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Posted by on May 19, 2016 in Uncategorized | 0 comments

Photogrammetry, or, 3D scan with a plain ol’ camera

Photogrammetry, or, 3D scan with a plain ol’ camera

After a few years of plodding along the hackerspace / shadetree engineering path, I have encountered the same problem multiple times in multiple forms.  Once in a while, you need to translate an object which exists in the real world, into the digital world. 

Let’s say you need a 3D model of the members of your band for a cool video.

 
In the old days, this was a tedious and manual process, to digitize anything, lacking any pre-existing tools that could easily facilitate the project.  Take Rebecca Allen, for example.  She worked for TWO YEARS to create the above 1986 Kraftwerk video.  Here she is pictured with a reference model of drummer Karl Bartos, plotting each point by hand with a digitizer, using homebrew software.  

Even if robust digitization tools existed 30 years ago, computer processing power at the time was generally unable to adequately handle the sheer volume of computation required for rendering even the simplest of 3D scenes. 

In 1982, it was even HARDER to digitize objects.  Remember Tron?

The Evans and Sutherland Picture System 2, which was used to render Tron, had a whopping 2 Megabytes of RAM, and 330 Mb of disk space.  A large percentage of the effects in this film were actually Rotoscoped by hand, rather than using a computer to add visual effects. 
Today, you can buy a Google Tango tablet, designed specifically for 3D imaging, with 4 Gb of RAM, a flamethrowing nVidia processor, all sorts of bells and whistles, for ~$500.  Put in relative terms, an iPad2 has equivalent GFLOP processing power of the most powerful Cray supercomputer from 30 years ago.  
You carry this around in your bag, like it’s no big deal, and use it mostly for Flappy Bird. 

But I digress.

The point I’m trying to make, is that we are now carrying enough processing power around in our pockets to be able to accomplish sophisticated 3D imaging, which was previously computationally prohibitive. 

Beyond the absurd availability of computational power today, lots of research has been performed in the last 30 years in the field of computer simulation, ray tracing, and other relevant algorithms.  All this research adds together to allow this deluge of computer power to be specifically focused on the task at hand, in this case 3D imaging and reconstruction. 

In previous blog posts, we covered a low-cost scanning technique using the Microsoft Kinect sensor.  Initially not intended for use as a 3D scanner, massive development of the Kinect ecosystem by Microsoft and others has created a wake of alternative uses for the Kinect hardware.

One challenge with scanning using the Kinect is the trade-off between scan volume and resolution.  The Kinect is capable of scanning physical space ranging from 0.5 to 8 meters in size. Instead of pixels as you would have in a 2D environment, the Kinect tracks “voxels” in a 3D environment, in an array of roughly 600 x 600 x 600 elements.  In the highest quality settings, this makes for an minimum tolerance of +/- 1 mm of error, about 1% overall, in the resulting scan data.  This is great resolution when scanning items about the size of a shoe box, 0.5 m^3, but sometimes you want to scan larger objects that the Kinect would struggle to visualize with high enough resolution.

What about scanning objects smaller than 0.5 m^3?  The Kinect has a miminum scanning distance of ~600 mm, and has a difficult time visualizing small features on small parts. 

Using photogrammetry (specifically, stereophotogrammetry), all you need is an array of photographs from different angles of the same scene, and a properly configured software stack. 

There are a few photogrammetry solutions on the market ranging from free to very expensive.  Most of these softwares essentially do the same thing, the main distinction being that 123DCatch performs remote processing on the cloud, where CapturingReality performs the required calculations locally.  Due to this fact, your choice of software boils down to what quality of hardware you’re using. 


My toolchain of choice for this process is twofold: VisualSFM and Meshlab.  Both of these tools are free, mostly open-source*, and quite robust once you know how to coax the proper filtered data out.  The main benefit of this toolchain is that they’re freely available for Linux / Mac / Windows.  It can even be done without CUDA cores, although it seems that some optimization for the process is achieved with using CUDA architecture GPUs

VisualSFM is used to sort an array of images, and apply the SIFT algorithm for feature detection on each image.  This processes each image using the Difference of Gaussians, one method for computerized feature recognition, along with a comparison between each frame.  The software is then able to infer a relative position and orientation for each camera.

Meshlab is used to perform a mathematical reconstruction of the VisualSFM output.  VisualSFM outputs a cartesian point cloud, and it’s your job as the creative human to make sense of that data (turns out 3 Dimensional matching has been proven to be NP-hard).  
A point cloud by itself isn’t inherently useful.  With Meshlab, we perform a conversion of the noisy point cloud to a high quality, watertight triangular mesh which can then be used in all sorts of applications like reverse engineering, 3D printing, VR and AR, computer vision, et cetera.  
We first perform a Poisson surface reconstruction to create a solid, triangular-faceted surface with a high quality alignment to the original point cloud.  The resulting mesh can tend to be noisy, so a few filtering algorithms are applied to smooth the surfaces and edges and clean the outliers.  Essentially, all you’re doing is noise filtering.
Mesh size is of crucial importance for computability.  Sometimes the meshes reconstruct with millions of faces, which can be challenging to process on anything but modern gaming rigs with giant GPUs.  Furthermore, our resulting reconstruction is rough, aesthetically approximating a surface, rather than being a 99% dimensionally accurate representation of the surface.  Such a high quality of reconstruction is approachable using strictly photogrammetric or structured light techniques, but probably better suited to Time-of-Flight laser scanning techniques like that of the NextEngine.  TOF scanners can achieve micron-scale resolution, unlike triangulation scanners like the Kinect.  
Do we need to reconstruct a scene in a video game, requiring a low quality model with a high quality, registered texture?  Do we need to recreate a shape with a high dimensional accuracy, with no consideration to texture or color?  Photogrammetry can accomplish both, but is better at the former.

