Surfaces for point clouds using non-uniform grids on the GPU
Date issued
2015
Journal Title
Journal ISSN
Volume Title
Publisher
Václav Skala - UNION Agency
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.
Description
Subject(s)
rekonstrukce povrchu, bodová mračna, shlukování, zakřivené mřížky
Citation
WSCG '2015: short communications proceedings: The 23rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2015 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech Republic8-12 June 2015, p. 107-115.