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

Quinn2007
J. A. Quinn, F. C. Langbein, R. R. Martin. Low-Discrepancy Sampling of Meshes for Rendering. In: Proc. Symp. Point-Based Graphics, Eurographics Assocication, pp. 19-28, 2007. [DOI:10.2312/SPBG/SPBG07/019-028] [PDF]

Low-Discrepancy Sampling of Meshes for Rendering