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Shape Modeling with Point-Sampled Geometry Mark Pauly, Richard Keiser, Leif P. Kobbelt, Markus Gross (ETH Zurich and RWTH Aachen)

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Presentation on theme: "Shape Modeling with Point-Sampled Geometry Mark Pauly, Richard Keiser, Leif P. Kobbelt, Markus Gross (ETH Zurich and RWTH Aachen)"— Presentation transcript:

1 Shape Modeling with Point-Sampled Geometry Mark Pauly, Richard Keiser, Leif P. Kobbelt, Markus Gross (ETH Zurich and RWTH Aachen)

2 Abstract Modeling framework with point-sampled geometry Hybrid representation –Point clouds –Implicit surface with MLS General operations on models –Booleans operations –Deformations

3 Surface representations Implicit surfaces: level sets, RBF +Topology defined –Non-intuitive to control –Rendering is slow Parametric surfaces: splines, subdivision surfaces, triangle meshes +Simplicity –Extreme deformation –Connectivity information Introduction

4 Hybrid surface Unstructured points Implicit surface –Signed distance –Normal defined Boolean operations –Preserve shard edges

5 Freeform modeling Global deformation –Preserve the sampling density Tools: –Push, pull, twist and etc. Topology control

6 A component in a modeling system Takes scanner inputs Rendering techniques –QSplat [Zwicker 01] –[Rusinkiwicz 00] –[Botsch 02] Fast with free LOD

7 Related work Points primitive –Szeliski and Tonnesen 92 Oriented particles Physical simulation –Witkin and Heckbert 94 Blend operation Limited deformations Freeform modeling –Chang and Rackwood 94 Wires system Dynamically sampling –Welch and Witkin 94 Trangle mesh Vertex split and edge collapse –Kobbelt 00 Multiresolution Dynamic mesh connectivity

8 Hybrid surface model Input points with attributes Moving least squares (MLS)

9 Moving least squares 1.Local reference domain 2.Minimize to find H 3.Minimize to find g 4. proj(r)=q+g(0,0)n

10 MLS kernel function Small h cause Gaussian decay faster Small h means approximation more local MLS act as an low pass filter

11 Adaptive MLS If the sampling of points is adaptive k ranges [6:20]

12 Boolean operations CSG in a binary tree. Computing the boolean operation 1.Find 2.New set of intersection curves 3.Crisp curves

13 Inside outside classification

14 Optimization 90% can be approximated Else use MLS projection to find y Local coherence –If x’ is within the sphere center at x, radius is

15 Classification table Points in Q1 is picked from P1 only if –p is outside of surface defined by P2 –Similarly for Q2

16 Intersection curves A set of points for intersection curves 1.Find points near the intersection using the distance function 2.Closest point pairs (q1 in Q1, q2 in Q2) 3.r: in the intersection of the tangent planes 4.Project r to new q1 and q2 5.Repeat step 3 to 5 for 3 iterations

17 Intersection curves diagram

18 Adaptive refinement Match sample density Use a simple subdivision New p for the Newton iteration

19 Rendering sharp creases Surface splatting [Zwicker 01] Surfel: elliptical splat –Intersection curve points –Clip against two normals

20 Sharp creases results

21 Freeform deformation Bending, twisting, stretching, compressing Interactive speed Intuitive control Global operation

22 Deformable regions Region zero: not selected Region one: handle d0=0, d1=x, t=b(0/(0+x))=0 d0=dist(p and x0)=x, d1=0, t=b(x/(x+0))=1 d0=dist(p and x0)=x, d1=min(p and x1)=y, t=b(x/(x+y))

23 Applying translation and rotation Demo

24 Blending function

25 Rotation and twisting results

26 Topology control Self intersections Collision detection –Nearest point for each point in Xd –Within the sphere: collision free

27 Collision handling 1.Undo (disallow self intersections) 2.Union (sharp edges) 3.Blending –Inter particle potential [Szeliski 92] –Define a local neighborhood

28 Topology results

29 Dynamic sampling Cause: Distortion and insufficient sampling Goal: insert and remove points Feature: Interpolate attributes –Color –Texture value

30 Measuring surface stretch u and v are on the tangent plane –Orthogonal –Unit length Local anisotropy –Ratio of the two eigenvalues Split a point into two

31 Dynamic sampling results

32 Filter operations Problem: what to do with the new points Relaxation [Turk 92] Confined radius of influence Projection back to the tangent plane

33 Interpolation (with Zombies) Scalar values Drifting happens when split happens Zombies are fixed –Only used for the attributes –Delete after each edit operation

34 Interpolation results

35 Downsampling Special case: –Shrink then grow –Lost values cause blur Preserve old value Garbage collection

36 Results and discussion Pointshop3D Multiresolution surface modeling –Detail vectors [Zorin 97] –Spectral decomposition [Guskov 99]

37

38 69,706295,220

39 69,268222,955

40 25,020non-uniform

41 100,269

42 Implementation Closest points query: –Kd-tree –Building 300,000 points in 0.23 s –Querying 10 points in 4.5e-6 and 6.2e-6 s

43 Dynamic updates Boolean classification –Two static structure Free-form deformation –No update until an edit session is done Collision detection –Only for deformable region with the zero region –Cannot handle collision of deformable regions Dynamically sampling –[Linsen 02] –Dynamic update at insertions and relaxation

44 Performance No connectivity to update Handling one million points Pointshop3D software rendering –50% of the total computation is rendering Software renderer [Botsch 02] Hardware renders [Rusinkiewicz 00]

45 Conclusion and future work A solid framework for modeling Provides basic operations Extends to traditional surface modeling techniques Efficiency and performance Future work: –Hairy or furry models, plants –Dynamic simulation


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