1. 3D reconstruction from uncalibrated images
Young Ki Baik
CV Lab

2. Contents Introduce Conditions for 3D reconstruction and Solution
Basic geometrical theory
Overview – 3D reconstruction
Conditions for 3D reconstruction and Solution
Correspondence
Camera parameters and motion
Results
Experimental results and demonstration
Future Works

3. Camera system for obtaining images
Introduction(1)
Mapping to images
3D point
3D object
mapping
Image plane
Camera
Camera
Camera system for obtaining images

4. 3D reconstruction system to make 3D object
Introduction(2)
3D reconstruction from images
Point correspondence
Camera parameter and motion
3D point
3D object
Camera
Camera
3D reconstruction system to make 3D object

5. 3D reconstruction from uncalibrated images
Overview
Image Sequence
Feature Extraction/ Matching
Relating Image
Projective Reconstruction
Auto-Calibration
Dense Matching
3D Model Building

6. Conditions for 3D reconstruction
Correspondence
Feature extraction
Harris corner method
SIFT method
Scale Invariant Feature Transform
Initial feature matching
Template matching (Image base descriptor)
Descriptor (SIFT-d, PCA-d, SIFT-d+PCA-d, …)
Feature matching
RANdom SAmple Consensus
To eliminate outlier

7. Conditions for 3D reconstruction
Correspondence
Guide matching
To get more correspondence
Using previous features and Geometry information
About 2 times more correspondence
Geometry based distance value using fundamental matrix
Correlation based cost value

8. Conditions for 3D reconstruction
Camera parameter and motion (Using Self-calibration)
Dual Absolute Conic
Hartley ’94 / Hartley ’99, David Nistér IJCV 2004 ( + cheirality solution )
Dual Absolute Quadric
Triggs’97
M.Pollefeys et al. PAMI’98, ECCV 2002, IJCV 2004
Dual Absolute Quadric
M. Pollefeys

9. Conditions for 3D reconstruction
Constraints for self-calibration
Constant internal parameter
Fixed camera
K1 = K2 = …
Known internal parameter
Rectangular pixel : s = 0
Square pixel : s = 0, fx = fy
Principle point known : ( ux , uy ) = image center

10. Experiments and results
Result using rig
Rig
Calibration using vanishing point
DAQ (using weighted linear equation)
Using the calibration rig information
Using the manual vanishing points input
Self-calibration result using rig correspondence only
Self-calibration result is similar to the method using calibration rig.

11. Experiments and results
Real scene test
Assuming that self-calibration works well

12. Experiments and results
Manual input to check self-calibration results
Points : Correspondence information
Line : Connection information

13. Experiments and results
Test 1 (Pinball machine : 3 images)
Key points
Match
Fig.1
Fig.2
Fig.3
5476
5609
8530
Fig.1-2
Fig.2-3
Fig.1-2-3
Initial
match
146
196 41
RANSAC
104
124
Guide
230
160 67
281
202

14. Experiments and results
Test 2 (Mask : 3 images)
Key points
Match
Fig.1
Fig.2
Fig.3
1837
1420
1888
Fig.1-2
Fig.2-3
Fig.1-2-3
Initial
match
102
158 4
RANSAC 35
100
Guide
150
258 23 78
186

15. Experiments and results
Test 3 (Building : 6 images)
Key points
Match
Fig.1
Fig.2
Fig.6
959
1064
1177
Fig.1-2
Fig.2-3
Fig.1~6
Initial
match
386
377 30
RANSAC
227
254
Guide
465
484 35
308
309

16. Experiments and results
Test 4 (House : 5 images)
Key points
Match
Fig.1
Fig.2
Fig.5
3013
3084
2873
Fig.1-2
Fig.2-3
Fig.1~5
Initial
match
1023
973 15
RANSAC
656
716
Guide
1186
1216 54
911
909

17. Future works Quasi-Dense matching technique and reconstruction
To get more reliable results
Full side 3D reconstruction
Using attaching algorithm
Bundle adjustment algorithm
To reduce error
Full 3D reconstruction system
Dense matching and 3D modeling