RAY-TRACED COLLISION DETECTION FOR DEFORMABLE

BODIES

Everton Hermann,

Franc¸ois Faure and

Bruno Rafﬁn

INRIA,

Grenoble Universities, France

Keywords:

Collision Detection, Collision Reponse, Physically Based Animation, Deformable Bodies.

Abstract:

This paper presents a new approach to collision detection and modeling between deformable volumetric bod-

ies. It allows deep intersections while alleviating the difﬁculties of distance ﬁeld update. A ray is shot from

each surface vertex in the direction of the inward normal. A collision is detected when the ﬁrst intersection be-

longs to an inward surface triangle of another body. A contact force between the vertex and the matching point

is then created. Experiments show that this approach is fast and more robust than traditional proximity-based

collisions.

1 INTRODUCTION

Collision detection and response is one of the major

computational tasks in physically based animation. It

has thus received considerable attention, and the con-

tributions are too numerous to discuss all of them.

While special shapes such as spheres or cubes allow

the use of optimized methods, the general case of tri-

angular meshes is much more complex. In the case

of rigid objects, the most efﬁcient approaches rely

on signed distance ﬁelds. Each point of one object

(which we call the colliding object) is tested against

the distance ﬁeld of the other (called the collided ob-

ject). If the point is inside the collided object, the

nearest point on the collided surface is found and a

constraint between these points is created. The test

can also be performed the other way round, by switch-

ing the colliding and collided objects. Computing a

distance ﬁeld is a compute-intensive task that is per-

formed once at initialization time for rigid objects,

and deﬁned with respect to a local reference frame.

When the objects are deformable, the distance

ﬁeld would have to be recomputed at each time step,

making it too complex for real-time applications. The

most popular strategy is thus to detect pairs of geo-

metric primitives in close proximity, and to set up

constraints to keep them apart. In this approach, the

contact points are those having a distance to a geomet-

ric primitive of the collided below a given arbitrary

proximity threshold. However, discrete time integra-

tion may allow the surfaces to cross each other, and

when a primitive of the colliding object goes deeper

Figure 1: Problems with proximity-based collision detec-

tion when the bodies intersect each other. (a): proximi-

ties inside and outside the intersection volume may balance

each other, resulting in a null net reaction. (b): undesirable

contacts may be modeled. Green: large areas of the inter-

secting surface are ignored.

in the collided object than the proximity threshold, it

can not be identiﬁed as a contact point. This results

in poor collision responses that sometimes leave the

bodies in intersection, as illustrated in ﬁgure 1.

The problem of surface crossing due to discrete

time integration can somehow be alleviated using so-

phisticated strategies based on collision prediction,

givencurrent positions and velocities. However, these

methods are complex. Their convergence is unclear

and they may require short time steps, while large

time steps are preferable for real-time applications.

293

Hermann E., Faure F. and Rafﬁn B. (2008).

RAY-TRACED COLLISION DETECTION FOR DEFORMABLE BODIES.

In Proceedings of the Third International Conference on Computer Graphics Theory and Applications, pages 293-299

DOI: 10.5220/0001097902930299

 SciTePress

Figure 2: Our ray-traced collision detection. A contact is

modeled at points 1 and 1’ because the ray shot from point 1

on the colliding object hits the inward surface of the collided

object, contrary to points 2 and 3.

Consequently, they have been mainly applied to off-

line cloth simulations.

For volumetric objects, a reasonable amount of

intersection can be visually acceptable, and a robust

contact modeling method would allow us to perform

large time steps. GPU-based methods can detect the

pixels of a colliding surface inside a collided body,

but they do not compute the matching points on the

collided surface. This prevents a robust setting of the

associated contact constraints.

In this paper, we propose to search the match-

ing point along the inward normal of each colliding

point, using a ray-tracing technique illustrated in ﬁg-

ure 2. This approach is simpler than ﬁnding the clos-

est primitive, because we search the matching point

on a one-dimensional ray, rather than over the whole

3D space. We then apply penalty forces between the

matching points. Such forces are necessarily perpen-

dicular to the colliding surface, which ensures a rea-

sonable reaction.

The remainder of this paper is organized as fol-

lows. We brieﬂy summarize related work in section 2.

We present our octree-based raytracing in section 3.

