Data Set for Parameterizations

 

This large testing benchmark contains 20712 meshes. It starts from well cut meshes $D_1$, continues with moderately bad ones $D_2$, and ends with extremely challenging examples $D_3$. If you use our data set, please cite [1] and [2].

$D_1$ (Download)

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Figure 1: Gallery of some selected models from $D_1$, which containts 10273 models. Since nearly half the models are very large, we use a program to subdivide the small sized meshes. Here we only provide the small sized meshes which are cut by our sphere-based method [2]. If you want to test your parameterization method on these large-scale models, please download the code to generate them.

Figure 2: Efficiency comparisons using two models from $D_1$. We select the scalable locally injective maps (SLIM) [Rabinovich et al. 2017], the approximate Killing vector field method (AKVF) [Claici et al. 2017], and the composite majorization method (CM) [Shtengel et al. 2017] as the competitors.

$D_2$ (Download)

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Figure 3: Gallery of some selected models from $D_2$, which containts 6189 models.

Figure 4: Efficiency comparisons using two models from $D_2$.

$D_3$ (Download)

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Figure 5: Gallery of some selected models from $D_3$, which containts 4250 models.

Figure 6: Efficiency comparisons using two models from $D_3$.

Reference

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[1]: Ligang Liu, Chunyang Ye, Ruiqi Ni, Xiao-Ming Fu. Progressive Parameterizations. ACM Transactions on Graphics (SIGGRAPH) 37(4), 2018. [Bibtex].
[2]: Shuangming Chai, Xiao-Ming Fu, Xin Hu, Yang Yang, Ligang Liu. Sphere-based Cut Construction for Planar Parameterizations. Computer & Graphics (SMI 2018). [Bibtex].