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The "Stanford Bunny" The Stanford 3D Scanning Repository In recent years, the number of range scanners and surface reconstruction algorithms has been growing rapidly.
Many researchers, however, do not have access to scanning facilities or dense polygonal models. The purpose of this repository is to make some range data and detailed reconstructions available to the public.
Here's how the models in this repository were created: Scanning and surface reconstruction The first set of models below, called "The Stanford Models", were scanned with a Cyberware MS scanner, with the exception of Lucy, who was scanned with the Stanford Large Statue Scannerdesigned for the Digital Michelangelo Project.
Both scanners are swept-stripe, laser triangulation range scanners. The triangulation calculations all the Stanford models except the Happy Buddha and Dragon were performed in hardware by the Cyberware scanner s. These last two models were acquired using Brian Curless's spacetime analysis.
Each scan takes the form of a range image, described in the local coordinate system of the scanner. To merge these range images, we must first align them together.
For all the Stanford models, alignment was done using a modified ICP algorithm, as described in this paper. These alignments are stored in ". Finally, the aligned range images are combined to produce a single triangle mesh a process sometimes called surface reconstruction using either zippering or volumetric mergingtwo methods developed at Stanford.
The entry for each model indicates which method was used. Implementations of both methods are currently available for download, respectively, at ZipPack and VripPack. The second method is the surface reconstruction method invoked by the Scanalyze software package used in the Digital Michelangelo Project.
Another software package that might be of interest is Volfillour diffusion-based hole filler for large polygon meshes.
All post-processing, including alignment, merging, editing, and polygon reduction, were done using Innovmetric 's Polyworks software.
These models come to us courtesy of Helmut Kungl. File format Unless otherwise noted, the range data and reconstructed models in this repository are stored in PLY files.
This format was developed at Stanford University, and the source code is available for download. Choosing ASCII makes it possible for someone unfamiliar with it to get a feel for the file format, and it avoids the problem of using the correct big-endian vs. To view PLY files, you can download our Scanalyze software package.
For converting PLY files to other formats, here are some converters we have or know about: Our utility for converting PLY files to Inventor files. Click here to download the executable. Richard Harding of the Sony Playstation group has contributed a ply-to-Maya plugin.
Starting with Maya 8. Click here to download it.
Here is his package. We have not tested any of these converters ourselves. Feedback or bug reports should be sent directly to the authors of these packages. The models in this archive are fairly widely used in the graphics, visualization, and vision communities.
Things people have done with these models include simplification, multi-resolution representation, curved surface fitting, compression, texture mapping, modeling, deformation, animation, physically-based simulation, texture synthesis, and rendering. The Stanford Bunny is particularly widely used, as surveyed by Greg Turk on this entertaining web page.
One use people have made of these models is as input for surface reconstruction algorithms, typically by stripping away the mesh connectivity and treating the vertices as an unorganized point cloud. We caution against this approach. Our zippering and volumetric range image merging methods produce smooth, usually manifold surfaces.
More specifically, they eliminate outliers in the range data, reduce noise, mask misalignments between range images, and generally hide many of the errors that arise naturally during 3D scanning.
In this sense, our reconstructed models do not constitute realistic input data for a surface reconstruction algorithm. If you want to experiment with a new reconstruction algorithm, and especially if you want to compare its performance against existing methods, then you should start with real range data.
For many of the models in this archive, we have made this raw data available.ii Project Editor Marvin Bassett ISBN Published by Air University Press in April Disclaimer The Air University Style and Author Guide provides guidance on writing, editing, and publishing matters related to official publi-.
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