Surfaces for point clouds using non-uniform grids on the GPU

Abstract

Clustering data is a standard tool to reduce large data sets, such as scans from a LiDAR, enabling real-time rendering. Starting from a uniform grid, we redistribute points from and to neighboring cells. This redistribution is based on the properties of the uniform grid and thus the grid becomes implicitly curvilinear, which produces better matching representatives. Combining these with a polygonal surface reconstruction enables us to create interactive renderings of dense surface scans. Opposed to existing methods, our approach is running solely on the GPU and is able to use arbitrary data fields to influence the curvilinear grid. The surfaces are also generated on the GPU to avoid unnecessary data storage. For evaluation, different data sets stemming from engineering and scanning applications were used and have been compared against typical CPU based reconstruction methods in terms of performance and quality. The proposed method turned out to reach interactivity for large sized point clouds, while being able to adapt to the point clouds geometry, especially when using non-uniform sampled data.