@@ -28,3 +28,13 @@ On the other side, the closest the mean is to zero the more points from target m
It is important to take into account that, in order to be able to compare different 3D models we should normalize the values of max, min, mean (so that there are not dependant on the size). That is the reason we have diag_mesh_0 and diag_mesh_1 values. A good approach is to use diag_mesh_0 and then obtain the percentage, in this case we could set up a minimum percentage required to consider that the decomposition is well done.
The Hausdorff distance was obtained for the different tests done before. We focus specially in how this distance is affected by the variation in the model resolution and concavity. The following two graphics show precicely this relationship, focusing in the max and mean values (calculated as a percentage over the mesh diagonal as described in the former paragraph).
A quality criteria obtained from the max and mean values of the Hausdorff distance is a good way to validate the decomposition of our 3D model.
In general terms we only need to focus in calculating the distance from our 3D model to the merged model. That is, our sample model is the original model and the target model is the decomposed one (the merge of all the pieces obtained). This is because our original model is normally more dense.
However:
- The distance (max value) calculated using the merged mesh as the sample model (the other side of the Hausdorff distance) can be bigger than the other distance (from original model to merged) when our merged model is more dense than the original model. This is the case when using high resolution values and really small concavity values.
Be careful when using the maximum of both distances (we have not done it in our tests although both values were calculated) because we can end up having big max distance values in cases where the decomposition is good but extremelly detailed.
- Although the Hausdorff distance can be considered as a global quality parameter, there will be always cases where a small max value does not mean a good decomposition. That is the case when using high beta values (see first tests section). In this case a beta = 0,1 has a max value (%) of 3,75 while for a beta = 0,5 it is of 3,49% but the decomposed model at beta = 0,5 has the cup handle wrong while at beta = 0,1 it is fine.
