PolyDepth: Real-Time Penetration Depth Computation · PDF fileDepth Computation Using...
Transcript of PolyDepth: Real-Time Penetration Depth Computation · PDF fileDepth Computation Using...
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PolyDepth: Real-Time Penetration
Depth Computation Using Iterative
Contact-Space Projection
Changsoo Je
Ewha Womans University and Sogang University
Min Tang, Youngeun Lee, Minkyoung Lee, and Young
J. Kim, Ewha Womans University
ACM Transactions on Graphics (ToG), Vol. 31, No. 1, Article 5, 2012
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Non-penetration Penetration
Interpenetration Problem
• Modeling problem
• Numerical problem
• Random sampling
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How to separate them?
Penetration Non-penetration
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Penetration Depth
Penetration Depth (PD)
Minimum translational distance to separate intersecting rigid objects
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Applications
– Rigid Body Dynamics
Point of impulse application [Tang et al. 2009]
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Applications
– Retraction-based Motion Planning
Retract in-collision samples to free space
[Zhang et al. 2008]
C-Free C-Obstacle
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Applications
– Penalty-Based 6DOF Haptics
Penalty Forces
[Kim et al. 03]
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Computational Complexity of PD
• Exact PD: 𝑂 𝑛6 for 𝑛 faces
𝔄
𝔅
𝔄 ⊕ 𝔅 Minkowski
Sums
(Translational
Contact Space)
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Main Contribution
The first realtime
PD algorithm for
general polygonal
models
Source codes available: google “polydepth”
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Main Contribution
• Tight upper bound
• Single global or multiple local solutions
• Iterative optimization using in/out
projections
• Efficient initialization strategies
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Contents
• Previous work
• Preliminaries
• Out-projection
• In-projection
• Initialization
• Results
• Conclusion
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Contents
• Previous work
• Preliminaries
• Out-projection
• In-projection
• Initialization
• Results
• Conclusion
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Previous Work
• Convex Polytopes [Cameron and Culley 1986], [Dobkin et al. 1993], [Agarwal et al. 2000], [Cameron 1997], [Bergen 2001],
[Kim et al. 2004]
• Nonconvex Polyhedra [Kim et al. 2002], [Redon and Lin 2006], [Lien 2008, 2009], [Hachenberger 2009], [Fisher and Lin 2001],
[Hoff et al. 2002], [Sud et al. 2006], [Tang et al. 2009], [Allard et al. 2010]
• Generalized Penetration Depth [Zhang et al. 2007], [Nawratil et al. 2009], [Ong and Gilbert 1996], [Ong 1993], [Weller and
Zachmann 2009]
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Previous Work - Limitations
• Too slow for interactive applications
• Non-Euclidean
• Lower bound solution
- Does not guarantee full separation
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Contents
• Previous work
• Preliminaries
• Out-projection
• In-projection
• Initialization
• Results
• Conclusion
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Minkowski Sum
• Minkowski sums for two sets
𝔄 ⊕ 𝔅 = 𝐚 + 𝐛 𝐚 ∈ 𝔄, 𝐛 ∈ 𝔅
𝔄 and 𝔅
𝔄 ⊕ −𝔅 = 𝐚 − 𝐛 𝐚 ∈ 𝔄, 𝐛 ∈ 𝔅
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Minkowski Sum
𝔄 𝔅
𝔄 ⊕ −𝔅
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𝐨𝔅
PD and Minkowski Sum
𝔄 ⊕ −𝔅
𝐨𝔄
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𝐨𝔅
PD and Minkowski Sum
𝔄 ⊕ −𝔅
𝐨𝔄 PD PD
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PD Estimation
𝐨
𝜕 𝔄 ⊕ −𝔅
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PD Estimation
𝐨
𝜕 𝔄 ⊕ −𝔅
Penetration depth
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PolyDepth Algorithm
• Local optimization
- Contact space localization
• Iterative optimization
- Repeated in- and out-projections
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Iterative Optimization
𝐨
𝜕 𝔄 ⊕ −𝔅
contact space
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Iterative Optimization 𝐪𝑓
𝐨
𝜕 𝔄 ⊕ −𝔅
contact space
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Iterative Optimization 𝐪𝑓
Out-Projection
𝐪0
𝐨
𝜕 𝔄 ⊕ −𝔅
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Iterative Optimization
𝐪0
𝐨
𝜕 𝔄 ⊕ −𝔅
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Iterative Optimization
