Mean Curvature, Cavity Map, ZBrush and nice tricks for enhancing surface shading

There are many many techniques for enhancing the look of a surface by mean of smart shaders. Without touching Non photo-realistic rendering techniques there are many tricks that can helps the perception of the features and the fine details of the surface of an object. ZBrush has popularized one of these techniques with the name of cavity mapping. The main idea is that you detect 'pits' on the surface and you make them of a different color and, very important, very dull. In practice it simulates in a rough way all those materials where in low accessibility regions dust/rust/oxide accumulates while in the most exposed parts the use make them shiny. You can do such effects in MeshLab and they can be very useful for making quick nice renderings of scanned objects; let make a
a practical example using a 3D scanned model of ancient statuette of the Assyrian demon Pazuzu (courtesy of Denis Pitzalis C2RMF/Louvre). The plain model (shown on the right) is rather dull and not very readable and at a first glance you cannot appreciate the scale of the scanned details.

You can improve it a bit by adding a bit of ambient occlusion. In MeshLab you can do it in a couple of ways, either computing an actual ambient occlusion term (e.g. a steradian denoting the portion of sky that you can see from a given point) for each vertex (Filter->Color->Vertex ambient occlusion) or just resort to a quick and dirty Screen Space Ambient Occlusion (SSAO) approximation (render->Screen Space Ambient Occlusion).In the first case as a bonus you are able to tweak the computed value as you want (by playing with the quality mapping tools), in the second you have just an approximation, but the nice fact is that it is a decoration e.g. it is blended over the current frame buffer and therefore it mix up with whatever rendering you have. In the two pictures  per vertex AO vs SSAO.

Not enough. Lets try to find out and enhance the pits. Mean Curvature is what you need, you can think it as the divergence of the normals over the surface and it captures well the concept of characterizing local variation of the surface. There is a vast literature on computing curvatures on discrete triangulated surface and MeshLab exposed a few different methods (and it has also a couple of methods for computing them on Point Clouds). The fastest one (a bit resilient on the quality of the mesh) is filter->color->Discrete Curvature. On the right you can see the result of such filter mapped into a red-green-blu colormap.
if you want expmeriment you can try the various options under filter->normal->compute curvature principal direction (be careful some of these filters can be VERY SLOW).

Now the final step is just to use this value for the shading. Just start the shader called ZBrush and play a bit with the parameters and then, hopefully, you can get the desired result. Some notes. It happens that curvature has some outliers so clamping the quality values before starting the filter can be a good idea (filter->quality->clamp). Similarly the range can be very large so playing with the "transition_speed" parameter of the shader can be a quite useful. To vary the amount of "pits" use the "transition center" slider.

Desktop Manifacturing

The January 2010 number of Make will contains a lot of stuff about Desktop Manufacturing, a field where MeshLab has always been useful as an all purpose repairing tooling (and it is often cited as a handy free stl viewer...). In particular in the "3D Fabbing state of the art" of Make, they refer MeshLab as a "really high quality free software". That's flattering :).

MeshLab on YouTube

Just a short post of a video created by Nicolò dell'Unto (a PhD student at IMT)
about the use of MeshLab and Arc3D for building up a 3D model of an archeological excavation and showing it inside a cave.



Side note, the data was collected during a workshop of the 3DCOFORM training series on “3D acquisition and post-processing” that took place at The Cyprus Institute in Nicosia on 2-6 November 2009 and that, among other things focused on the use of MeshLab for Cultural Heritage related activities.

3D scanning and unrolling an ancient seal

A few lines on an interesting recent project I participated and that exploited MeshLab processing abilities.
The project whose results are now shown in a exhibition at the Louvre involved the scanning with non traditional technologies of the very small and wonderful ancient Cylinder Seal of Ibni-Sharrum (photo © CRMF / D. Pitzalis), a precious antique mesopotamic artifact that is considered one of the absolute masterpieces of glyptic art.

This small seal was digitally acquired at CRMF at a very high resolution and with a variety of 3D scanning techniques (microprofilometry, x-ray Tomography, photogrammetric techniques) and, obviously, the results were processed and integrated entirely with MeshLab.

Among the nice things that we did inside MeshLab was the virtual unrolling of the seal, e.g. getting the inverse shape that you get when you roll the seal over a soft substance like clay or wax.  It was quite easy from a technical point of view, but very appreciated by the restorers that disregard invasive plaster based techniques that often can leave small residuals over the precious artifacts.  You can find more details on the whole acquisition and processing of the seal on this VAST conference paper.

