3D Scanning Research at Princeton
Although 3D scanning has become a widely-used technique, most commercially
available systems acquire data at relatively low rates and require careful
control over the setup and calibration of cameras and/or active light sources.
We are working on making range scanning systems faster, more flexible, more
robust, and easier to use by developing novel scanner designs and 3D scan
processing algorithms, including combinations of structured light,
spacetime stereo, photometric stereo, and efficient registration algorithms.
3D Scanning System Design:
3D Scan Registration:
Dense 3D Reconstruction from
Diego Nehab, Tim Weyrich, and Szymon Rusinkiewicz.
Computer Vision and Pattern Recognition (CVPR), 2008.
Mark Young, Erik Beeson, James Davis, Szymon Rusinkiewicz, and Ravi Ramamoorthi.
Computer Vision and Pattern Recognition (CVPR), 2007.
- Improved Sub-pixel Stereo
Correspondences through Symmetric Refinement.
Diego Nehab, Szymon Rusinkiewicz, and James Davis.
International Conference on Computer Vision (ICCV), October 2005.
- Efficiently Combining Positions
and Normals for Precise 3D Geometry.
Diego Nehab, Szymon Rusinkiewicz, James Davis, and Ravi Ramamoorthi.
ACM Transactions on Graphics (SIGGRAPH 2005), 24(3), August 2005.
- Spacetime Stereo: A Unifying
Framework for Depth from Triangulation.
James Davis, Diego Nehab, Ravi Ramamoorthi, and Szymon Rusinkiewicz.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 27(2):296-302, February 2005.
- Spacetime Stereo: A Unifying Framework for Depth from Triangulation.
James Davis, Ravi Ramamoorthi, and Szymon Rusinkiewicz.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 359-366, June 2003.