Abstract
With the explosive growth of scientific data, significant challenges exist with respect to the interaction of large volumetric datasets. To solve these problems, we propose a visualization algorithm based on the Hilbert R-tree improved by the clustering algorithm using K-means (CUK) and a stacked long short-term memory (LSTM) model to quickly display massive data. First, we use the Hilbert R-tree optimized by the CUK to quickly store unevenly distributed data and build a fast index for the massive data. Then, we determine the position of the current point of view and use the stacked LSTM model to predict the next point of view. According to the location of two points, we divide the visible area. Finally, according to the preloading strategy, we import the data into the cache area of the graphics processing unit (GPU), which greatly realizes smoother rendering data and large-scale data interaction visualization. The experimental results showed that the proposed algorithm can quickly and accurately draw large volumetric data with high quality while guaranteeing rendering quality.
Highlights
The three-dimensional (3D) visualization technique is an effective method to intuitively analyze data for researchers in medicine, remote sensing, and geological exploration
The graphics processing unit (GPU)-based [6]–[8] application used in 3D volume rendering relieves the workload of the CPU when the latter handles many 3D data operations, the GPU is always limited by the size of its memory and cannot directly process massive data that exceed the scale of the memory
With the aim of addressing the problems found in the above methods, this paper proposes an index method based on the Hilbert R-tree optimized by the clustering algorithm using K-means (CUK) and a viewpoint prediction method based on the stacked long short-term memory (LSTM) model to improve the quality and browsing fluency of the 3D display of massive data
Summary
The three-dimensional (3D) visualization technique is an effective method to intuitively analyze data for researchers in medicine, remote sensing, and geological exploration. With the aim of addressing the problems found in the above methods, this paper proposes an index method based on the Hilbert R-tree optimized by the CUK and a viewpoint prediction method based on the stacked LSTM model to improve the quality and browsing fluency of the 3D display of massive data.
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