Extending the Compatibility of Point Cloud Indexing Systems with State-of-the-Art Point Cloud Visualizers

As point clouds become increasingly important across a wide range of applications, efficient visualization methods are crucial. Before point clouds can be rendered, they must first undergo preprocessing and be stored in spatial data structures to enable fast access and visualization. However, this preprocessing is often time-consuming, and many existing tools face compatibility issues with current visualizers. In particular, one novel tool that leverages Big Data technologies and stores its data in a Cassandra database is currently unsupported by any visualizer. Motivated by this issue, this thesis aims to improve the compatibility between point cloud preprocessing tools and browser-based visualizers. To achieve this, a modified HTTPserver is proposed that converts HTTP-requests from visualizers in real-time into various formats used by modern preprocessing tools. Additionally, a framework is developed that introduces an intermediate representation to separate input and output formats of the conversion, facilitating the easy integration of future formats. The feasibility of this approach is demonstrated through the implementation of support for two popular point cloud formats and the currently unsupported Cassandra-based format. Benchmark results indicate that conversion is achievable in real-time with minimal additional processing time, thereby validating the effectiveness of the proposed solution. As a result, this thesis introduces a novel approach for improving the compatibility and flexibility of point cloud visualization tools in a general and extensible manner.