Large-scale three-dimensional point cloud visualization platform of plug-in architecture

Abstract

According to a large-scale three-dimensional point cloud visualization platform of the plug-in type architecture, a combined type point cloud organization and a storage structure provided in the invention, point cloud data can be rapidly and conveniently accessed, the retrieval efficiency is extremely high, and later-stage large-scale three-dimensional point cloud visualization is also facilitated; an optimization method of visual multi-level rendering of a large-scale three-dimensional scene is provided, a vision field body cutting scheduling and multi-detail-level representation method in large-scale point cloud rendering is realized by using an octree storage structure of the point cloud, and the rendering efficiency of the large-scale three-dimensional point cloud is greatly improved; on the basis of a QT plug-in architecture, a platform plug-in architecture with comprehensive and abundant functions and a flexible and fast point cloud plug-in interface are designed and realized, reading and writing of different types of point cloud data are realized, and development of a three-dimensional point cloud algorithm is more convenient; the large-scale three-dimensional point cloud visualization platform based on the plug-in is provided, the platform plug-in architecture has expandability, and the platform development efficiency is improved.

Description

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AI Technical Summary

Benefits of technology

This patented technology describes various ways to simplify complex 3D graphic systems such as mobile phone apps or Java APIs (Java Platform) into smaller portables called Plug-Insight™ System(PCI). These small devices allow users to customize their own hardware configurations without being limited by programming capabilities like CPU speed. They offer advanced functionality through enabling efficient execution of graphics commands and exploring diverse areas within them. Additionally, they integrate image caching tools and live streaming techniques to enhance performance while reducing memory usage. Overall, these improvements help streamline the integration phase and improves overall developer productivity.

Problems solved by technology

Technologies aim to improve upon current point cloud display techniques, particularly those related to point cloud applications like autonomous driving vehicles (ADV), virtual reality simulations, and augmented reality environments. Current point cloud displays require manual inputting of data values from an external device called a camera, resulting in slow downy operation rates and increased resource usage. Existing approaches involve developing customized image databases based on specifications, requiring extensive effort and computational power. There is currently no known effective approach for improving the speed and accuracy required for realistic viewpoint cloud visualizations.

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