3D model retrieval and assessment for point cloud modeling

Po Chi Hsu, Jyun Yuan Chen, Chao-Hung Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Based on the concept of data reuse and data sharing, a 3D model retrieval and assessment approach is proposed to reconstruct point clouds for cyber city modeling and updating. The main idea is to build a gigantic database containing great diversity of 3D building models. The models in database are collected from model-sharing www applications. All the models in the database are encoded by a small set of low-frequency spherical harmonic functions (SHFs). A point cloud obtained by airborne LiDAR is inputted as query to search the similar models from the database. By means of matching the SHFs coefficients between point clouds and 3D models, the most similar model can be efficiently extracted. The extracted model can be used as a template model to fit the point cloud. The experiment results show that the proposed approach can efficiently extract the fittest model from a huge database. This makes the proposed approach feasible to efficiently construct and update 3D city models.

Original languageEnglish
Title of host publication31st Asian Conference on Remote Sensing 2010, ACRS 2010
Pages1578-1583
Number of pages6
Publication statusPublished - 2010 Dec 1
Event31st Asian Conference on Remote Sensing 2010, ACRS 2010 - Hanoi, Viet Nam
Duration: 2010 Nov 12010 Nov 5

Publication series

Name31st Asian Conference on Remote Sensing 2010, ACRS 2010
Volume2

Other

Other31st Asian Conference on Remote Sensing 2010, ACRS 2010
CountryViet Nam
CityHanoi
Period10-11-0110-11-05

Fingerprint

Harmonic functions
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Hsu, P. C., Chen, J. Y., & Lin, C-H. (2010). 3D model retrieval and assessment for point cloud modeling. In 31st Asian Conference on Remote Sensing 2010, ACRS 2010 (pp. 1578-1583). (31st Asian Conference on Remote Sensing 2010, ACRS 2010; Vol. 2).
Hsu, Po Chi ; Chen, Jyun Yuan ; Lin, Chao-Hung. / 3D model retrieval and assessment for point cloud modeling. 31st Asian Conference on Remote Sensing 2010, ACRS 2010. 2010. pp. 1578-1583 (31st Asian Conference on Remote Sensing 2010, ACRS 2010).
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abstract = "Based on the concept of data reuse and data sharing, a 3D model retrieval and assessment approach is proposed to reconstruct point clouds for cyber city modeling and updating. The main idea is to build a gigantic database containing great diversity of 3D building models. The models in database are collected from model-sharing www applications. All the models in the database are encoded by a small set of low-frequency spherical harmonic functions (SHFs). A point cloud obtained by airborne LiDAR is inputted as query to search the similar models from the database. By means of matching the SHFs coefficients between point clouds and 3D models, the most similar model can be efficiently extracted. The extracted model can be used as a template model to fit the point cloud. The experiment results show that the proposed approach can efficiently extract the fittest model from a huge database. This makes the proposed approach feasible to efficiently construct and update 3D city models.",
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Hsu, PC, Chen, JY & Lin, C-H 2010, 3D model retrieval and assessment for point cloud modeling. in 31st Asian Conference on Remote Sensing 2010, ACRS 2010. 31st Asian Conference on Remote Sensing 2010, ACRS 2010, vol. 2, pp. 1578-1583, 31st Asian Conference on Remote Sensing 2010, ACRS 2010, Hanoi, Viet Nam, 10-11-01.

3D model retrieval and assessment for point cloud modeling. / Hsu, Po Chi; Chen, Jyun Yuan; Lin, Chao-Hung.

31st Asian Conference on Remote Sensing 2010, ACRS 2010. 2010. p. 1578-1583 (31st Asian Conference on Remote Sensing 2010, ACRS 2010; Vol. 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Hsu PC, Chen JY, Lin C-H. 3D model retrieval and assessment for point cloud modeling. In 31st Asian Conference on Remote Sensing 2010, ACRS 2010. 2010. p. 1578-1583. (31st Asian Conference on Remote Sensing 2010, ACRS 2010).