A Spherical Volume-Rendering Method of Ocean Scalar Data Based on Adaptive Ray Casting

Abstract

There are some limitations in traditional ocean scalar field visualization methods, such as inaccurate expression and low efficiency in the three-dimensional digital Earth environment. This paper presents a spherical volume-rendering method based on adaptive ray casting to express ocean scalar field. Specifically, the minimum bounding volume based on spherical mosaic is constructed as the proxy geometry, and the depth texture of the seabed terrain is applied to determine the position of sampling points in the spatial interpolation process, which realizes the fusion of ocean scalar field and seabed terrain. Then, we propose an adaptive sampling step algorithm according to the heterogeneous depth distribution and data change rate of the ocean scalar field dataset to improve the efficiency of the ray-casting algorithm. In addition, this paper proposes a nonlinear color-mapping enhancement scheme based on the skewness characteristics of the datasets to optimize the expression effect of volume rendering, and the transparency transfer function is designed to realize volume rendering and local feature structure extraction of ocean scalar field data in the study area.

Dynamic typhoon visualization based on the integration of vector and scalar fields

“…To accelerate the calculation speed of the ray casting method and improve its visualization effect, scholars at home and abroad have carried out in-depth research and improvement on it. Lee et al ( 2010) proposed a fast ray casting algorithm that uniformly reconstructs the intensity values using a high-order convolution filter as the rays pass through the volume data, which reduces the number of sampling points as well as the computation steps for the color of the sampling points, and increases the sampling rate (Zanarini et al, 1998) proposed an optimization algorithm for the early cutoff of opacity, where the opacity iterates to 1 when the opacity stops subsequent sampling, eliminating invalid sampling and increasing sampling efficiency (Li et al, 2023) proposed an adaptive step-size sampling algorithm that arranges each layer in volume data according to its depth, shrinks the sampling step size for denser regions of data, increases the sampling points to obtain detailed information about the region of data changes, and increases the sampling step size for sparse regions of data to accelerate the sampling step. Chi (2012) proposed an adaptive multiple pre-integrated volume rendering algorithm.…”

“…Since the proposal of scientific computing visualization, it has been widely used and developed in many fields such as geography, medicine, architecture, etc (Kehrer and Hauser, 2013), the scientific computing visualization of typhoon data presents the typhoon observation data and the data obtained in the process of scientific computation on the screen in the form of images or graphs (Zirui, 2020), which makes it convenient for researchers to interact, process, and analyze. The multi-dimensional, large-scale, complex structure and other characteristics of the current typhoon data make the traditional two-dimensional visualization methods not able to meet the demand for accurate expression of the typhoon, the need to take three-dimensional dynamic visualization techniques, such as volume data drawing, three-dimensional particle system, etc., the use of graphics processing unit(GPU) acceleration to improve the efficiency of the visualization of data (Li et al, 2023), to be able to characterize the morphology of the typhoon in a threedimensional environment and its spatial-temporal evolution of the law. Dissecting the internal structure of typhoon data (Hao, 2020), predicting the movement and influence range of the typhoon, facilitating meteorologists to summarize the meteorological laws of the current typhoon, assisting in the decision-making of typhoon warning and personnel evacuation, and contributing to the cause of disaster prevention and mitigation.…”