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Full Body 3D Scanning

Full Body 3D Scanning. Team KIPPA Sam Calabrese, Abhishek Gandhi, Changyin Zhou {smc2171, asg2160, cz2166}@columbia.edu. Outline. Background Motivation Our Plan Data Capture Data Processing Result Comparison Discussion.

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Full Body 3D Scanning

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  1. Full Body 3D Scanning Team KIPPA Sam Calabrese, Abhishek Gandhi, Changyin Zhou {smc2171, asg2160, cz2166}@columbia.edu

  2. Outline • Background • Motivation • Our Plan • Data Capture • Data Processing • Result Comparison • Discussion

  3. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Background • Laser Scanner using TOF • High precision, long range, slow • Laser Scanner using triangulation method • High precision, smaller range, occlusion problem, slow • Image-based method using triangulation method (motion, stereo, focus/defocus, shading…) • Relatively low precision and resolution • Pose assumption to the surface • Fast (can be real-time) • .. With Structured Light • Improve the precision and resolution • Indoor

  4. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Motivation • Lots of people dream to have an accurate 3D model of themselves • Lots of applications with the real 3D model (Animation, Augmented Realistic and even Clothes Design…) • Difficulties • Laser scanner – too slow to scan a live person (moving and non-rigid) • Image-based method – not enough resolution and precision (even with structured light) • Laser may hurt the eyes

  5. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Our Plan • Image-based method to model the head • Laser scanner to capture the body • Proper experiment settings to minimize the model movement during the scanning • Proper post-process to tolerant slight movement • Software to merge them together • Map skins and do animation afterward

  6. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Data Capture - Head FaceGen: www.facegen.com Use face symmetric, a large set of 3D Face models (gender, ages, races..)

  7. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Data Capture -Body • A professional model + a large private room + 4 hours • Tripods to rest the arms (10 cm lower than the shoulders) • Mark the feet position • A camera to track the movement • Scanner is as high as the shoulder • The distance to scanner ranges from 2.5m to 3m

  8. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Data Capture –Body (cont’d) • Four scan points (Frontal/Back x Left/Right) • Ten targets around for further registration • Precision set to 2mm • ≈10 min for each scan, but a long time to move the scanner • 90 – 130 K vertices for each range data

  9. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Data Capture –Body (cont’d) • Registration with Cyclone • The body movement causes big problems • we need more stronger non-rigid mesh merging methods

  10. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison MeshLab • Initially used to convert PTX to PLY • Problems arose with the original PTX files from Cyclone • Used MeshLab to reorient • Used again to clean data and resurface after VRip surfacing

  11. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Scanalyze • Used to re-register the Range scans following the initial errors • Prepares for VRip directly by saving out .conf and .xf files which VRip reads to orient the different scans to each other while maintaining their original orientation

  12. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison VRip • Used to create a surface from the registered point cloud • Uses view direction and ramp weights to get confidence about each vertex • Sampled at .002m in each direction per voxel • Used ramp weight of .004m with slight increase in standard weights

  13. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison PlyCrunch and Meshlab (again) • PlyCrunch is Packaged with VRip • Decimated the Mesh from 3.5 million polygons to just over 10,000 • Full of holes because Plycrunch deleted Triangles, but left proper vertices. • Meshlab used to clean and resurface resulting mesh, filled most of the holes

  14. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison 3dsMax • Used to finalize mesh by capping the remaining holes and smoothing the result. • Used to attach the head from facegen • Attached a skin shader for Mental Ray, one of the built in Renderers which used Sub-Surface Scattering for realism, modified to match the texture from FaceGen • Rigged with Biped object • Animated with stock Motion Capture Data • Rendered into animations

  15. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison 3dsMax

  16. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Result Comparison • We compared our output with that of two professional companies : • Headus – based in Australia • AvatarMe (initially developed by University of Surrey) • Criteria for Comparison : • Resolution of the output • Accuracy of scanned data • Error prevention during scanning • Cost/Ease of setup

