이전|다음

다중 특징 주요점 추출과 유사 삼각형 최적화 점 쌍 기반 점군 정합 방법

  • role： First author第一作者
  • Affiliation：

    College of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China

    Nanchang Base of International Centre on Space Technologies for Natural and Cultural Heritage under the Auspices of UNESCO, Nanchang 330022, China

    Research Center for Linguistic Spatial Information Science, Jiangxi Normal University, Nanchang 330022, China

  • Email：tuhaowen0204@126.com
  • Introduction：涂浩文，研究方向为点云配准与建模。E-mail： tuhaowen0204@126.com
TU Haowen123,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China

    Nanchang Base of International Centre on Space Technologies for Natural and Cultural Heritage under the Auspices of UNESCO, Nanchang 330022, China

    Research Center for Linguistic Spatial Information Science, Jiangxi Normal University, Nanchang 330022, China

  • Email：wjhgis@126.com
  • Introduction：吴建华，研究方向为空间数据智能集成、地理信息工程、语言空间智能。E-mail： wjhgis@126.com
WU Jianhua123*,  
  • Affiliation：

    College of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China

    Nanchang Base of International Centre on Space Technologies for Natural and Cultural Heritage under the Auspices of UNESCO, Nanchang 330022, China

WANG Yuan12

추상적인

기존의 점군 정합 알고리즘에서 점 특징 계산은 주로 고정된 이웃 영역을 사용하여 복잡한 점군의 특징 계산에 적합하지 않아 주요점 추출 결과가 좋지 않고 대응점에 많은 외점이 존재하는 등의 문제로 인해 점군 정합 정확도에 영향을 미친다. 이에 본 논문에서는 다중 특징 주요점 추출과 유사 삼각형을 이용한 점 쌍 최적화 기반 점군 정합 방법을 제안하며, 핵심은 적응형 이웃 전략을 통해 주요점을 추출하고 매칭 유사도 모델을 최적화하는 데 있다. 먼저 복셀 다운샘플링과 혼합 필터링으로 데이터를 전처리하고 특징 엔트로피 함수를 기반으로 점군 최적 이웃 영역을 결정하며, 이웃 내 법선 벡터 각도 표준편차, 이방성, 곡률을 결합하여 주요점을 추출한다; 다음으로 방향 히스토그램(SHOT)을 이용해 주요점 특징을 계산한다; 그 후 쌍방향 최근접 이웃 거리 비(BNNDR)를 사용해 초기 매칭 점 쌍을 구성하고, 유사 삼각형 기반 계산 모델을 제안하여 점 쌍을 최적화하여 거친 정합을 수행한다. 마지막으로 법선 벡터 각도 제약을 추가한 점-평면 ICP(Point to plane ICP) 알고리즘을 이용해 정밀 정합을 수행한다. 본 논문은 Stanford 데이터셋을 대상으로 실험을 진행하였으며, 결과는 고전적 ICP, 주요점 기반 4점 일관성 집합(K-4PCS), 샘플링 일관성(SAC-IA)과 ICP 결합점군 정합 알고리즘 대비 본 방법의 오차가 최소임을 보여준다. 또한 실제 측정 데이터 정합 응용에서도 본 방법은 뚜렷한 장점을 보여주었다.

