5/10/2023 0 Comments Comp to nighttone modeThis paper introduces a new approach to accomplish reality real-time outdoor rendering by considering the relation between items in AR regarding shadows in any place during daylight. However, the issue remains an active research topic, especially in outdoor rendering. Several of these approaches are not dealt with real-time rendering. A few realistic rendering approaches were built to overcome this issue. Realism items in outdoor AR need advanced impacts like shadows, sunshine, and relations between unreal items. Realism rendering methods of outdoor augmented reality (AR) is an interesting topic. Finally, the results are discussed in detail. Especially in field experiment, it is very difficult to estimate 3D attitude. However, when there are measurement noise or model error, the accuracy of 3D attitude estimation drops significantly. The results of simulation show that the SSO algorithm can estimate 3D attitude and the established sky model contains 3D attitude information. So, a sky model is established, which combines Berry model and Hosek model to fully describe AOP, DOP, and LI information in the sky, and considers the influence of four neutral points, ground albedo, atmospheric turbidity, and wavelength. In addition, to explore this problem, we not only use angle of polarization (AOP) and degree of polarization (DOP) information, but also the light intensity (LI) information. So, in this paper, a social spider optimization (SSO) method is proposed to estimate three Euler angles, which considers the difference of each pixel among polarization images based on template matching (TM) to make full use of the captured polarization information. However, it is still necessary to further explore whether the skylight polarization patterns contain 3D attitude information. Our previous work has demonstrated that Rayleigh model, which is widely used in polarized skylight navigation to describe skylight polarization patterns, does not contain three-dimensional (3D) attitude information. The frequency of the cases that exhibited differences less than 1 tenth between the observed and calculated cloud cover was 46.82%, while the frequency of cases that exhibited differences less than 2 tenths was 87.79%. The calculated cloud cover exhibited a bias of −0.28 tenths, root mean square error (RMSE) of 1.78 tenths, and a correlation coefficient of 0.91 for DROM across all cases. In addition, the red-blue ratio (RBR) threshold was determined, according to the image characteristics (RBR and luminance) using the red, green, and blue (RGB) brightness value of the area in which the solar zenith angle (SZA) was less than 80 Prior to calculating the cloud cover of ACOS, pre-processing was performed by removing surrounding obstacles and correcting the distortion caused by the fish-eye lens. Annual and seasonal analyses were conducted using the 1900-0600 local standard time (LST) hourly data from January to December 2019. An Automatic Cloud Observation System (ACOS) and cloud cover calculation algorithm were developed to calculate the cloud cover at night, and the calculation results were compared with the cloud cover data of a manned observatory (Daejeon Regional Office of Meteorology, DROM) that records human observations.
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