A novel simplification algorithm based on MLS and Splats for point models

The simplification of point models is important in point-based processing technology because of the increasing of data complexity. Many researches focus on getting the subset from the initial point set, which can not represent the whole object properly. In this paper, we present a novel simplification algorithm based on Moving Least Square (MLS) and Splats for point models. The algorithm uses MLS to represent the point models and can get the minimum error point which is used to deputize its neighborhood. This approach can get new proper agent points instead of subset. Then we calculate the error based on the rendering results, which means considering the geometry of splat when calculating the error of simplification. Experiment results show that this algorithm is not only efficient, but also has good quality.