3D building model retrieval system based on LiDAR point cloud filling and encoding

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

研究成果: Conference contribution

摘要

Airborne Light Detection and Ranging (LiDAR) has the ability of acquiring huge and highly accurate point cloud. Therefore, the processing of huge point clouds has become an important research topic and has drawn increasing attention in the fields of remote sensing. In this paper, an automatic three-dimensional (3D) model retrieval system is introduced to retrieve building models using point clouds as input queries. The proposed system includes a novel point cloud encoding approach and a 3D building model database acquired from Internet. The encoding approach is based on low frequency spherical-harmonic basis functions which provide compact representation of 3D data and has the properties of noise insensitivity and rotation invariance. To ease the problem of inconsistent encoding of point cloud and building models, three steps, namely, model resampling, datum determination, and data filling, are introduced in the preprocessing. The origins of input point cloud and models are aligned in the step of datum determination, and the aliasing problems caused by sparse and incomplete sampling of point clouds are eased in the steps of model resampling and data filling. The experimental results show that the proposed method which consistently encodes a point cloud and a model can yield satisfactory retrieval results.

原文English
主出版物標題34th Asian Conference on Remote Sensing 2013, ACRS 2013
發行者Asian Association on Remote Sensing
頁面261-268
頁數8
ISBN(列印)9781629939100
出版狀態Published - 2013 一月 1
事件34th Asian Conference on Remote Sensing 2013, ACRS 2013 - Bali, Indonesia
持續時間: 2013 十月 202013 十月 24

出版系列

名字34th Asian Conference on Remote Sensing 2013, ACRS 2013
1

Other

Other34th Asian Conference on Remote Sensing 2013, ACRS 2013
國家Indonesia
城市Bali
期間13-10-2013-10-24

指紋

Invariance
Remote sensing
Internet
Sampling
Processing

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

引用此文

Chen, J. Y., Hsu, P. C., & Lin, C-H. (2013). 3D building model retrieval system based on LiDAR point cloud filling and encoding. 於 34th Asian Conference on Remote Sensing 2013, ACRS 2013 (頁 261-268). (34th Asian Conference on Remote Sensing 2013, ACRS 2013; 卷 1). Asian Association on Remote Sensing.
Chen, Jyun Yuan ; Hsu, Po Chi ; Lin, Chao-Hung. / 3D building model retrieval system based on LiDAR point cloud filling and encoding. 34th Asian Conference on Remote Sensing 2013, ACRS 2013. Asian Association on Remote Sensing, 2013. 頁 261-268 (34th Asian Conference on Remote Sensing 2013, ACRS 2013).
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Chen, JY, Hsu, PC & Lin, C-H 2013, 3D building model retrieval system based on LiDAR point cloud filling and encoding. 於 34th Asian Conference on Remote Sensing 2013, ACRS 2013. 34th Asian Conference on Remote Sensing 2013, ACRS 2013, 卷 1, Asian Association on Remote Sensing, 頁 261-268, 34th Asian Conference on Remote Sensing 2013, ACRS 2013, Bali, Indonesia, 13-10-20.

3D building model retrieval system based on LiDAR point cloud filling and encoding. / Chen, Jyun Yuan; Hsu, Po Chi; Lin, Chao-Hung.

34th Asian Conference on Remote Sensing 2013, ACRS 2013. Asian Association on Remote Sensing, 2013. p. 261-268 (34th Asian Conference on Remote Sensing 2013, ACRS 2013; 卷 1).

研究成果: Conference contribution

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N2 - Airborne Light Detection and Ranging (LiDAR) has the ability of acquiring huge and highly accurate point cloud. Therefore, the processing of huge point clouds has become an important research topic and has drawn increasing attention in the fields of remote sensing. In this paper, an automatic three-dimensional (3D) model retrieval system is introduced to retrieve building models using point clouds as input queries. The proposed system includes a novel point cloud encoding approach and a 3D building model database acquired from Internet. The encoding approach is based on low frequency spherical-harmonic basis functions which provide compact representation of 3D data and has the properties of noise insensitivity and rotation invariance. To ease the problem of inconsistent encoding of point cloud and building models, three steps, namely, model resampling, datum determination, and data filling, are introduced in the preprocessing. The origins of input point cloud and models are aligned in the step of datum determination, and the aliasing problems caused by sparse and incomplete sampling of point clouds are eased in the steps of model resampling and data filling. The experimental results show that the proposed method which consistently encodes a point cloud and a model can yield satisfactory retrieval results.

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Chen JY, Hsu PC, Lin C-H. 3D building model retrieval system based on LiDAR point cloud filling and encoding. 於 34th Asian Conference on Remote Sensing 2013, ACRS 2013. Asian Association on Remote Sensing. 2013. p. 261-268. (34th Asian Conference on Remote Sensing 2013, ACRS 2013).