A 3D Visualization Method of Coverage Based on Fast Estimation of Camera Pose

Abstract

The invention provides a 3D (three-dimensional) visualization method for a coverage range based on the quick estimation of the attitude of a camera, which comprises the following steps: carrying out 3D scene modeling so as to obtain a 3D scene model, carrying out enhancement on the 3D scene model, and in the process of 3D scene modeling, restoring a depth image from each frame of a video; according to the 3D scene model and the installation position of the camera, registering the camera to the 3D scene model, obtaining camera parameters through estimation according to the attitude of the camera, and determining a projection mapping relation between the video and the 3D scene model; and carrying out displaying in the 3D scene model according to a depth shadow texture principle, interactively picking up a target in the 3D scene model, and according to the projection mapping target, calculating an imaging area of the target in the current camera parameter mode.

Description

technical field [0001] The invention relates to the technical fields of computer vision and augmented reality, in particular to a method for quickly estimating camera pose and 3D visualization of coverage. Background technique [0002] With the continuous development of computer graphics and computer vision, it has become a reality to use computer technology to efficiently and realistically simulate the real world interactively. Augmented reality technology is more and more widely used in the real world, and its role is becoming more and more important. The increase of a large number of cameras not only brings massive video data, but also puts forward higher requirements for scientific and efficient resource management. [0003] The existing camera pose estimation mostly adopts the method of camera calibration and feature tracking. Camera calibration is widely used in desktop cameras, robotics, and industrial control. However, in the security field based on the augmented ...

AI Technical Summary

Problems solved by technology

Existing calibration methods are based on precise calibration objects, or require complex attitude control operations on the camera to calculate camera parameters

In many applications, these calibration objects cannot be placed on site, and attitude control operations cannot be performed on the bolt camera, which leads to situations where traditional methods cannot be applied

[0005] 2. The coverage calculation is not accurate and intuitive enough

The calibration method based on active vision needs to control the camera to do some special movements. The algorithm is simple but cannot be applied to occasions where the camera movement is unknown or uncontrollable

The camera self-calibration method uses the camera internal parameter constraints to solve the equation. The calibration process has nothing to do with the scene and camera movement, and the application is flexible, but the accuracy is low.

Images