  17. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Resolution of the output • We made way for Quality via Quantity • KIPPA • Number of Polygons in original scan • Scan 1 : 168,855 • Scan 2 : 179,868 • Scan 3 : 174,686 • Scan 4 : 101,010 • Total number of points : 500,000 • Data Density : 2mm • Others • Data Density 4 mm (Headus) • Average number of points : 300,000

  18. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Resolution of the output AvatarMe KIPPA

  19. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Accuracy of Scanned Data(smoothness, details captured) • Used more than just one good software – Vrip- 3,858,996 MeshLab- 3,858,783 PlyCruch- 10,019 MeshLab- 15,056 3ds Max- 15,080 93,108

  20. Background | Motivation | Our Plan | Data Capture | Data Processing | Result Comparison Error prevention during scanning &Cost/Ease of setup • KIPPA • We didn’t have a pre-defined model/shape against which to map our data.(explain) • Cheap/Simple set-up • Separate scanning for head and body • Limited body movement below the neck • Others • WBX Platform(explain) (movable platform, cables, etc.) http://www.cyberware.com/documentation/digisize/www/info/WBXPlatform.html

  21. Discussion & Conclusion • Reduction and dealing with motion… • Repositioning the scanner was difficult… • Problems during transformation… • Efficient use of markers… • Ignored hair… • Missed details of hands and toes… To conclude, 3D human scan was a very interesting problem to deal with. We managed to get a satisfying output in a very efficient manner. It was enjoyable learning and using softwares such as Vrip, Cyclone, MeshLab, PlyCrunch and 3ds Max.

  22. References & Resources • ALLEN, P., 2007. 3d photography 2007 fall. Class notes on Active 3D Sensing. • ALOIMONOS, Y., AND SPETSAKIS, M. 1989. A unified theory of structure from motion. • BERALDIN, J., BLAIS, F., COURNOYER, L., GODIN, G., AND RIOUX, M. 2000. Active 3D Sensing. Modelli E Metodi per lo studio e la conservazionedell’architetturastorica, 22–46. • BLAIS, F., PICARD, M., AND GODIN, G. 2004. Accurate 3d acquisition of freely moving objects. In 3DPVT04, 422–429. • BLAIS, F. 2004. Review of 20 years of range sensor development. Journal of Electronic Imaging 13, 231. • DHOND, U., AND AGGARWAL, J. 1989. Structure from stereo-a review. Systems, Man and Cybernetics, IEEE Transactions on 19, 6, 1489–1510. • DU, H., ZOU, D., AND CHEN, Y. Q. 2007. Relative epipolar motion of tracked features for correspondence in binocular stereo. In IEEE International Conference on Computer Vision (ICCV). • NAYAR, S., WATANABE, M., AND NOGUCHI, M. 1996. Realtime focus range sensor. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 12, 1186–1198. • RUSINKIEWICZ, S., HALL-HOLT, O., AND LEVOY, M. 2002. Real-time 3D model acquisition. Proceedings of the 29th annual conference on Computer graphics and interactive techniques, 438–446. • SCHECHNER, Y., AND KIRYATI, N. 2000. Depth from Defocus vs. Stereo: How Different Really Are They? International Journal of Computer Vision 39, 2, 141–162. • WATANABE, M., AND NAYAR, S. 1998. Rational Filters for Passive Depth from Defocus. International Journal of Computer Vision 27, 3, 203–225. • ZHANG, R., TSAI, P., CRYER, J., AND SHAH, M. 1999. Shape from shading: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 8, 690–706. • Facegen: http://www.facegen.com, Meshlab: http://meshlab.sourceforge.net/ • Vrip, Scanalyze, Plycrunch: http://graphics.stanford.edu/software/vrip/, 3Ds Max: www.autodesk.com/3dsmax • Prometheus : http://personal.ee.surrey.ac.uk/Personal/A.Hilton/research/PrometheusResults/index.html • Headus : www.headus.com/au/3D_scans/index.html • TC Square : http://www.tc2.com/what/bodyscan/index.html • Cornell University Body Scan : http://www.bodyscan.human.cornell.edu/scene0037.html

  23. Thanks to Prof. Allen, Karan, Matei and Paul for your kindly help and support. Special thanks to our model, Daniel, for his professional, passion and great cooperation.

  24. Any Questions… ~Thank You (team KIPPA)

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