Reaction forces are evaluated in section 4, and results

are discussed in section 5.

2 RELATED WORK

The ﬁeld of collision detection is too large to be re-

viewed in detail here. An excellent survey of colli-

sion detection between deformable objects is given

in (Teschner et al., 2005). Bounding volume hierar-

chies are typically used to accelerate proximity de-

tection (Mezger et al., 2003). Continuous collision

detection can be used to avoid intersections in cloth

simulation (Bridson et al., 2002). Strangely enough,

volumetric elastic bodies have not received much at-

tention. (Guy and Debunne, 2004) apply stochastic

proximity-based surface detection, while (Teschner

et al., 2003) apply spatial hashing to tetrahedra and

apply heuristics to set up contact forces between these

elementary volumes. Distance ﬁelds (Frisken et al.,

2000) are generally used when at least one of the

colliding objects is rigid. Their update requires a

volumetric mesh and is reduced to small deforma-

tions (Fisher and Lin, 2001). Image-space techniques

can detect intersections (Heidelberger et al., 2004),

but they can only model reactions along the camera

axis.

3 DETECTION

If we consider a collision pipeline, our algorithm is

placed on the Narrow Phase process, as it works with

pairs of objects that are potentially colliding. It re-

quires a previous step, the Broad Phase, for identify-

ing pairs of objects whose bounding boxes are collid-

ing. Given such a pair of objects, our algorithm ﬁnds

pairs of colliding points (one point per object). Col-

lision response forces are then applied to these point

pairs.

To identify these pairs of colliding points, we take

a vertex on an object surface and follow the oppo-

site direction of the normal up to ﬁnding a point in

the other object. Our approach allows to solve col-

lisions even if objects are deeply interpenetrated and

triangles are not close enough to be detected based on

proximity. Also, using the normal give us a good di-

rection to be used with collision reaction penalties, so

in the same algorithm we can do the collision detec-

tion and the collision reaction.

Once two objects are interpenetrated and colliding

points are detected, the collision reaction forces are

applied to separate them. Our method does not de-

pend on precomputed data to determine the colliding

points. For this reason, it is well suited to deformable

objects, where the distance ﬁelds are too expensive to

be recomputed at each time step.

The search path from one vertex of an object to

one point on the other object can be represented as a

ray with the origin at the vertices and a direction op-

posite to the normal at the vertices. To speedup the

searching of elements that cross this ray, we stored

all the triangles of each colliding objects in an oc-

tree. Therefore we can easily navigate inside this oc-

tree and efﬁciently ﬁnd the points crossing the ray.

The octree structure allow us to have a satisfying per-

formance independently from the size of the triangles

used, what is not the case for a regular grid.

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294

Figure 3: Quadtree version of the proposed triangle distrib-

ution algorithm. (a): a large triangle placed at the ﬁrst cell

level, (b) 2 triangles stored at the second level and (c) 3 tri-

angles at the third level.

In the next sections we detail how this octree is

built, and how we use this structure to ﬁnd pairs of

colliding points between two objects.

3.1 Octree Construction

For each potentially colliding object we create an oc-

tree containing the object triangles. We obtain a spa-

tial data structure that allows us to easily ﬁnd the tri-

angles that intersect a given region. The efﬁciency of

the octree depends on the triangle spacial distribution.

One way to construct the octree is to split a cell while

it contains more than one triangle. However this ap-

proach does not enable to control the number of cells

a triangle belongs to.

Our algorithm ensures that each triangle is present

in at most eight octree nodes. The goal is to avoid

storing a triangle into a large number of octree cells.

It ensures a good balance between octree precision

and the number of cells to be tested when traversing

the octree. To reach that goal, a triangle is stored at

the deepest level where cell size is greater than the

largest dimension of the triangle’s bounding box (see

Figure 3). Some triangles can be stored into non leaf

nodes, as triangles of different size can be located in

the same area.

3.2 Ray Tracing

We ray-trace starting only from vertices located in the

intersection of the object bounding boxes, which al-

lows us to cull out numerous tests. Our algorithm is

decomposed in two phases (see Algorithm 1): search

for colliding pairs, and result ﬁltering.

