Research on the Application of Environmental Art Design Based on the Combination of VR and Panoramic Video Technology

The panoramic video technology is introduced to collect multiangle data of design objects, draw a 3D spatial model with the collected data, solve the first-order differential equation for the 3D spatial model, obtain the spatial positioning extremes of the object scales, and realize the alignment and fusion of panoramic video images according to the positioning extremes above and below the scale space. Then, the panoramic video is generated and displayed by computer processing so that the tourist can watch the scene with virtual information added to the panoramic video by wearing the display device elsewhere. It solves the technical difficulties of the high complexity of the algorithm in the system of panoramic video stitching and the existence of stitching cracks and the “GHOST” phenomenon in the stitched video, as well as the technical difficulties that the 3D registration is easily affected by the time-consuming environment and target tracking detection algorithm. The simulation results show that the panoramic video stitching method performs well in real time and effectively suppresses stitching cracks and the “GHOST” phenomenon, and the augmented reality 3D registration method performs well for the local enhancement of the panoramic video.

Multi-Frame Based Homography Estimation for Video Stitching in Static Camera Environments

In this paper, a multi-frame based homography estimation method is proposed for video stitching in static camera environments. A homography that is robust against spatio-temporally induced noise can be estimated by intervals, using feature points extracted during a predetermined time interval. The feature point with the largest blob response in each quantized location bin, a representative feature point, is used for matching a pair of video sequences. After matching representative feature points from each camera, the homography for the interval is estimated by random sample consensus (RANSAC) on the matched representative feature points, with their chances of being sampled proportional to their numbers of occurrences in the interval. The performance of the proposed method is compared with that of the per-frame method by investigating alignment distortion and stitching scores for daytime and noisy video sequence pairs. It is shown that alignment distortion in overlapping regions is reduced and the stitching score is improved by the proposed method. The proposed method can be used for panoramic video stitching with static video cameras and for panoramic image stitching with less alignment distortion.