키워드

적응형 이웃;점군 정합;유사 삼각형;점-평면 ICP

References

  1. 1.
    Abdi H and Williams L J. 2010. Principal component analysis. WIREs: Computational Statistics, 2(4): 433-459
  2. 2.
    Besl P J and McKay N D. 1992. Method for registration of 3-D shapes//Proceedings Volume 1611, Sensor Fusion IV: Control Paradigms and Data Structures. Boston: SPIE: 586-606
  3. 3.
    Chen Y and Medioni G. 1992. Object modelling by registration of multiple range images. Image and Vision Computing, 10(3): 145-155
  4. 4.
    Chetverikov D, Svirko D, Stepanov D and Krsek P. 2002. The trimmed iterative closest point algorithm//2002 International Conference on Pattern Recognition. Quebec City: IEEE: 545-548
  5. 5.
    Demantké J, Mallet C, David N and Vallet B. 2012. Dimensionality based scale selection in 3D lidar point clouds. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-5-W12: 97-102
  6. 6.
    Huang Z J, Wen Y X, Wang Z H, Ren J J and Jia K. 2024. Surface reconstruction from point clouds: a survey and a benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12): 9727-9748
  7. 7.
    Jia C Q, Pang M and Han X. 2023. A feature extraction algorithm based on optimal neighborhood size. Circuits, Systems, and Signal Processing, 42(4): 2193-2214
  8. 8.
    Li Y X, Guo J M, Pan S Y, Lü L L, Lu Z X and Zhang D. 2020. A point cloud registration algorithm based on ISS-SHOT features. Bulletin of Surveying and Mapping, (4): 21-26
  9. 9.
    Liu R F, Wang F, Ren H W, Wang M Y and Yang J B. 2022. Road scene laser point cloud registration method based on geographical object features. Chinese Journal of Lasers, 49(18): 1810002
  10. 10.
    Luo R, Zhou Z X, Chu X, Ma W and Meng J H. 2022. 3D deformation monitoring method for temporary structures based on multi-thread LiDAR point cloud. Measurement, 200: 111545
  11. 11.
    Salti S, Tombari F and Di Stefano L. 2014. SHOT: unique signatures of histograms for surface and texture description. Computer Vision and Image Understanding, 125: 251-264
  12. 12.
    Tombari F, Salti S and Di Stefano L. 2013. Performance evaluation of 3D keypoint detectors. International Journal of Computer Vision, 102(1): 198-220
  13. 13.
    Wang M J, Yi F, Li L and Huang C J. 2022. Local neighborhood feature point extraction and matching for point cloud alignment. Infrared and Laser Engineering, 51(5): 20210342
  14. 14.
    Wang W B, Tian M Y, Yu J Y, Song C H, Li J R and Zhou M L. 2022. Improved iterative nearest point point cloud alignment method. Laser and Optoelectronics Progress, 59(2): 0215007
  15. 15.
    Weinmann M, Jutzi B and Mallet C. 2014. Semantic 3D scene interpretation: a framework combining optimal neighborhood size selection with relevant features. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-3: 181-188
  16. 16.
    Xiong F G. 2018. Research on Registration Technology of 3D Point Cloud. Taiyuan: North University of China
  17. 17.
    Xu G X, Pang Y J, Bai Z X, Wang Y L and Lu Z W. 2021. A fast point clouds registration algorithm for laser scanners. Applied Sciences, 11(8): 3426
  18. 18.
    Yang J L, Li H D and Jia Y D. 2013. Go-ICP: solving 3D registration efficiently and globally optimally//Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney: IEEE: 1457-1464
  19. 19.
    Yang J Q, Zhang S K, Fan S C and Cao Z G. 2022. Survey on multi-view point cloud registration algorithm. Journal of Huazhong University of Science and Technology (Nature Science Edition), 50(11): 16-34, 43
  20. 20.
    Yang S, Hou M L and Li S N. 2023. Three-dimensional point cloud semantic segmentation for cultural heritage: a comprehensive review. Remote Sensing, 15(3): 548
  21. 21.
    Zeybek M and Şanlıoğlu İ. 2019. Point cloud filtering on UAV based point cloud. Measurement, 133: 99-111
  22. 22.
    Zhang J Y, Yao Y X and Deng B L. 2022b. Fast and robust iterative closest point. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7): 3450-3466
  23. 23.
    Zhang S J, Ma Y Z and Zhao H F. 2019. Elimination algorithm of mismatched point pairs based on point cloud geometric feature. Computer Engineering, 45(4): 163-168
  24. 24.
    Zhang Z Y, Zheng J Y, Tao Y Y, Xiao Y, Yu S M, Asiri S, Li J C and Li T S. 2022a. Traffic sign based point cloud data registration with roadside LiDARs in complex traffic environments. Electronics, 11(10): 1559
  25. 25.
    Zhu J H, Jiang Z T, Evangelidis G D, Zhang C Q, Pang S M and Li Z Y. 2019. Efficient registration of multi-view point sets by K-means clustering. Information Sciences, 488: 205-218

더 읽기

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website