The search phase consists in taking the opposite of

the point normal, and following this direction to ﬁnd

a point on the other object. The octree cells are vis-

ited using the octree traversal algorithm presented by

(Revelles et al., 2000). Each cell of the octree con-

tains a list of triangles that intersect this cell. When

a cell is visited, all the triangles it contains are tested

against the ray using the algorithm from (M

Aller and

Trumbore, 1997). If an intersection point is found,

this algorithm gives us its coordinates and the distance

from the ray’s origin.

Algorithm 1 Collision detection Algorithm.

Require: Object1, Object2

Ensure: pairs of colliding points between Object1

and Object2

for each point1 in Object1 do

point2=traceRay(point1, - point1.normal, Ob-

ject2)

if angle between point1.normal and

point2.normal ≤ π/2 then

continue with the next point

end if

point3=traceRay(point1, - point1.normal, Ob-

ject1)

if distance(point1,point2) ≤ dis-

tance(point1,point3) then

add collision pair to the collision response

end if

end for

Having a pair of colliding points, one on each ob-

ject, we test the validity of the resulting contacts as

illustrated in Figure 4. The ﬁrst veriﬁcation concerns

the angle between the normals of both points. An

acute angle means that the ray is entering the sec-

ond object instead of exiting. Eliminating acute an-

gles avoids the misdetection of collision pairs like the

one shown in Figure 4(a), where two differentrays are

traced from O1, but only one is valid, as the angle A

is acute. Applying forces to those points would make

the objects collide even more.

However, only eliminating colliding pairs that tra-

verse the second object from the outside face is not

enough. We must ensure that the point we found is

not outside O1, as only using the normal as ﬁlter cri-

terion may generate ambiguous results. A point that

is part of two triangles can have a normal that satisﬁes

the ﬁrst criterion even if the point is outside the col-

liding object. Figure 4(b) illustrates the second vali-

dation condition of a colliding point. The ray used to

RAY-TRACED COLLISION DETECTION FOR DEFORMABLE BODIES

295

search a colliding point on O2 is reused to intersect

the object O1. If the point found on O1 (point3) is

closer to the origin of the ray (point1) than the point

on O2, this collision pair is eliminated as the second

point is outside object1. The collision pairs that sat-

isfy all the tests are kept to be treated by the collision

response phase.

Figure 4: Colliding points validation. Point1, point2 and

point3 denote the points identiﬁed by the Algorithm 1.

3.3 Self-collision

Self-collision can be detected using an extension of

our method illustrated in Figure 5. A self-collision is

detected when the ray hits twice the inward surface

of the object. This test is more expensive because all

the vertices of a potentially self-intersecting body are

tested.

4 REACTION

Once collisions are detected and modeled, we apply

a penalty force to each pair of matching points. The

Figure 5: Self-collision detection.

Figure 6: Contact force. In (a), a sharp object undergoes

a non-null net tangential force. In (b), angle α is used to

estimate the quality of the contact model and to weight its

force.

force is proportional to the distance, and parallel to

the line joining the matching points. This guarantees

that Newton’s third law on opposite forces is satisﬁed.

We apply the force directly to the colliding point, and

we distribute the opposite force to the vertices of the

associated triangle in the collided object according to

the barycentric coordinates of the intersection point.

We perform an implicit time integration to avoid in-

stabilities due to high contact forces.

The direction of the force is not necessarily paral-

lel to the normal of the collided object, as illustrated

in Figure 6, and some contact pairs are more reliable

than others. As a result, sharp objects may undergo

undesirable net tangential forces.

We therefore multiply the intensity of the force by

the cosine of the angle α in ﬁgure 6(b). This reduces

the inﬂuence of the less reliable contact forces.

To evaluate the contact forces created by our algo-

rithm, we used a cylinder crossed by a plane, and the

expected direction for the resulting force is a vertical

force as shown in 2D in Figure 7. Due to symmetry,

tangential forces should balance each other and the

net tangential force should be null. However, due to

surface discretization, as the rotational position of the

cylinder changes, the resulting force direction may

differ from the normal of the plane.

To measure the variation of the resulting force, we

tested cylinders with a number of sides ranging from

10 to 150. For each cylinder we took 100 different ro-

tational positions, and measured the response forces.

