Fast Visualization of 3D Massive Data Based on Improved Hilbert R-Tree and Stacked LSTM Models

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.

Keywords

• Large-scale datasets
• prediction model based on a stacked LSTM model
• Hilbert R-tree
• load in advance