Geometric Registration for Deformable Shapes. 2.1 ICP + Tangent Space optimization for Rigid Motions

Similar documents
Efficient, General Point Cloud Registration with Kernel Feature Maps

Point cloud to point cloud rigid transformations. Minimizing Rigid Registration Errors

CS 468 Lecture 16: Isometry Invariance and Spectral Techniques

1 Derivation of Point-to-Plane Minimization

CS4495/6495 Introduction to Computer Vision. 3C-L3 Calibrating cameras

Body Models I-2. Gerard Pons-Moll and Bernt Schiele Max Planck Institute for Informatics

Solutions HW #2. minimize. Ax = b. Give the dual problem, and make the implicit equality constraints explicit. Solution.

Meshless Surfaces. presented by Niloy J. Mitra. An Nguyen

Structure from Motion. Forsyth&Ponce: Chap. 12 and 13 Szeliski: Chap. 7

2. PROBLEM STATEMENT AND SOLUTION STRATEGIES. L q. Suppose that we have a structure with known geometry (b, h, and L) and material properties (EA).

Generalized Penetration Depth Computation based on Kinematical Geometry

Topic 5: Non-Linear Regression

T f. Geometry. R f. R i. Homogeneous transformation. y x. P f. f 000. Homogeneous transformation matrix. R (A): Orientation P : Position

Projective change between two Special (α, β)- Finsler Metrics

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017

Protein Structure Comparison

ρ some λ THE INVERSE POWER METHOD (or INVERSE ITERATION) , for , or (more usually) to

Chapter 11: Angular Momentum

SELECTED SOLUTIONS, SECTION (Weak duality) Prove that the primal and dual values p and d defined by equations (4.3.2) and (4.3.3) satisfy p d.

p 1 c 2 + p 2 c 2 + p 3 c p m c 2

Error Bars in both X and Y

Gaussian Conditional Random Field Network for Semantic Segmentation - Supplementary Material

UNIVERSIDADE DE COIMBRA

Adaptive Manifold Learning

Spin-rotation coupling of the angularly accelerated rigid body

Video Layer Extraction and Reconstruction

Vector Norms. Chapter 7 Iterative Techniques in Matrix Algebra. Cauchy-Bunyakovsky-Schwarz Inequality for Sums. Distances. Convergence.

FUZZY GOAL PROGRAMMING VS ORDINARY FUZZY PROGRAMMING APPROACH FOR MULTI OBJECTIVE PROGRAMMING PROBLEM

Performance of Different Algorithms on Clustering Molecular Dynamics Trajectories

APPENDIX F A DISPLACEMENT-BASED BEAM ELEMENT WITH SHEAR DEFORMATIONS. Never use a Cubic Function Approximation for a Non-Prismatic Beam

Tensor Analysis. For orthogonal curvilinear coordinates, ˆ ˆ (98) Expanding the derivative, we have, ˆ. h q. . h q h q

MMA and GCMMA two methods for nonlinear optimization

Solutions to exam in SF1811 Optimization, Jan 14, 2015

Army Ants Tunneling for Classical Simulations

Minimizing Algebraic Error in Geometric Estimation Problems

Support Vector Machines

FMA901F: Machine Learning Lecture 5: Support Vector Machines. Cristian Sminchisescu

Chapter 11: Simple Linear Regression and Correlation

Support Vector Machines

Modeling curves. Graphs: y = ax+b, y = sin(x) Implicit ax + by + c = 0, x 2 +y 2 =r 2 Parametric:

Finite Element Modelling of truss/cable structures

VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES

Supporting Information

CS 523: Computer Graphics, Spring Shape Modeling. PCA Applications + SVD. Andrew Nealen, Rutgers, /15/2011 1

Available online at ScienceDirect. Procedia Technology 14 (2014 ) Kailash Chaudhary*, Himanshu Chaudhary

PHYS 705: Classical Mechanics. Newtonian Mechanics

Lecture 21: Numerical methods for pricing American type derivatives

Report on Image warping

Rotation Invariant Shape Contexts based on Feature-space Fourier Transformation

Inexact Newton Methods for Inverse Eigenvalue Problems

Image Analysis. Active contour models (snakes)

Lie Group Formulation of Articulated Rigid Body Dynamics

8/25/17. Data Modeling. Data Modeling. Data Modeling. Patrice Koehl Department of Biological Sciences National University of Singapore

6.3.4 Modified Euler s method of integration

Introductory Optomechanical Engineering. 2) First order optics

Mean Field / Variational Approximations

An Algorithm to Solve the Inverse Kinematics Problem of a Robotic Manipulator Based on Rotation Vectors

(δr i ) 2. V i. r i 2,

Lecture 10 Support Vector Machines. Oct

THE representation of high-dimensional signal sets with. Learning Smooth Pattern Transformation Manifolds. Elif Vural and Pascal Frossard

Mathematical Modeling to Support Gamma Radiation Angular Distribution Measurements

Physics 607 Exam 1. ( ) = 1, Γ( z +1) = zγ( z) x n e x2 dx = 1. e x2

Invariant deformation parameters from GPS permanent networks using stochastic interpolation

Communication with AWGN Interference

La fonction à deux points et à trois points des quadrangulations et cartes. Éric Fusy (CNRS/LIX) Travaux avec Jérémie Bouttier et Emmanuel Guitter

Advanced Mechanical Elements

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

COMPUTATIONALLY EFFICIENT WAVELET AFFINE INVARIANT FUNCTIONS FOR SHAPE RECOGNITION. Erdem Bala, Dept. of Electrical and Computer Engineering,

Lecture Topics VMSC Prof. Dr.-Ing. habil. Hermann Lödding Prof. Dr.-Ing. Wolfgang Hintze. PD Dr.-Ing. habil.

