30. 3D Point P
x, x‘: Image correspondence between image 1 and image 2
Projection centers (EO+IO)
Viewing rays
① Image Matching Correspondence x x‘
② Exterior + Interior orientation Viewing rays
③ Intersection of viewing rays 3D Point P
30
Image Matching
35. Stereo
Great overview – the Middlebury Stereo Page
» D. Scharstein and R. Szeliski. (2002)
A taxonomy and evaluation of dense
two-frame stereo correspondence
algorithms.
» Datasets
» Overview of methods
» Automatic benchmark
» http://vision.middlebury.edu/stereo/
36. Data fusion
Exploit redundancy
Image space
» Use epipolar relations
» Corresponding measurements
from image matching
+ fast data access
+ balance actual measurement
+ topology is available
-- relation limited to matching
(weak on small baselines) 36
Object space
Use actual 3D data
e.g. analysis in local neighborhood
+ local geometry is analyzed
+ indepedent validation
+ no image matching required
-- expensive data access
-- topology is challenging
P
xb
xm1
xm2
d
37. Stereo
Approach: Normalized Cross Correlation (NCC)
» Compare local mask for each pixel (e.g. 9 x 9 pixels)
» NCC: „sliding normalized dot product“
» High correlation match
Image source: https://siddhantahuja.wordpress.com/tag/normalized-cross-correlation/
38. Stereo
Approach: Scanline Optimization
» Dynamic programming
» Consistency along
epipolar line
streaking effect
Image source: Behzad Salehian ; Abolghasem A. Raie ; Ali M. Fotouhi ; Meisam Norouzi (2013).
Efficient interscanline consistency enforcing method for dynamic programming-based dense stereo matching algorithms
39. Stereo
Approach: Belief Propagation
» Message passing algorithm
» Usable for global optimization
» Popular:
• Bayesian networks
• Markov random fields
» Similar: graph cuts
Exact minimum solution
Computationally rather expensive
Image source: Klaus, A., Sormann, M., & Karner, K. (2006, August).
Segment-based stereo matching using belief propagation and a self-adapting
dissimilarity measure. In Pattern Recognition, 2006. ICPR 2006.
40. Stereo
Approach: Semi-Global Matching
• Matching: dense, intensity-based
• Global: optimization approach using a global model
• Semi: approximation fast numerical solution
Castle Neuschwanstein, Bavaria, Germany
source: Hirschmüller, Heiko (2005) – Accurate and efficient stereo processing by Semi Global Matching an Mutual Information
Intensity image Disparity image using a
correlation matching method
Disparity image using
Semi Global Matching
41. Stereo
Approach: Semi-Global Matching
» SGM Optimization approach:
disparities similar to neighboring pixels are preferred
Assignment of costs for each possible disparity on each pixel
• Costs for the similarity of the grey value ( similar low costs )
• Additional costs for disparity jumps forces smooth surfaces 41
Disparity along a path L in the image
02.03.2015
42. Stereo
Approach: Semi-Global Matching
» Recursive cost aggregation on paths through the image
Base image, pixel
pi
Match image, pixel qi,j
Minimal costs
Costs c(pi ,qi,j)
43. • Problem: SGM cost structures require large
amount of memory
• Solution:
• Reduce disparity search ranges to a tube
around actual surface
• Coarse-to-fine approach: Initialize search
ranges /tubes using low resolution imagery
Fast
Low memory requirements
x [pix]
disparity
[pix]
x [pix]
disparity[pix]
Rothermel, M., Wenzel, K., Fritsch, D., Haala, N. (2012).
SURE: Photogrammetric Surface Reconstruction from Imagery.
Stereo
Approach: Semi-Global Matching – tSGM variation
46. » Steve Seitz, Brian Curless, James Diebel, Daniel Scharstein, Richard Szeliski
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR
2006, vol. 1, pages 519-526.
» http://vision.middlebury.edu/mview/
Stereo
Great overview – the Middlebury Multi-View Stereo Page
47. » Yasutaka Furukawa and Jean Ponce,
Accurate, Dense, and Robust Multi-View
Stereopsis, CVPR 2007
» Steps:
• Match: find features
• Expand: grow patches
• Filter: using visibility constraint
» Mesh using regulation constraints
» Available Open Source as PMVS
Multi-View Stereo
Approach: Grow patches around feature points
48. » Deseilligny, M. P., & Clery, I. (2011). Apero, an open source bundle adjusment
software for automatic calibration and orientation of set of images. 3D Arch 2011
» Multi-Stereo matching for one reference image (available as Open Source: MICMAC)
» Graph cut & dynamic programming optimization
Multi-View Stereo
Approach: Multi-stereo matching
49. Multi-View Stereo
Approach: Depth maps and Volumeteric Range Image Integration
» M. Goesele, N. Snavely, B. Curless, H. Hoppe, S. Seitz (2007).
