Interactive Generalized Penetration Depth Computation for Rigid and

Articulated Models using Object Norm

Min Tang  and  Young J. Kim

Ewha Womans University, Seoul, Korea

ACM Transactions on Graphics, Volume 33, Issue 1, January 2014
(to be presented in SIGGRAPH 2014)


Paper (PDF 6.7M)

Video (Download 20.7M)

Source Codes (coming soon)


 

[Abstract]  We present a novel, real-time algorithm to accurately approximate the generalized penetration depth (PDg) between two overlapping rigid or articulated models. Given the high complexity of computing PDg, our algorithm approximates PDg based on iterative, constrained optimization on the contact space, defined by the overlapping objects. The main ingredient of our algorithm is a novel and general formulation of distance metric, the object norm, in a configuration space for articulated models, and a compact closed-form solution for it. Then, we perform constrained optimization, by linearizing the contact constraint, and minimizing the object norm under such a constraint. In practice, our algorithm can compute locally optimal PDg for rigid or articulated models consisting of tens of thousands of triangles in tens of milliseconds. We also suggest three applications using PDg computation: retraction-based motion planning, physically-based animation and data-driven grasping.

 


 

Benchmarking Scenarios

image001.jpg

Benchmarking models with triangle counts. Rigid Benchmarking Models with Triangle Count. CAD (2.4K), Spoon (1.34K), Dragon (174K), Bunny (40K), Torusknot (3K), L-shape(20), Cup (1K), Pawn (0.3K), Bumpy-Sphere (2.9K), Hammer (1.7K).

image003.png

Interactive Generalized Penetration Depth (PDg) Computation.  (a, b) As a result of PDg computation for rigid models, the red, colliding objects are rigidly transformed to the green ones, just in contact with obstacles. PDg computation for articulated models and application to motion planning (c, d)and grasping (e). Our algorithm can compute PDg for these challenging benchmarks at interactive rates. The models in (c), (d) and (e) were obtained fromthe GAMMA and KIT research groups, respectively, under permission.


image005.png

 

Performance for Articulated Models.

Model

Time (msec)

No. of Iterations

Puma1

27.9

3.1

Puma2

14.9

2.3

Hand

71.6

1.9

 

 

 

 

 


 

image007.jpgimage009.jpg

 

Performance in
a Predefined Path Scenario for rigid body

Model

Time (msec)

No. of Iterations

Spoon and Cup

7.76

2.35

Bunny and Dragon

22.8

2.14

 

 


 

Related Links

C2A:

http://graphics.ewha.ac.kr/C2A/

 

 

PQP:

http://www.cs.unc.edu/%7Egeom/SSV/index.html

 

PolyDepth:

http://graphics.ewha.ac.kr/polydepth/

 


 

Copyright 2014 Computer Graphics Laboratory

Dept of Computer Science & Engineering

Ewha Womans University, Seoul, Korea

Last update: Jan 6th, 2014

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