the respective vertex resolution.[]{data-label="fig:comparison_model"}](pic/deg1.png "fig:"){width="2.45cm"}   ![The degradation of one of the model used to test the robustness of the descriptor of both edgeLBP and meshLBP. The number on each image is the respective vertex resolution.[]{data-label="fig:comparison_model"}](pic/deg2.png "fig:"){width="2.45cm"}
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First, we re-sampled the meshes in the CCP dataset with $40K$ vertices. On this dataset, we compare the outcome of the edgeLBP with the default settings with the meshLBP, see Table \[tb:downgrades\]. If compared with the performances on the original CPP dataset in Figure \[fig:ccp\_res\], the edgeLBP degrades less than the meshLBP, demonstrating of being more robust to mesh degradation and re-sampling.

              NN     FT      ST      e      mAP    nDCG
  --------- ------ ------- ------- ------ ------- ------
   edgeLBP   0.95   0.688   0.857   0.59   0.761   0.9
   meshLBP   0.77   0.517   0.703   0.47   0.58    0.79

  : Evaluation measures of the performances on the CPP dataset resampled with 40K vertices.

\[tb:downgrades\]

Second, we selected 3 patches from the Artifacts dataset and sub-sampled them with $32K$, $24K$, $16K$ and $8K$ vertices (see Figure \[fig:comparison\_model\]).These four meshes are compared against the original patch (that has $40K$ vertices).These four distance values provide an estimate of the error the descriptors do when working with the simplified meshes.

We performed two runs for 