Affiliation:
1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2. School of Mechanical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
Abstract
The ex-service and old type aero-engines are valuable for education. In many cases, these aero-engines only have physical objects, but lack geometric models. This brings difficulties to talent cultivation. Therefore, the education department needs to reconstruct geometric models of above aero-engines. The laser scanning devices provide raw data of aero-engine profile, but noise directly affects reconstruction accuracy. In order to ensure that noise is removed without blurring or distorting structural features, a structural feature-preserving point cloud denoising method is proposed. The noisy point cloud is divided into casing feature data, pipeline feature data and complex shape feature data. According to shape characteristics of each feature data, three denoising networks are designed to estimate position correction vectors of noisy points and project them back onto underlying surfaces. Qualitative and quantitative experiments show that our method significantly outperforms state-of-the-art methods, both in terms of preservation and restoration of structural features.
Reference34 articles.
1. Pointnet: Deep learning on point sets for 3d classification and segmentation;C. R. Qi
2. PointNet++: deep hierar chical feature learning on point sets in a metric space;C. R. Qi;Advances in Neural Information Processing Systems,2017
3. Dynamic Graph CNN for Learning on Point Clouds
4. PointCleanNet
: Learning to Denoise and Remove Outliers from Dense Point Clouds
5. Edge-aware point set resampling
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献