In-Projection
𝐪1
𝐨
𝜕 𝔄 ⊕ −𝔅
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Iterative Optimization
𝐪1 Out-Projection
𝐪2
𝐨
𝜕 𝔄 ⊕ −𝔅
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Iterative Optimization
𝐪2
𝐨
𝜕 𝔄 ⊕ −𝔅
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Iterative Optimization
𝐨 In-Projection
𝐪3 𝜕 𝔄 ⊕ −𝔅 PD
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Contents
• Previous work
• Preliminaries
• Out-projection
• In-projection
• Initialization
• Results
• Conclusion
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Out-Projection
𝐪𝑓
𝐨
𝜕 𝔄 ⊕ −𝔅
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Out-Projection
𝐪0
𝐪𝑓
𝐨
Out-Projection
𝜕 𝔄 ⊕ −𝔅
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Continuous Collision Detection
(CCD)
Find the first time of contact (ToC) 𝜏
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Translational CCD
Find the first time of contact (ToC) 𝜏
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𝔄 𝜏
Translational CCD
𝔄 𝐯
𝔅
𝑑𝐯
𝑑𝐯 : Minimal directional distance along 𝐯
𝜏 =𝑑𝐯 𝔄, 𝔅
𝐯
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Contents
• Previous work
• Preliminaries
• Out-projection
• In-projection
• Initialization
• Results
• Conclusion
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Contact-Space Localization
𝐪0
𝐪𝑓
𝐨
𝜕 𝔄 ⊕ −𝔅
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Contact-Space Localization
𝐪0
𝐨
𝜕 𝔄 ⊕ −𝔅
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Contact-Space Localization
𝐪0
𝐉𝐪 = 𝐜
𝜕 𝔄 ⊕ −𝔅
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In-Projection
In-Projection
𝐪1
𝐨
𝜕 𝔄 ⊕ −𝔅
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In-Projection
𝐨
𝐪
Minimize 𝐪 2
subject to 𝐉𝐪 ≥ 𝐜
𝜕 𝔄 ⊕ −𝔅
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In-Projection
𝐨
Linear Complementarity Problem
𝐪 −
1
4𝐉𝐉T𝜆 + 𝐜 ≥ 𝟎
𝜆 ≥ 𝟎
−1
4𝐉𝐉T𝜆 + 𝐜
T
𝜆 = 0
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In-Projection
𝐨
𝐪
𝐉𝐉T𝜆 = 4𝐜
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In-Projection
𝐨
𝐪
where 𝐃 + 𝐋 + 𝐔 = 𝐉𝐉T
Projected Gauss-Seidel method
𝜆𝑘+1 = 𝐃 + 𝐋 −1 4𝐜 − 𝐔𝜆𝑘
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Contents
• Previous work
• Preliminaries
• Out-projection
• In-projection
• Initialization
• Results
• Conclusion
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Collision-Free Configuration for
Out-Projection
𝐪𝑓
𝐨
𝜕 𝔄 ⊕ −𝔅
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Initialization
• Find a collision-free configuration as close as possible to the relative origin
• Strategies: - Centroid difference - Maximally clear configuration - Motion coherence - Random configuration - Sampling-based search
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Centroid Difference
𝐪𝑠
𝐪𝔄
𝐨𝔄
𝐨𝔅
𝐪𝔅
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Centroid Difference Initial
collision-free
In- & out-projections
Input in-collision
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Maximally Clear Configuration Equidistant
• Approximate the
maximally clear
configurations
• Voronoi boundary
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Maximally Clear Configuration
1. AABB
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Maximally Clear Configuration
2. Grid positions
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Maximally Clear Configuration
3. Remove the
points with
small distances
xy
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Maximally Clear Configuration
4. Remove
the closer
points along
xy-directions
xy
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Maximally Clear Configuration
5. Remove
the grids on
the boundary
of AABB
z
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Maximally Clear Configuration
6. Remove
the closer
points along
z-direction
z
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Maximally Clear Configuration
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Maximally Clear Configuration Initial
collision-free
Input in-collision
PD
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Sampling-Based Search
Collision
Collision-free
𝐪0𝑓
𝐪1𝑓
𝐨
Collision 𝜕 𝔄 ⊕ −𝔅
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Initial
collision-
free
Input
in-collision PD
Maximally Clear Configurations
and Sampling-Based Search
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Maximally Clear Configurations
and Sampling-Based Search
Initial
collision-free
Input in-collision
PD
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Motion Coherence in Dynamics
Many collision-free
configurations are
found
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Motion Coherence
Initial
collision-free
Input in-collision
PD Out-projections
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Contents
• Previous work
• Preliminaries
• Out-projection
• In-projection
• Initialization
• Results
• Conclusion
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Benchmarking Models
10K 1K 40K 1K 1.33K 174K 105K
0.54K 0.94K 152K 1.34K 2K 3K
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Performance with Random
Configurations
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Performance
with Random
Configurations
top : no. of contacts
mid : no. of iterations
bot : timing
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Performance with Random
Configurations
Model Time
(msec)
No. of
Contacts
No. of
Iterations
Torusknot 5.53 4.84 2.81
Buddha 6.23 5.04 1.92
Bunny2 7.55 8.25 2.16
Dragon 12.76 11.92 2.29
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Comparison to
Lien’s Method
[Lien 2009]
top : PD for
mid : timing for
bot : PD for
2,000 Times Faster
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Average PD Errors
Model Mean error
(%)
Median error
(%)
Bunny1 0.500 0.165
Distorted-
Torus 1.165 0.066
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Predefined Path Scenario
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Dragons with
Predefined
Path
top : no. of contacts
mid : no. of iterations
bot : timing
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Performance on Predefined Paths
Models Time
(msec)
No. of
Contacts
No. of
Iterations
Spoon and Cup 0.96 4.49 1.02
Buddha 3.50 5.07 1.29
Dragon 5.84 10.32 1.45
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Realtime PD Results
Spoon: 1.3K triangles
Cup: 8.4K triangles
Buddha 1: 10K triangles
Buddha 2: 20K triangles
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Applications
– Rigid Body Dynamics
• Multiple
contacts
• To stabilize
dynamics
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Local PD Computation
• Computed for each contact region
• Derived from the global PD
• Corresponding to contact feature pairs that
are resolved as a result of the global PD
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Local PD Computation
Two objects are
interpenetrated.
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Local PD Computation
d
d: global PD
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Local PD Computation
d
n1
n2
: contact normals ni
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Local PD Computation
d
d1
d2
di=(d·ni)ni
i-th local PD:
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Dynamics Simulations
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Performance in the Dynamics
Scenario
Models Global PD
(msec)
Local PD
(msec)
Total
(msec)
Torus 3.13 1.04 4.17
Bunny2 5.43 1.78 7.21
Golf-club 4.87 1.67 6.54
Santa 9.14 2.90 12.04
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Penalty-Based Haptic Rendering
Spoon and Cup Box and Box
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Realtime Rigid Body Dynamics
1 Plane: 12 triangles
7 Bunnies: 40K each
3 Dragons: 174K each
Total: 802K
Performance:
10~50 msec
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Conclusion
• Interactive PD algorithm for complicated
polygon-soup models
• Several schemes for selecting an initial
collision-free configuration
• Method of computing a local PD for
realistic dynamics simulation
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Limitations
• Highly complex models may require a
large number of iterations
• Approximates an upper bound of PD
• Depends on initial collision-free
configurations
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Future Work
• Extension to n-body PD problems
• Extension to generalized PD problem
• Applying to haptic rendering and robot
motion planning
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Acknowledgements
• Graphics group, Ewha
• Stephane Redon, INRIA
• Jyh-Ming Lien, GMU
• Anonymous reviewers
• National research foundation of Korea
• IT R&D Program of MKE/MCST in Korea
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Thank You!
C. Je et al.
PolyDepth: Real-time Penetration Depth Computation.
ACM Transactions on Graphics (ToG), Vol. 31, No. 1, Article 5.
Presented in SIGGRAPH 2012.
http://home.sogang.ac.kr/sites/je