Shape Modeling and Computing for Design

Lecturer: Charlie C.L. Wang

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  • The course project presentation will be given in the Bernd Schierbeek room of IO faculty between 9:45 and 12:30 on November 24 (Friday), 2017
  • The source code for LDNI based solid modeling has been added. (November 6, 2017)
  • The slides for HRBF-based closed form surface reconstruction: Link. (October 13, 2017)
  • The source code for implementation on Linux has been added. (October 6, 2017)
  • The source code and resources for the framework of implementation has been added. (September 29, 2017)
  • The course homepage is opened. (September 19, 2017)

    Computational tools have been widely used in current product design and realization, such as all kinds of Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM) and Computer-Aided Engineering (CAE) software systems. However, the capability of simply using these CAD, CAM and CAE software systems is not sufficient for future products design and manufacturing. This course aims to help PhD students in understanding the principles behind CAD, CAM and CAE systems, and provides students with deep understanding of computational techniques and practical experience in developing novel CAD/CAM applications. With advance knowledge, the students will be prepared for advanced careers in the fields of CAD/CAM/CAE, robotics, design and manufacturing automation, virtual reality, and computer graphics. The targeting audience of the course includes but not limited to the students in PhD program of all engineering faculties. In addition, the course will also serve for research purpose by training the students to read literature, understand current research problems, and identify possible contributes to the related fields.

    Teaching Assistants:
    Chengkai Dai (Email:
    Yabin Xu (Email:

    Reference Books:
    [1] Gross, M. and Pfister, H. Point-based Graphics, Morgan Kaufmann Publishers, 2007.
    [2] de Berg, M. et. al. Computational Geometry – Algorithms and Applications. Springer, 2000.
    [3] Mortenson, M. E. Geometric Modeling. Wiley Computer Publishing, 1997.
    [4] Shah, J. and Mantyla, M. Parametric and Feature-Based CAD/CAM, John Wiley and Sons, 1995.
    [5] Hoffmann, C. M. Geometric and Solid Modeling. Morgan Kaufman Publishers, 1989.
    [6] Mantyla, M. Introduction to Solid Modeling. Computer Science Press, 1988.

    L1 - Introduction (Date: September 22, 2017)
    L2 - Data Acquisition (Date: September 22, 2017)
    L3 - Preprocessing (Date: September 29, 2017)
    L4 - Direct Surface Reconstruction (Date: October 6, 2017)
    L5 - Implicit Surface Reconstruction (Date: October 13, 2017)
    Practice Section - RGBD Camera Based Data Accquisition (Date: October 20, 2017)
    L6 - Topology Optimization (by Dr. Jun Wu; Date: October 27, 2017)
    L7 - Layered Depth-Normal Images (Date: November 3, 2017)
    L8 - Advanced Topics in Solid Modeling using LDNI (Date: November 10, 2017)
    L9 - Computational Design and Fabrication (Date: November 17, 2017)

    40% - Programming and Implementation Assignments
    30% - Oral Presentation
    30% - Report and Technical Documents

    Topics for course project (oral presentation):
    1) 3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
    2) L1-Medial Skeleton of Point Cloud
    3) Quality-driven poisson-guided autoscanning
    4) Automated View and Path Planning for Scalable Multi-Object 3D Scanning
    5) Direct visibility of point sets
    6) O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
    7) Two-Scale Topology Optimization with Microstructures
    8) Autonomous Reconstruction of Unknown Indoor Scenes Guided by Time-varying Tensor Fields
    9) Sketch-Based Implicit Blending
    10) Beyond Developable: Computational Design and Fabrication with Auxetic Materials
    11) Practical 3D Frame Field Generation
    12) Fast and Reliable Example-Based Mesh IK for Stylized Deformations

    Useful libraries can be used in completing the course assignments and projects are listed below.
  • GPU-based version of LDNI solid modeling can be found at here - Link
  • The source code of LDNI based solid modeling is available here - Download
  • The library of GLUT (download) and the data set of point clouds (download)
  • Source Code of the programming framework - PntsWork by GLUT: Windows_Version (by Visual Studio), Mac_Version (by XCode) and Linux_Version
  • Building OpenGL/GLUT programs on different platforms (Linux/Mac/Windows): link
  • LDNI DLL Version 1.3: project page

    All rights about the content listed on this page are reserved by Charlie C.L. Wang at the Department of Design Engineering, Delft University of Technology. In no event shall the author be liable to any party for direct, indirect, special, incidental, or consequential damage arising out of the use of the materials downloaded from this page.

    This course is funded by the PhD Kickstart Fund of TU Delft graduate school.