Point cloud encoding for 3D building model retrieval

Jyun Yuan Chen, Chao Hung Lin, Po Chi Hsu, Chung Hao Chen

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


An increasing number of three-dimensional (3D) building models are being made available on Web-based model-sharing platforms. Motivated by the concept of data reuse, an encoding approach is proposed for 3D building model retrieval using point clouds acquired by airborne light detection and ranging (LiDAR) systems. To encode LiDAR point clouds with sparse, noisy, and incomplete sampling, we introduce a novel encoding scheme based on a set of low-frequency spherical harmonic basis functions. These functions provide compact representation and ease the encoding difficulty coming from inherent noises of point clouds. Additionally, a data filling and resampling technique is proposed to solve the aliasing problem caused by the sparse and incomplete sampling of point clouds. Qualitative and quantitative analyses of LiDAR data show a clear superiority of the proposed method over related methods. A cyber campus generated by retrieving 3D building models with airborne LiDAR point clouds demonstrates the feasibility of the proposed method.

Original languageEnglish
Article number6642077
Pages (from-to)337-345
Number of pages9
JournalIEEE Transactions on Multimedia
Issue number2
Publication statusPublished - 2014 Feb

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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