A clustering-based simplification of massive automobile-bodies point cloud for lightweight design

Abstract

Adaptive simplification for massive and large-scale automobile-bodies point cloud obtained by 3D laser-scanning has been proven to be an effective technology to conduct lightweight design. This paper introduces a point-based algorithm to simplify laser-scanning point cloud without any support of fitted surface. The intrinsic characteristic of laser-scanning data is investigated to produce a topological connectivity for adjacent points in scanlines. We explore an automatic normal-vector estimation framework through the relationship between normal-vector and its adjacent geometric elements. To retain more points in high-curvature areas and fewer points in planar regions efficiently, the local normal-vector variance is adopted to determine subdivision-decision condition. The boundary points are detected and then preserved before non-uniform subdivision. A relevant simplification system based on our algorithm is developed. Many simplification cases are implemented to validate the effectiveness of our method and demonstrate the feasibility for automobile-bodies point cloud. The comparison with other point-based methods is also performed to illustrate the superiority of our method.