Simplification of point-sampled model based on geometry images

An adaptive curvature simplification method for point-sampled model is presented based on geometry images. First, the point-sampled model is represented as geometry images by projecting its spherical polar coordinates onto a plane. Using geometry images, the K-nearest neighbors of each sample point are then determined significantly fast and its surface variation is calculated. Finally, the point set surfaces are re-sampled in combination with the surface variation and simple density control. In addition, the quality of the simplified point set surfaces is evaluated using the error measurement method based on the moving least squares surfaces. Experimental results show that this algorithm is fast, easy to control the simple density and preserve the detail, and can create high-quality surface approximations.