Dynamic Programming. Lecture 13 (5/31/2017)

Numerical Integration of Geometric Flows for Space Curves

MEASUREMENT OF MOMENT OF INERTIA

On a direct solver for linear least squares problems

First Law: A body at rest remains at rest, a body in motion continues to move at constant velocity, unless acted upon by an external force.

Electronic Quantum Monte Carlo Calculations of Energies and Atomic Forces for Diatomic and Polyatomic Molecules

Linear Classification, SVMs and Nearest Neighbors

Some modelling aspects for the Matlab implementation of MMA

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM

Instance-Based Learning (a.k.a. memory-based learning) Part I: Nearest Neighbor Classification

ITERATIVE ESTIMATION PROCEDURE FOR GEOSTATISTICAL REGRESSION AND GEOSTATISTICAL KRIGING

where λ = Z/f. where a 3 4 projection matrix represents a map from 3D to 2D. Part I: Single and Two View Geometry Internal camera parameters

GEOMETRIC SELF-ASSEMBLY OF RIGID SHAPES: A SIMPLE VORONOI APPROACH

Introduction to Simulation - Lecture 5. QR Factorization. Jacob White. Thanks to Deepak Ramaswamy, Michal Rewienski, and Karen Veroy

Study Guide For Exam Two

Spectral Clustering. Shannon Quinn

PHYS 215C: Quantum Mechanics (Spring 2017) Problem Set 3 Solutions

18-660: Numerical Methods for Engineering Design and Optimization

A Quantum Gauss-Bonnet Theorem

Modeling of Dynamic Systems

UIC University of Illinois at Chicago

A property of the elementary symmetric functions

SIO 224. m(r) =(ρ(r),k s (r),µ(r))

Lecture Notes on Linear Regression

Lecture 3. Camera Models 2 & Camera Calibration. Professor Silvio Savarese Computational Vision and Geometry Lab. 13- Jan- 15.

Maximal Margin Classifier

The classical spin-rotation coupling

Note on EM-training of IBM-model 1

Free vibration analysis of a hermetic capsule by pseudospectral method

Singular Value Decomposition: Theory and Applications

STAT 511 FINAL EXAM NAME Spring 2001

Transcription:

Geometrc Regstraton for Deformable Shapes 2.1 ICP + Tangent Space optmzaton for Rgd Motons

Regstraton Problem Gven Two pont cloud data sets P (model) and Q (data) sampled from surfaces Φ P and Φ Q respectvely. Q P data model Assume Φ Q s a part of Φ P. Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Regstraton Problem Gven Two pont cloud data sets P and Q. Goal Regster Q aganst P by mnmzng the squared dstance between the underlyng surfaces usng only rgd transforms. Q P data model Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Notatons P = { p } Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Regstraton wth known Correspondence { p }and{ q }such that p q Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Regstraton wth known Correspondence { p }and{ q }such that p q p Rp + t mn R, t Rp + t q 2 R obtaned usng SVD of covarance matrx. Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Regstraton wth known Correspondence { p }and{ q }such that p q p Rp + t mn R, t Rp + t q 2 R obtaned usng SVD of covarance matrx. t = q R p Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

ICP (Iterated Closest Pont) Iteratve mnmzaton algorthms (ICP) [Besl 92, Chen 92] 1. Buld a set of correspondng ponts 2. Algn correspondng ponts 3. Iterate Propertes Dense correspondence sets Converges f startng postons are close Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

No (explct) Correspondence Φ P Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Squared Dstance Functon (F) x Φ P Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Squared Dstance Functon (F) x d Φ P F( x, Φ P ) = d 2 Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Regstraton Problem Rgd transform α that takes ponts q α ( q ) Our goal s to solve for, mn α q Q F ( α( q ), Φ ) P An optmzaton problem n the squared dstance feld of P, the model PCD. Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Regstraton Problem α = rotaton ( R ) + translaton( t) Our goal s to solve for, mn R, t q Q F ( Rq + t, Φ ) P Optmze for R and t. Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes Regstraton n 2D ),, ( y t x t θ ε Mnmze resdual error = 1 M 2 M t t y x θ depends on F + data PCD (Q).

Approxmate Squared Dstance For a curve Ψ, Ψ d F( x, + 2 2 2 2 Ψ) = x1 + x2 = δ1x1 x2 d-ρ1 Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes [ Pottmann and Hofer 2003 ]

Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes ICP n Our Framework 0 )) ( ( ), ( 2 = = Φ j n F δ p x x P 1 ) ( ), ( 2 = = Φ j F δ p x x P Pont-to-plane ICP (good for small d) Pont-to-pont ICP (good for large d)

Example d2trees 2D 3D Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Convergence Funnel Translaton n x-z plane. Rotaton about y-axs. Converges Does not converge Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Convergence Funnel Plane-to-plane ICP Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes dstance-feld formulaton

Descrptors P = { p } closest pont based on Eucldean dstance Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Descrptors P = { p } closest pont based on Eucldean dstance P = { p, a, b,...} closest pont based on Eucldean dstance between pont + descrptors (attrbutes) Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

(Invarant) Descrptors P = { p } closest pont based on Eucldean dstance P = { p, a, b,...} closest pont based on Eucldean dstance between pont + descrptors (attrbutes) Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Integral Volume Descrptor 0.20 Relaton to mean curvature Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

When Objects are Poorly Algned Use descrptors for global regstratons global algnment refnement wth local (e.g., ICP) Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes