interactively modeling with photogrammetry pierre poulin mathieu ouimet marie-claude frasson dép....
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Interactively Modeling with Photogrammetry
Pierre PoulinMathieu Ouimet
Marie-Claude Frasson
Dép. Informatique et recherche opérationnelle
Université de Montréal
Motivation
• Photo-realism is difficult to achieve
• Important recent progress in rendering
• Acquiring realistic 3D models is still a major hurtle
• Important needs for realism, special effects in movies, CAR, etc.
Extracting 3D models from photographs
Computer Vision / Robotics
• 3D models do not satisfy most of the visual accuracy necessary in graphics
• Fully automatic systems are challenging :– false correspondences– missed edge detections– noise– textures
• Provide much inspiration in our system
Our Interactive Reconstruction System
• User knows the 3D models / textures
• User is responsible for everything
• User interactions :– User draws 2D primitives– User puts the 2D primitives in correspondences– User adds 3D constraints– User extracts a unified texture
Drawing 2D Primitives
Correspondences
3D Constraints
Perpendicularity
Parallelism
Co-planarity
Extracted Textures
Reconstruction Process
• Incremental
• Robust
• Intuitive
• Provides good graphics models
• Labor-intensive
The Camera
• Our camera is a transformation matrix
• No explicit need for real camera parameters
1
0000
1098
7654
3210
TTT
TTTT
TTTT
Reconstructing a Camera
• 6 or more 2D-to-3D point correspondences
(0,1,0)
(0,1,1)
(1,1,0)
(0,0,1)
(1,0,0)
(1,0,1)
Reconstructing a Camera
• Least-squares to compute all Ti
• Solution with SVD– Fast– Robust– Always provides a solution– Conditions for accuracy similar to non-linear
Reconstructing a 3D Point
• Incidence of 3D point on planes
• Least-squares to compute each (x,y,z)
• Polygons as set of 3D points
Reconstructing a 3D Line
• Plücker coordinates of a 3D line
Additional 3D Constraints
• Co-planarity
• Parallelism
• Perpendicularity
• Weights can be used to alter the importance of certain constraints
Weights
Iterating
• Better cameras give better 3D geometry
• Better 3D geometry give better cameras
• Iterations between the two improve both
Convergence
Recovering Texel Colors
u
v
u
v
t
s
Texture map 3D Polygon
2D Images
t
t
s
s
Occlusion Testing
Zones of Occlusion3D Model
2D Image
Linear Fit
• Misalignments due to imprecisions in the 3D model and its cameras
• 2D transformation matrix using least-squares
Unifying Texel Criteria
• Clustering to discriminate view-dependent colors for a texel
• Other metrics used to weight valid texels :– Projected area (adaptive sampling)– Texture quality
Two Scenes with Cubes
Desktop
Lego Tower
Coffee Pot
Conclusions
• User knows best
• Satisfying 3D models and extracted textures
• Labor-intensive
Future Work
• Better user interface
• Error detection
• Radiances, reflectances, and global illumination
• Displacement maps on 3D primitives
• Bounds on reconstructed information