Multi-view stereo for community photo collections, ICCV 2007
» Grow sparse points from SfM
» Estimate refined depth maps with
photoconsistent normals
» Integration using
Volumetric Range Image Integration
Brian Curless and Marc Levoy,
Stanford University (1996):
A Volumetric Method for Building
Complex Models from Range Images.
http://grail.cs.washington.edu/
software-data/vrip/
50. 50
Multi-View Stereo
Approach: Depth maps and Volumeteric Range Image Integration
Korcz, D. (2011). Volumetric Range Image Integration. Diplomathesis, ifp, University of Stuttgart
Dataset: Middlebury Multi-View Stereo
evaluation, Temple
51. 1. Build volumetric space
entity: voxel, a volumetric pixel
2. Project range image into voxel space
3. Compute Signed Distance Field
4. Extract optimal surface
51
Korcz, D. (2011). Volumetric Range Image Integration. Diplomathesis, ifp, University of Stuttgart
Multi-View Stereo
Approach: Depth maps and Volumeteric Range Image Integration
52. 52
Korcz, D. (2011). Volumetric Range Image Integration. Diplomathesis, ifp, University of Stuttgart
Multi-View Stereo
Approach: Depth maps and Volumeteric Range Image Integration
Signed Distance Field for „Dino“ dataset
53. 53
Korcz, D. (2011). Volumetric Range Image Integration. Diplomathesis, ifp, University of Stuttgart
Multi-View Stereo
Approach: Depth maps and Volumeteric Range Image Integration
54. » Iso-Surface extraction using Marching Cubes algorithm
» Hole filling by space carving method
54
Multi-View Stereo
Approach: Depth maps and Volumeteric Range Image Integration
55. » [Zach et al., 2007]: simple averaging of signed distance fields without further
regularization causes inconsistencies
• due to frequent changes of sign
introduction of additional regularization force
energy minimization
uses total variation norm (TV-L1), [Rudin et al, 1992]
Smoothness term allows
• regulatization
• noise suppression
• outlier rejection
55
[Rudin et al., 1992] Rudin, L. I., Osher, S., and Fatemi, E. (1992).
Nonlinear total variation based noise removal algorithms.
[Zach, 2008] Zach, C. (2008). Fast and High Quality Fusion of Depth Maps.
Multi-View Stereo
Depth maps and Volumeteric Range Image Integration + Total Variation
56. 56
Multi-View Stereo
Depth maps and Volumeteric Range Image Integration + Total Variation
Source: Korcz, D. (2011). Volumetric Range Image Integration.
57. » Vu, H.; Keriven, R.; Labatut, P. and
Pons, J.-P (2009). Towards high-resolution
large-scale multi-view stereo. CVPR, 2009
» Rough dense point cloud through
normalized cross correlation (NCC)
» minimum s-t cut global optimization
with visibility filtering
» Mesh refinement with photo consistency
Multi-View Stereo
Approach: rough point cloud and mesh refinement
58. » Rothermel, M., Wenzel, K., Fritsch, D., Haala, N. (2012).
SURE: Photogrammetric Surface Reconstruction from Imagery.
» Approach:
1) Stereo matching using tSGM
2) Multi-ray triangulation
3) Object space fusion, e.g.
• DSM
• Volumetric point cloud filtering
• Meshing
Multi-View Stereo
Approach: stereo matching, multi-ray triangulation, object space fusion
P
xb
xm1
xm2
d
Images
Epipolar images
Disparity images
61. SURE: Point Cloud Fusion for 2.5D Surfaces
» Fusion of 3D point clouds to 2.5D surface models
3D point cloud stereo
matching
3D point clouds multi-view
stereo matching
Fusion of point clouds to
2.5D surface model
71. SURE: Out-of-core point cloud filtering
» Retrieve locally densest point cloud
removal of redundancy
» Validate points
keep only points, which are
validated by other point clouds
» Adapt resolution locally
» Scalable – out-of-core octree
Wenzel, K., Rothermel, M., Fritsch, D., & Haala, N. (2014).
Filtering of Point Clouds from Photogrammetric Surface Reconstruction
72. SURE: Out-of-core point cloud filtering
(IGI DigiCam Penta)
Imagery courtesy of Aerowest GmbH