Efficient reduction of point data sets for surface splatting using geometry and color attributes

Abstract

Surface splat, one of the point-based rendering primitives, has offered a powerful alternative to triangle meshes when it comes to the rendering of highly complex objects due to its potential for high-performance and high-quality rendering. Recently, the technological advance of 3D scanners has made it possible to acquire color as well as geometry data of highly complex objects with very high speed and accuracy. However, scanning and acquisition systems often produce surfaces that are much more dense than actually required for the intended application. Therefore, reduction of point data set is necessary to further process the model. Although many efficient sampling methods for point-based surfaces have been proposed to reduce the complexity of geometric models, none of these has taken into account color, which is fundamental for achieving a high quality visual appearance. Therefore, we propose an efficient sampling method of point data sets for surface splatting which uses both geometry and color attributes. Our proposed method converts a dense set of point samples into a sparse set of object space splats. It successfully approximates of the original model within a given geometric and color error. In order to measure color differences between point samples with consistency, the color error tolerance is evaluated in a CIELAB uniform color space.