the array of light sources; such algorithm is used in the light source near the point of light (i.e.

the light intensity) to meet the distance of the square inverse decay (i.e.

in line with the actual situation) and achieve low cost. The extraction of depth information is the key technology of 3D reconstruction and virtual reality. Traditional monocular methods are computationally large that the application scenario is limited. Monocular information should be used to find a convenient way for quickly extracting the depth of a scene. In this study

we integrate photometric 3D

imaging principles

computer vision

and many other technologies for analysis. The radiance of surface of object illuminated by light source is obtained using the body surface reflection model

and the relation between the radiance of surface of object and the brightness of camera image is deduced using photometric stereo theory. The relationship between depth and change is found on the basis of the change in the brightness of image. The algorithm based on the said relationship is designed to obtain depth information. The algorithm is applied on various experimental scenarios. First

the depth value is estimated for the ladder-like object in the relatively simple scene. The actual distance is accurately measured by the scale

and the value is obtained after 2 decimal places. Then

the algorithm is used to calculate the depth value

which is after 4 decimal places

under the experimental conditions with a maximum total error of 8.6%. Results show that the maximum error of the experiment is less than 9%. The experimental conditions can be improved on the basis of the overall experimental results to achieve the desired requirements of the algorithm. Experimental results show that the proposed algorithm achieves good recovery in simple scenes and other daily scenes and that the accuracy of depth values is over 90%. In this study

depth information is estimated by the image intensity change caused by the movement of light source

thereby avoiding the complicated camera calibration process. The algorithm presents a low computational complexity and is a new method for obtaining depth information. Meanings This study provides a new idea for acquiring monocular depth information based on image brightness cues. The method is based on the analysis of the relationship between surface radiance and image brightness and uses the change in light intensity of the point light source in the process of moving to obtain the scene depth value. The method requires only three pictures for processing

has simple hardware requirements and small calculation complexity

and does not need significant edge of scene and other geometric information. However

only the preliminary principle and performance verification of the proposed depth extraction method are presented herein. In the future work

the proposed method will be improved and optimized by analyzing non-ideal light sources in the case of light reflection of object and using mixed surface reflection models to fit the surface of the non-diffused reflector.