 

On the side you can see a couple of renderings of the 2-million of triangle model of the unrolled seal; the renderings were done inside MeshLab, the first one is a simple flat shaded rendering, while the second one exploit a nice shader that I have recently added to the MeshLab shading arsenal, it mimics in a shameless way the ZBrush technique of varying shininess and color according to the "cavities" of the geometric model (they use it for the famous zbrush wax and bronze materials).  It is nice to see how the shading vastly improve the shape perception of the 3D model.
I have not seen many correct discussion on how to perform these kind of shading, so expects a post on that...


A massive physical reproduction (4 meters long!) of the unrolled seal is at the center of "OnLab" a thematic exhibition of Michel Paysant, that will open in the next days at Louvre, Denis Pitzalis worked a lot on this project and you can find  more details and photos in his blog.

Meshing Point Clouds

One of the most requested tasks when managing 3D scanning data is the conversion of point clouds into more practical triangular meshes. Here is a step-by-step guide for transforming a raw point cloud into a colored mesh.

Let's start from a colored point cloud (typical output of many 3D scanning devices), each point has just color and no normal information. The example dataset that we will use is a medium sized dataset of 9 millions of points. Typical issues of such a dataset dataset: it is non uniform (comes from an integration of different datasets), has some strongly biased error (alignment error, some problem during data integration), it comes without normals (hard to be shaded).

1. Subsampling
   
   
   
   As a first step we reduce a bit the dataset in order to have amore manageable dataset. Many different options here. Having a nicely spaced subsampling is a good way to make some computation in a faster way. The Sampling->Poisson Disk Sampling filter is a good option. While it was designed to create Poisson disk samples over a mesh, it is able to also compute Poisson disk subsampling of a given point cloud (remember to check the 'subsampling' boolean flag). For the curious ones, it uses an algorithm very similar to the dart throwing paper presented at EGSR2009 (except that we have released code for such an algorith long before the publication of this article :) ). In the invisible side figure a Poisson disk subsampling of just 66k vertices.
2. Normal Reconstruction
   
   
   
   Currently inside MeshLab the construction of normals for a point cloud is not particularly optimized (I would not apply it over 9M point cloud) so starting from smaller mesh can give better, faster results. You can use this small point cloud to issue a fast surface reconstruction (using Remeshing->Poisson surface reconstruction) and then transfer the normals of this small rough surface to the original point cloud. Obviously in this way the full point cloud will have a normal field that is by far smoother than necessary, but this is not an issue for most surface reconstruction algorithms (but it is an issue if you want to use these normals for shading!).
3. Surface reconstruction
   
   
   
   Once rough normals are available Poisson surface reconstruction is a good choice. Using the original point cloud with the computed normals we build a surface at the highest resolution (recursion level 11). Roughly clean it removing large faces filter, and eventually simplify it a bit (remove 30% of the faces) using classical Remeshing->Quadric edge collapse simplification filter (many implicit surface filters rely on marching cube like algorithms and leave useless tiny triangles).
4. Recovering original color
   
   
   
   Here we have two options, recovering color as a texture or recovering color as per-vertex color. Here we go for the latter, leaving the former to a next post where we will go in more details on the new automatic parametrization stuff that we are adding in MeshLab. Obviously if you store color onto vertexes you need to have a very dense mesh, more or less of the same magnitudo of the original point cloud, so probably refining large faces a bit could be useful. After refining the mesh you simply transfer the color attribute from the original point cloud to the reconstructed surface using the vertex attribute transfer filter.
   
   
5. Cleaning up and assessing
   
   
   
   The vertex attribute transfer filter uses a simple closest point heuristic to match the points between the two meshes. As a side product it can store (in the all-purpose per-vertex scalar quality) the distance of the matching points. Now just selecting the faces having vertices whose distance is larger than a given threshold we can easily remove the redundant faces created by the Poisson Surface Reconstruction.

This pipeline is only one of the many possible way of ending up into a nice mesh. For example different choices could have been done for step 2/3. There are reconstruction algorithms that do not need surface normals, like for example the "Voronoi Filtering" that is an interpolating reconstruction algorithm (e.g. it build up only triangles on the given input points) but usually these filters works better on very clean datasets, without noise or alignment errors. Otherwise on noisy datasets it is easy that they create a lot of non manifold situations. Final thanks to Denis Pitzalis for providing me this nice dataset of a Cypriot theater.