collision detection of point clouds - universität...
TRANSCRIPT
9/6/06
1
Collision Detection ofPoint Clouds
Gabriel ZachmannClausthal University, [email protected]
EG ’06, Sep 2006, Vienna, Austria
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Renaissance of points as object representation because of 3Dscanners
Goal:
Fast collision detection between 2 given point clouds
No polygonal reconstruction
Motivation
9/6/06
2
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Contents
1. De!nition of surfaces over point clouds (PCs)
2. Hierarchical PC collision detection
3. Intersection detection at leaf level (stochastic)
4. Kinetization of BVHs (PC hierarchies)
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Idea: assume "distance function" f from surface,then surface S is
"Distance" function f :
De!nition of the surface
Surface def.
9/6/06
3
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
De!nition of n by Weighted LeastSquares:
Weighting function (kernel):
Surface def.
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Cause and solution:
Which neighborhood graph?→ k-SIG (sphere-of-in"uence graph)
proximity graph
Surface def.
9/6/06
4
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Result
Surface def.
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Point Cloud Hierarchy
Build BVH according to somelocal criterion (.e.g., volume ofchild BVs)
Construct subsampling andsphere covering at inner nodes
→Ef!cient storage
Hierarchical coll.det.
9/6/06
5
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Observation: surface stays (usually) within set of convex hulls Ci
Randomised technique:
Basic operation: construct random point inside
Draw set of samples from orig. point set for sphere centeres
Compute common radius like Monte-Carlo integration
Sphere covering
Hierarchical coll.det.
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
P
P'
r(P')
SP
Automatic bandwidth detection
Which bandwith h in kernel ?
Too small → noisy surface
Too large → no details
Introduce "Sampling Radius" of a point cloud:Given point cloud P in BV A, P' ⊆ P.Sampling radius r(P') := smallest radius,so that spheres about P' with this radius coversurface SP, de!ned by P, within A.
Hierarchical coll.det.
9/6/06
6
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Compute bandwidth h from r(P ):
η = sampling-independent bandwith,Θε = small threshold (e.g., machine precision)r(P) = sampling radius of (sub-) point cloud
Estimate r(P') :
Hierarchical coll.det.
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Result
time
/ m
illis
ec
distance
Hierarchical coll.det.
9/6/06
7
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Coll.Det. of PCs using Stochastic Sampling
Given two point clouds A and B (or subsets thereof), constructa sampling of
Overall method:
A,B(pi,pj) ∈ A
on different sides of B
Approx.intersection
points
Refinedintersection
point
Stochastic intersection
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
A
B
Algorithm Overview
1. Bracket intersections by pairs of points
2. Find approximate intersection point (AIP) by interpolation search
3. Re!ne AIP by (randomized) sampling
Stochastic intersection
9/6/06
8
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
1. Root Bracketing
Goal: The pairs should evenly sample the surface A
The two points should not be too far apart
Do it without explicit spatial data structure
a) First step: draw number of points(to be one side of the root brackets)
Thought experiment: Assume surface is covered by p surfels.
Draw enough points from PC A so thateach surfel is hit by at least one point
b) Second step: for each point, try to !nd anotherpoint from A lying on the "other" side of B(completing the brackets)
Stochastic intersection
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
a) drawing the points
Avoid spatial data structure (e.g., grid)
Pursue probabilistic approach: occupy all p surfels with highprobability
Assumption: PC A is uniformly sampled
Lemma [WSCG'05] →draw random and independent pointsfrom ,then each surfel is hit by probability
Depending on app: choose p constant or adapt p to samplingdensity
Stochastic intersection
9/6/06
9
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
b) completing the brackets
Use as an indicator
Test only points pj that
belong to the randomly chosen points
are close to each other
→ Only very few points pj need to be tested
Solution: neighborhood graph(e.g., Sphere-of-In"uence graph)
Complexity of !nding brackets:
where d = max. out-degree
Stochastic intersection
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
CalculatingfB(…) …
CalculatingfB(…) …
CalculatingfB(…) …Calculating
fB(…) …CalculatingfB(…) …
AB
2. Interpolation Search
Find along shortest path in the geometricproximity graph, such that is minimal.
Utilize interpolation search → O ( log log m), m = # pts on path
pi
pj
Stochastic intersection
9/6/06
10
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
3. Precise Intersection Points
Intercept Theorem (assuming surface is not curved):
True intersection point is in sphere S(x,r), with
Sample sphere by points on regular grid
Stochastic intersection
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Results
28,000 points
Benchmarking old vs. new method
0
5
10
15
20
25
0 0,5 1 1,5 2 2,5 3
R S T (old)
iS earch (new)
distance (relatve to bbox size)
time
/ m
sec
Stochastic intersection
9/6/06
11
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Theoretical complexity:
Assumptions: f(x) monotone along paths ; and,evenly sampled point cloud.
