TY - GEN
T1 - Scaling Invariant Harmonic Wave Kernel Signature for 3D Point Cloud Similarity
AU - Zhang, Dan
AU - Liu, Na
AU - Yan, Yuhuan
AU - Ma, Xiujuan
AU - Renqing, Zhuome
AU - Zhang, Xiaojuan
AU - Ma, Fuxiang
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/8
Y1 - 2021/8
N2 - In recent years, the analysis tasks of 3D point cloud models have also attracted wide attention from researchers. The most basic and important research work of 3D point cloud model analysis is the similarity measurement of 3D models. The similarity measurement of 3D point cloud models are generally calculated by shape descriptors, which can capture the most unique features for 3D point cloud models. However, the traditional feature extraction methods for 3D point cloud models are less robust, only focus on rigid deformation and less attention to non-rigid deformation. Recent publications introduce the Laplace-Beltrami operator to define shape descriptors and analysis the non-rigid deformation of models. In this paper, a concise 3D point cloud descriptor is defined to describe the internal structure of 3D point cloud models: scaling invariant harmonic wave kernel signature (SIHWKS). SIHWKS is a shape descriptor involving in the Laplace-Beltrami operator, which can effectively extract geometric and topological information from 3D point cloud models. Based on SIHWKS, the modified Hausdorff distance between SIHWKS values of 3D point cloud model is calculated as similarity measurement, which provides an effective method for 3D point cloud model analysis. Lastly, experiments conducted on public 3D shape datasets show the SIHWKS has the advantages of isometric invariance, scaling invariance and it is robust to topology, sampling and noise.
AB - In recent years, the analysis tasks of 3D point cloud models have also attracted wide attention from researchers. The most basic and important research work of 3D point cloud model analysis is the similarity measurement of 3D models. The similarity measurement of 3D point cloud models are generally calculated by shape descriptors, which can capture the most unique features for 3D point cloud models. However, the traditional feature extraction methods for 3D point cloud models are less robust, only focus on rigid deformation and less attention to non-rigid deformation. Recent publications introduce the Laplace-Beltrami operator to define shape descriptors and analysis the non-rigid deformation of models. In this paper, a concise 3D point cloud descriptor is defined to describe the internal structure of 3D point cloud models: scaling invariant harmonic wave kernel signature (SIHWKS). SIHWKS is a shape descriptor involving in the Laplace-Beltrami operator, which can effectively extract geometric and topological information from 3D point cloud models. Based on SIHWKS, the modified Hausdorff distance between SIHWKS values of 3D point cloud model is calculated as similarity measurement, which provides an effective method for 3D point cloud model analysis. Lastly, experiments conducted on public 3D shape datasets show the SIHWKS has the advantages of isometric invariance, scaling invariance and it is robust to topology, sampling and noise.
KW - 3D point cloud model
KW - Laplace-Beltrami operator
KW - Shape feature
KW - Shape similarity
UR - http://www.scopus.com/inward/record.url?scp=85117096092&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87361-5_4
DO - 10.1007/978-3-030-87361-5_4
M3 - Conference article published in proceeding or book
AN - SCOPUS:85117096092
SN - 9783030873608
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 44
EP - 56
BT - Image and Graphics - 11th International Conference, ICIG 2021, Proceedings
A2 - Peng, Yuxin
A2 - Hu, Shi-Min
A2 - Gabbouj, Moncef
A2 - Zhou, Kun
A2 - Elad, Michael
A2 - Xu, Kun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Image and Graphics, ICIG 2021
Y2 - 6 August 2021 through 8 August 2021
ER -