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.

MeshLab V1.2.2 Released!

Yet another minor release of MeshLab. This time a lot of large internal changes (we redesigned the parameter mechanism of the filters for a better previewing mechanism) and we added a few new features:
* pdb molecular importing to build up meshes from molecular description. It feature various ways of building meshes from pdb description.
* Weighted simplification; you can now weight the simplification process with a generic scalar value (e.g. simplify more the internal regions, preserve better the face of a character, etc, etc.).
* Improved the vertex attribute transfer filter (the filter that allows you to transfer color, vertex, position, quality from a mesh to another one) to support the management
of point cloud data and to limit the attribute transfer to a limited
distance.
* The new abstract surface parametrization algorithm in now inside MeshLab; currently it is a bit slow and buggy (well it is the first release) so sometime it can crash. The current version of the filter support only the remeshing side of the technique, e.g. you can create an abstract texture and then use it to remesh your model in a very nice way. Full texture parametrization of meshes ahead in the next version.
* And obviously a lot of small bug issues....
As usual release notes are here in the wiki.

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 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. 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 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 quadric 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 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.

Computation & Cultural Heritage Siggraph Course

Shameless linking of the Computation & Cultural Heritage Siggraph Course where, a week ago, I gave my contribution. The course surveyed several practical CG techniques for applications in cultural heritage, archeology, and art history. Topics include: efficient/advanced/cheap techniques for 2D/3D digital capture of heritage objects, appropriate uses in the heritage field, an end-to-end pipeline for processing archeological reconstructions (with special attention to incorporating archeological data and review throughout the process), how digital techniques are actually used in cultural heritage projects, and an honest evaluation of progress and challenges in this field.

Specifically to this blog in my first presentation I described a free photo to 3D pipeline that relies on the free web-based service Arc3D (developed during the Epoch EU project by Visic of KUL) for Structure-from-Motion reconstruction and (obviously) on MeshLab for the processing of the generated 3D range maps. In practice it is a pipeline that allows to cheaply reconstruct nice accurate 3D models from just a set of high resolution photos. Obviously not all the subject fit with this kind of approaches (forget moving subjects and glassy, shiny, fluffy, iridescent stuff), but for stable, dull, textured objects, it works surprisingly well, giving results with a quality not far from traditional laser based 3D scanning. More info on the process in the slides (and eventually in other posts here). In the top right picture a typical example of the results that you can obtain when starting from a reasonable set of photos of a detail of a weathered stone romanesque high relief (Monasterio de Santa María de Ripoll). The model is untextured, with just a bit of ambient occlusion: all you see is geometry.

Almost isometric mesh parameterization

A short post after a long inactivity just before going to Siggraph.


Many users of MeshLab complained the lack of texturing tools. As you probably know perfect, nice, clean, robust, automatic texture parametrization is a kind of 'holy grail' in CG. There are many many solutions around and a huge literature on that, but no silver bullet.
We (mostly Nico and Marco) added our 5 cents to the literature with yet another approach [1] that is able to produce parametrizations that exhibit a very low distortion and are composed by a small number of large regular patches. The parametrization domain is a collection of equilateral triangular 2D regions enriched with explicit adjacency relationships (we call it abstract because no explicit 3D embedding is necessary). It is tailored in order to minimize the distortion, resulting in excellent parametrization qualities, even when meshes with complex shapes and topology are mapped into domains composed of a small number of large contiguous regions.

An interesting consequence of having a texturing domain that is composed by 'abstract' equilateral triangles is that you can exploit this parametrization to build high quality remeshing that are better that the current state of the art. Look at the top figures to get an idea of the quality of the produced meshes. As usual all the gory details of the technique in the below paper preprint and a working open source implementation in the next versions of MeshLab.




[1] Nico Pietroni, Marco Tarini, Paolo Cignoni, Almost isometric mesh parameterization through abstract domains, IEEE Transaction on Visualization and Computer Graphics, Volume In press - 2009

MeshLab V1.2.1 Released!

Initially this release was planned just as a bug fixing release (a really needed one!): a couple of really annoying bugs infiltrated the 1.2.0 release, causing crashes for all the tools that involved a marching cube processing and malfunctioning of the U3D exporting. Now they should work well.

In practice it is a feature rich release: as a bonus we have added some new nice functionalities (thanks to M. Sottile for implementing them): Convex Hull, Alpha shape, Voronoi Filtering, and Visible points filters. These filters rely on the well known Qhull convex hull library.
Convex hulls and Alpha shapes do not need extensive introduction, but a few notes on the two other filters are probably needed.

Voronoi filtering implements the homonym surface reconstruction algorithm by Nina Amenta and Marshall Bern that is able to reconstruct a nice interpolating triangulated mesh from a point clouds. It requires nicely sampled, low noise point clouds, but it works well.

The Visible Points filter implements a nice algorithm of Sagi Katz, Ayellet Talfor and Ronen Basri for computing direct visibility of point clouds. It is a really really simple and smart trick that works well and it is really easy to be implemented (once you have a convex hull implementation).

As usual, release notes are here in the wiki.