Experimental complexity:
0
0,5
1
1,5
2
2,5
avg
.ti
me
/m
sec
.
buddha
aphrod ite
8,000 20,000 32,000 44,000 56,000 68,000
number of points per object
Stochastic intersection
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Conclusion of Stochastic Point Cloud CD
Technique:
utilizes a proximity graph for collision detection and surface de!nition
needs no BV hierarchies and no spatial partitioning data structure
any BV hierarchy can be augmented by this technique to increaseperformance
Runtime:
fast (approximate) collision detection
overall runtime: O(log log N) in average case
Quality/resolution of output (intersection points) can be adjusted (→ "surfel" radius)
Stochastic intersection
9/6/06
12
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Conclusions on Stochastic Approach
Cannot be proven error-free
Good for plausible & fast simulations
Interesting alternative to BVHs in speci!c cases
Can often be combined with BVHs
Naturally yield time-critical collision detection
Stochastic intersection
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Kinetic AABB Trees
Not just for collision detection (ray-tracing, occlusion culling,collision detection)
Pre-processed hierarchy becomes invalid when object deforms
→ BVH must be rebuilt or updated after deformations
Cou
rtes
y G
RIS,
Tüb
inge
n
Kinetization
9/6/06
13
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Brute Force Updates
Kinetization
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Our Approach
Observation:
Motion in the physical world is normally continuous
Changes in the combinatorial structure of the BVHs occur only atdiscrete time points
We store only the combinatorial structure of the BVH and use anevent-based approach for updating (kinetization)
Kinetization
9/6/06
14
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Kinetic Updates
Kinetization
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Kinetic AABB Tree
Kinetization of the AABB tree
Pre-processing: Build the tree by any algorithm suitable for staticAABB trees
It is only required that the height of the BVH is logarithmic
With every node, store the indices of those points that determinethe BV
Initialize the event queue
Kinetization
9/6/06
15
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Animation Loop
while animation runs
determine time t of next rendering
e ← min event in event queue
while e.timestamp < t
processEvent(e)
e ← min event in event queue
check for collisions (or cast ray, or …)
render scene
Kinetization
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Advantages
Fewer update operations
Valid BVHs at every point in time
Independent of query sampling frequency
Can handle all kinds of object representations
Polygon soups, point clouds, and NURBS models
Can handle insertions/deletions during run-time
Can handle all kinds of deformations
Only a "ightplan is required for every vertex
These "ightplans may change during animation
Kinetization
9/6/06
16
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Analysis
Theorem 1:Let n = number of vertices.The kinetic AABB tree requires only O(n) space. Each vertexparticipates in only O(log n) events. The kinetic AABB tree canbe updated in only O(log n) time when an event occurs.Finally, it is a valid BVH at every point of time.
Theorem 2:Given n vertices, we assume that the number of intersectionsfor each pair of vertex "ightplans is bounded by a constant.Then, the total number of events is in O(n log n), i.e., it isindependent of the number of queries (within the given"ightplan duration).
Kinetization
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Results
Total number of BV updates for complete animation sequence(note logarithmic scale):
Kinetization
9/6/06
17
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Average update time per frame:
Kinetization
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Total time incl. collision detection time:
Kinetization
9/6/06
18
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
References
Jan Klein and Gabriel Zachmann, “ADB-Trees: Controlling the Error of Time-Critical CollisionDetection”, Proc. VMV ’03
Jan Klein and Gabriel Zachmann, “Time Critical Collision Detection Using an Average CaseApproach”, Proc. VRST ’03
M.C. Lin and J.F. Canny “Ef!cient Collision Detection for Animation”, Eurographics Workshopon Animation and Simulation ’92
Stephane Guy and Gilles Debunne, “Monte Carlo Collision Detection”, INRIA TechnicalReport RR-5136, 2004
Laks Raghupathi et al. “Real-time Collisions and Self-Collisions for Virtual IntestinalSurgery”, Surgical Simulation and Soft Tissue Modeling, pp.38-46, Springer, 2003
Laks Raghupathi et al. “An Intestinal Surgery Simulator: Real-Time Collision Processing andVisualization”, IEEE TVCG, Vol. 10, No. 6,
Stefan Kimmerle, Matthieu Nesme and Francois Faure, “Hierarchy Accelerated StochasticCollision Detection”, Proc. VMV ‘04
Jan Klein and Gabriel Zachmann: "The Expected Running Time of Hierarchical CollisionDetection", SIGGRAPH 2005, Poster
Jan Klein and Gabriel Zachmann: "Interpolation Search for Point Cloud Intersection", Proc.of WSCG 2005
Conclusion
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Jan Klein and Gabriel Zachmann: "Nice and Fast Implicit Surfaces over Noisy Point Clouds",SIGGRAPH 2004, Sketches and Applications
Adams & Dutre: "Interactive boolean operations on surfel bounded solids", 2003
Hubbard: "Approximating Polyhedra with Spheres for Time-Critical Collision Detection",1996
Dingliana, O‘Sullivan: "Graceful Degradation of Collision Handling in Physically BasedAnimation", 2000
Adamson & Alexa: "Approximating and Intersecting Surfaces from Points", 2003
Jan Klein and Gabriel Zachmann: "Point Cloud Collision Detection", Proc. EUROGRAPHICS2004
Otaduy & Lin: "Sensation Preserving Simpli!cation for Haptic Rendering", 2003
Otaduy & Lin:"CLODs: Dual Hierarchies for Multiresolution Collision Detection", Symp. onGeometry Processing (2003)
Weller, Zachmann: "Kinetic Bounding Volume Hierarchies for Deformable Objects", VRCIA(2006)
Conclusion
9/6/06
19
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
Wrap-Up
Rigid bodies Stephane Redon
Deformable objectsMatthias Teschner
ClothPascal Volino
Point CloudsGabriel Zachmann
FluidsRobert Bridson
HairMarie-Paule Cani
Conclusion
Surface def. Hierarchical coll.det. Stochastic intersection Kinetization Conclusion
The End
Conclusion