In Figure 8 we show the mean of the variations for a

varying numbers of sides. We observe that the quality

of the resulting force increases with the object level

of detail. For a cylinder having only 10 sides, we

get a worst case deviation of 8%. As we increase

the number of sides, variation levels decrease to about

1%. This variation depends of course on the shape of

the object, and the results improve with smoother ob-

jects. As our algorithm uses penalties to separate the

objects, the usage of the normal direction ﬁts to our

needs, as it give us a good aproximation on the di-

rection one object interpenetrated the other. In some

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296

Figure 7: A cylinder undergoing various tangential forces

due to low geometric resolution.

Figure 8: Ratio of tangential and normal force, against the

number of cylinder faces.

cases we are not able to fully separate to objects in

one step, in those situations using the velocity direc-

tion has no meaning, as it was changed by the penal-

ties forces in the previous step.

A limitation of our methods occurs when all the

inward rays cross the colliding body before the col-

lided one, as illustrated in ﬁgure 9. In this case, no

collision is detected and the contact force is null. Note

that this does not induce instabilities. A proximity-

based method would succeed if the colliding vertices

were not deeper than the proximity threshold, and

fail otherwise. The same happens when one object

is completly inside the other, and our algorithm does

not ﬁnd a way to separate them.

Another limitation occurs when an object that is

in a self-colliding state, also collides another object.

In this case, some collision may be missed as the ﬁrst

colliding point may be an outside face. Also edge-

Figure 9: Our method can fail in case of non-convex inter-

section volume.

edge interpenetration are not always detected as we

project the rays from the object vertexes.

5 RESULTS

We compared the efﬁciency of our method with a hi-

erarchical implementation of a proximity-based ap-

proach, similar to the one proposed by (Bridson

et al., 2002). The algorithms were implemented

using de Simulation Open Framework Architecture

(SOFA) (Allard et al., 2007). The parallel version of

our algorithm was developed using KAAPI (Gautier

et al., 2007).

The ﬁrst test consists in starting the simulation

with interpenetrating objects. We observe how the al-

gorithms manage to push the objects apart. In Fig-

ure 10 we have the starting scene followed by the re-

action produced by each algorithm. The ray-tracing

algorithm manages to separate the objects while the

objects are only deformed and stay interpenetrated

with the proximity-based approach. The ray-tracing

applies all the penalties in a direction that separates

the objects. The proximity-based algorithm tries just

to push apart triangles that are too close. As a result,

some penalties are oriented in a direction opposite to

the one that should be used to separate the objects.

Another advantage of our approach is the larger

simulation step (dt) that can be used. With a large

dt objects can move from a non colliding state to a

deep interpenetration. With a proximity-based ap-

proach, deeply interpenetrated objects lead to trian-

gles too far apart to be detected as colliding. Still us-

ing the same scene (Figure 11), the proximity-based

algorithm gives satisfying results up to a maximum dt

RAY-TRACED COLLISION DETECTION FOR DEFORMABLE BODIES

297

Figure 10: A test scene. Each torus includes 1600 triangles.

Figure 11: Scene used for performance comparison.

of 0.2 seconds, while our algorithm is still effective

up to 0.45 seconds.

In the scene illustrated in Figure 12 we have a sim-

ilar situation where the interpenetration of objects re-

strains the movements when using a proximity-based

approach. The rings start the scene with no collision

between them, but when they get interpenetrated they

are not able to follow the chain movement. How-

ever, with the ray trace algorithm the rings can move

freely, even with interpenetrations. It allow us tu use

larger timesteps without degrading the collision reac-

tion quality.

Figure 12: A deformable chain test.

When running both algorithms one a a Xeon

2.5Ghz machine simulating a scene like the one in

Figure 11, our algorithm reaches 30 fps, while the

proximity-base algorithm reaches only 12 fps. It is

mainly due to a smaller number of colliding points de-

tected by the ray tracing, as close triangles that are not

in a colliding state do not generate colliding points.

With less penalties applied, the solver run faster. The

use of the octree structure to detect matching triangles

also leads to signiﬁcant performance gains.

In terms of scalability our algorithm behaves as

expected, having a linear degradation of performance

as the number of colliding objects increases. The ba-

sic element of our collision detection algorithm is the

triangle. The algorithm performance depends directly

on the number of triangles needing to be evaluated. In

Figure 13, we display the time to solve 200 iterations

with a varying number of objects in the scene. The

objects used are torus initially interpenetrating each