Earticle

초록

영어

We propose a new technique for enhancing high frequency geometric features and geometric texture for scatter point clouds. We generalize the classical High-Boost filtering of signal and image processing to the geometric feature manipulation for 3D point clouds. We get a smoothed version of the input point clouds by Adaptive Moving Least Squares (MLS) based point clouds smoothing operator. The distance fields between the point clouds and their corresponding smoothed version are regarded as the high frequency geometric features and geometric textures intuitively. High Boost operations are performed by iteratively updating the position of the input points along the normal direction which is proportion to the distance between the point clouds and their corresponding smoothed versions with given geometric enhancement scale factor. The effectiveness of the proposed method is demonstrated by several examples with both synthetic and real scanned point clouds.

목차

Abstract
 1. Introduction
 2. Related Works
 3. High Boost Filtering
 4. Modified MLS based Point Cloud Smoothing and Normal Estimation
  4.1. Normal Estimation
  4.2. Modified MLS based Point Cloud Smoothing
 5. Iterative Position Updating for Shape Enhancement
 6. Implementation and Results
 7. Conclusions and Future Work
 Acknowledgements
 References

키워드

저자

• Cao Juming [ School of Electronic and Information Engineering, Xi’an Jiaotong University Xi'an, Shaanxi, 710049, P.R. China, Department of Compter Science , Baoji University of Arts and Sciences ,Baoji, Shaanxi, 721000, P.R. China ]
• Wushour Slamu [ Department of Compter Science , Baoji University of Arts and Sciences ,Baoji, Shaanxi, 721000, P.R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605