Meshlab is used to robustly modify the reconstructed mesh surface with mathematical processes (you should probably also look at Meshmixer).  Some of the more challenging, opaque problems can hide deep within the mesh, like a single non-manifold vertex which may never appear in the visual rendering.  This can be solved with a quick selection filter, and deleting the offending geometry.  “Quadric Edge Collapse Decimation” is used to reduce the polygon count of the resulting surface.  My favorite filter lately has been “Parameterize and texturize from registered rasters” which creates an awesome texture map with VisualSFM output. 

Once you have the clean, reconstructed surface, save the file somewhere memorable.  VisualSFM has an output file called “bundle.rd.out” which is a sparse reconstruction of the surface, along with “list.txt” which is the list of registered raster images we’re going to use to apply color to the reconstructed surface. By importing the reconstructed surface into this new workspace, we can superimpose the aligned raster images with the reconstructed mesh, then projecting the color with a little software magic. 

The resulting surface can be further refined by applying different shaders and lighting schemes. 

Granted, there is a small amount of visual distortion in the resulting reconstruction and texturizing of the mesh.   I’m sure with a few dozen more images of this scene, along with more processing time, that would result in a more accurate volumetric representation of the scene.  To achieve a higher quality texture map, a little more love needs to be used when parameterizing the raster images onto the mesh.  
One thing to remember is that photogrammatically reconstructed surfaces have no inherent relation to scale.  This can be corrected with a comparison to a known reference dimension.  We could probably look up on Amazon the dimensions of the “Pocket Kanji Guide”, and appropriately scale the data.  In this instance, accuracy in scale isn’t the main intent.  If inserted dimensionally accurate references into the photo, rescaling should be reasonably accurate.   Meshlab’s default output unit is in millimeters. 

Compare the result of our quick, admittedly low-quality reconstruction (using a dozen VGA-resolution images), versus one with hundreds of reference photos and processed overnight using expensive (but very good) software.  These images are probably taken on something with better than VGA resolution. 

A few limitations –

Since we’re using visible-light techniques, we have to deal with optical restrictions.  Reflections and shadowed surfaces are troublesome to reconstruct.  Diffuse, even lighting conditions are optimal for photogrammetric reconstruction, so try taking pictures outside on a cloudy day.  Lens choice is also important, with a 35-50 mm lens most closely approximating the human field of view with the least amount of distortion. 

Objects scanned with photogrammetry techniques should typically remain still while capturing data.  It’s possible to assemble a camera rig with n cameras in various orientations around a common scene.  Multiple instantaneous views could then be processed using these techniques.

The SIFT algorithm works best when applied on images with lots of orientable visible features, like repeating vertical and horizontal lines and contrasting color; not so well with objects like a plain flower vase where all sides appear the same.  
The toolchain is painfully disjoint, requiring extensive domain knowledge and a mostly undocumented software stack to make sense of the subject.  We have yet to attempt importing the resulting mesh into engineering software like Solidworks, which would require conversion of file type using yet another piece of software.  We’ve used FreeCAD to convert the mesh to IGES format, but this can also be done with propietary software packages like GeoMagic.  The conversion can be non-trivial and lossy, akin to making vectors from rasters. 
A few benefits –

Apart from being a non-contact reconstruction method, photogrammetry lends itself well to scaling.  Very small or very large objects can be reconstructed with similar ease.  Your only limitation for mesh size is how much horsepower your workstation has.  Meshlab / VisualSFM can also be configured to run on AWS cloud, which has options for choosing GPU heavy machines. 

You can also grok crowdsourced images from Google, and use people’s vacation photos of visiting the Coliseum in Rome, feeding the resulting data into VisualSFM with impressive results.  Screen captures from videos?  No problem.  In fact, you could walk by your scan target with your cameraphone recording a high definition video of the target, and reconstruct these things later.  Soon, this is a process that will happen on-the-fly.
Only recently has technology become affordable enough and accessible enough to whimsically perform these types of operations on a large data set like a complex, textured 3D object.   Although Moore’s law is tapering off, processor power continues to get cheaper and smaller.  It’s exciting to consider what will develop in the near future as people continue to discover more efficient algorithms, better sensors, and more creative applications.  
What kinds of interesting things can you think of to use this technology for? 

*VisualSFM, by Changchang Wu, is a closed source program developed from scratch.  SiftGPU is Open sourced.  The SIFT algorithm, by David Lowe, is patented in the US to the University of British Columbia.  Meshlab is released using the GPL license.