Low-Discrepancy Sampling This is the PhD work of Jonathan Quinn about creating low-discrepancy sampling sequwnces of surfaces utilising space-filling curves. This has been applied to point-based rendering, remeshing and robotic painting (in collaboration with Jonathan Corney and Finlay McPherson). Jonathan’s supervisors are Frank Langbein and Ralph Martin.

Point Sampling


The following software detect approximate regularities for design intent detection. It has been implemented on Linux in C++ (gcc 3.3) and Matlab using OpenCascade 5.2. Software for Approximate Regularity Detection If you use this code please cite one of these publications:

Approximate Regularity Detection



The following software constructs regularity feature trees for design intent detection. It has been implemented on Linux in C++ (gcc 3.3) and Matlab using OpenCascade 5.2. Melon.tar.gz If you use this code please cite one of these publications:

Regularity Feature Tree Construction


liu2007a
S. Liu, R. R. Martin, F. C. Langbein, P. L. Rosin. Segmenting Periodic Reliefs on Triangle Meshes. In: R. R. Martin, M. A. Sabin, J. J. Winkler (eds), Maths of Surfaces XII, Springer LNCS, 4647:290-306, 2007. [DOI: 10.1007/978-3-540-73843-5_18] [PDF]

Segmenting Periodic Reliefs on Triangle Meshes



sun2007
X.-F. Sun, P. L. Rosin, R. R. Martin, F. C. Langbein. Fast and Effective Feature-Preserving Mesh Denoising. IEEE Trans. Visualization and Computer Graphics, 13(5):925-938, 2007. [DOI:10.1109/TVCG.2007.1065] [PDF]

Fast and Effective Feature-Preserving Mesh Denoising




F. C. Langbein. Notes on “How to be Creative?!”. School of Computer Science and Informatics, Cardiff University PhD student away day. 16th May 2007. [PDF]

Notes on “How to be Creative?!”




Sun2007a
X.-F. Sun, P. L. Rosin, R. R. Martin, F. C. Langbein. Random Walks for Mesh Denoising. In: Proc. ACM Symp. Solid and Physical Modeling, pp. 11-22, ACM Siggraph 2007. [DOI:10.1145/1236246.1236252] [PDF]

Random Walks for Mesh Denoising