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Research at VIMS Lab www.cis.udel.edu/~vims Gowri Somanath, Rohith MV, Abhishek Vincent Ly, Scott Sorensen, Philip Saponaro, Guoyu Lu, Xiaolong Wang, Ryan O'Dowd Chandra Kambhamettu
Research at VIMS Lab
www.cis.udel.edu/~vims
Gowri Somanath, Rohith MV, Abhishek
Vincent Ly, Scott Sorensen, Philip Saponaro, Guoyu Lu, Xiaolong Wang, Ryan O'Dowd
Chandra Kambhamettu
Current projects
• Nonrigid Motion Analysis
• Tumor analysis
• Object categorization and recognition
• Stereo analysis and reconstruction of ice
topography
• Single camera stereo
Nonrigid Motion: Main Goals
• Investigate, design and implement methods for Nonrigid motion analysis using intrinsic differential geometry measures.
• Develop algorithms for Nonrigid Motion Classification and segmentation.
• Develop metrics for evaluating the performance of motion models, and approaches to apply simple to complex hierarchy of motion models.
• Integrate developed algorithms into the structure and motion recovery process from 2D image sequences.
• Devise quantitative and qualitative evaluation measures of Nonrigid motion analysis for a variety of applications.
3D Images
Stereo/Multiple Views
Monocular
Structure & Nonrigid
Motion Analysis System
(SMAS)
3D Scene Flow
Estimation
Nonrigid Motion
Analysis
Point Correspondences
Nonrigid Motion Models
Dense 3D Map
- Complete and automatic system to compute dense 3D
scene flow and scene structure.
- Seamless integration of 2D motion and stereo constraints.
Nonrigid
Approaches
Rigid
Isometric
Conformal
General
- General hierarchical system to integrate
local and global motion analysis for
shape-based and non-shape based applications.
- Initial Structure is assumed.
System Overview
- Differential geometry based methods
- Geometric & spline based methods
Applications and Results
Scene Flow
Cloud Motion
Facial Motion
Protein Docking
Rigid and Nonrigid motion analysis based on
surface segments
Lung Tumors: GGOs
GGO Extracted
Stereo
• How do we see depth? - Stereo
Stereo
• Meeting point of 2 lines
Reconstruction recipe
• Find the two lines
– Start with searching for the same point in the
two images
– Define the rays in the same co-ordinate
system
• Figure out the pinhole positions
• Figure out where the pinholes are wrt each other
• Now see where the two lines meet – that’s
the 3D point
Prism Cameras
Indoor Stereo
Ice cameras
Dimension=20mt by 60mt; scene is 15-25 mt from camera
Ice cameras
Mean error between the
stereo and 3D laser
scanned surfaces when
smoothness parameter
α is varied.
Reconstruction from laser scanner (100,000 vertices)
Reconstruction from stereo (mesh with 1,337,079 vertices)
• Structure based
– Surface roughness
– Ice thickness
• Appearance based
– Snow cover
– Floe size distribution
– Lead structure and polynya density
– Ice color
Products for habitat study
Proposed setup
20m 50m
20m
30m
Stereo Swath
Video Swath
Stereo pipeline
Calibration Images
Calibration
Data
Field Images Disparity
True scale 3D reconstruction
3D Reconstruction
(a) One image from the sequence captured on river ice.
(b) Reconstructed point cloud in 3D of the region shown in (a)
Structure from motion
Video frames Feature tracks
3D Reconstruction
of feature points
Surface reconstruction by interpolation
