Semantic decomposition and reconstruction of residential scenes from LiDAR data

Hui Lin, Jizhou Gao, Yu Zhou, Guiliang Lu, Mao Ye, Chenxi Zhang, Ligang Liu, Ruigang Yang

Research output: Contribution to journalArticlepeer-review

117 Scopus citations


We present a complete system to semantically decompose and reconstruct 3D models from point clouds. Different than previous urban modeling approaches, our system is designed for residential scenes, which consist of mainly low-rise buildings that do not exhibit the regularity and repetitiveness as high-rise buildings in downtown areas. Our system first automatically labels the input into distinctive categories using supervised learning techniques. Based on the semantic labels, objects in different categories are reconstructed with domain-specific knowledge. In particular, we present a novel building modeling scheme that aims to decompose and fit the building point cloud into basic blocks that are blockwise symmetric and convex. This building representation and its reconstruction algorithm are flexible, efficient, and robust to missing data. We demonstrate the effectiveness of our system on various datasets and compare our building modeling scheme with other state-of-the-art reconstruction algorithms to show its advantage in terms of both quality and speed.

Original languageEnglish
Article number66
JournalACM Transactions on Graphics
Issue number4
StatePublished - Jul 2013


  • Decomposition and reconstruction
  • Hierarchical representation
  • Residential scenes
  • Symmetric blocks

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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