• [2017.05] Our paper "Extract Feature Curves on Noisy Triangular Meshes" is accepted to Graphical Models.
  • [2017.03] Our paper "Surface Reconstruction with Data-driven Exemplar Priors" is accepted to Computer-Aided Design.
  • [2017.02] Our paper "Measurement-based geometric reconstruction for milling turbine blade using free-form deformation" is accepted to Measurement.
  • [2017.01] Our paper "Indoor scene modeling from a single image using normal inference" is accepted to The Visual Computer (TVC).
  • [2016.08] Our paper "Shape Detection from Raw LiDAR Data with Subspace Modeling" is accepted to IEEE Transactions on Visualization and Computer Graphics (TVCG).
  • [2016.08] Our paper "Automatic Modeling of Urban Facades from Raw LiDAR Point Data" is accepted to Pacific Graphics 2016, a special issue of Computer Graphics Forum.
  • [2016.07] Yan, Xiaoxi and Kaicheng joined us. Welcome!
  • [2016.04] Our paper "A Closed-Form Formulation of HRBF-Based Surface Reconstruction" is accepted to Computer-Aided Design.
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Join Us

If you are interested in digital geometry processing, large-scale 3D data processing, 3D scene analysis and understanding, or 3D measurement and inspection, and would like to join the group as a master or PhD student, postdoc or visiting researcher, please contact Prof. Jun Wang ( to discuss opportunities in the group.

About us

We study visual computing the computational principles on 3D data capturing, processing, reconstruction, and high-level 3D scene analysis and understanding. We are interested in building robot-based systems that automatically understand visual scenes, both inferring the semantics and modeling 3D structures.

At the moment, we focus on exploiting 3D geometry data for scene analysis, understanding and reconstruction (e.g. driven by RGB-D data, 3D point clouds, and polygonal mesh models). Additionally, we are also developing techniques on 3D measuring and inspection on industrial products and large-scale construction structures.