Abstract

Recently, with the rapid growth of manufacture and ease of user convenience, technologies utilizing virtual reality images have been increasing. It is very important to estimate the projected direction and position of the image to show the image quality similar to the real world, and the estimation of the direction and the position is solved using the relation that transforms the spheres into the expanded equirectangular. The transformation relationship can be divided into a camera intrinsic parameter and a camera extrinsic parameter, and all the images have respective camera parameters. Also, if several images use the same camera, the camera intrinsic parameters of the images will have the same values. However, it is not the best way to set the camera intrinsic parameter to the same value for all images when matching images. To solve these problems and show images that does not have a sense of heterogeneity, it is needed to create the cost function by modeling the conversion relation and calculate the camera parameter that the residual value becomes the minimum. In this article, we compare and analyze efficient camera parameter update methods. For comparative analysis, we use Levenberg–Marquardt, a parameter optimization algorithm using corresponding points, and propose an efficient camera parameter update method based on the analysis results.

Highlights

  • Up to now, the development of image processing has focused on quality ranging from high definition and full high definition to ultrahigh definition

  • We propose an efficient parameter updating method of images in 360-degree virtual reality (VR) system

  • Even though there are initial field of view (Fov) and initial lens K2 values, there is a problem that the values of Fov and lens K2 greatly change in the optimization process when the corresponding points are densified or linear

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Summary

Introduction

The development of image processing has focused on quality ranging from high definition and full high definition to ultrahigh definition. The 360-degree VR system is a shooting technique that allows viewing of 360 degrees by stitching images that are captured with multiple cameras. It can solve the limitations of images produced by a single camera and is widely analyzed in various fields such as computer vision and computer graphics.[1,2] since 360-degree VR technology is an image taken from different cameras, it is difficult to perfectly match overlapping parts. To show quality similar to the real world, estimation of the projected direction and location of an image is a very important part in image-based 360-degree VR systems. The estimation of the direction and the position is solved using a relation of converting the sphere into an equirectangular with a ratio of 2:1.3 Because 360-degree VR is shot with

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