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3D full object reconstruction from 3D full object reconstruction from

3D full object reconstruction from - PowerPoint Presentation

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3D full object reconstruction from - PPT Presentation

kinect Yoni Choukroun Elie Semmel Advisor Yonathan Afflalo Agenda Projects Goals Project Development Kinect Registration Reconstruction Performances Demonstration Projects Goals ID: 583497

icp kinect registration reconstruction kinect icp reconstruction registration point shapes points closest performances rigid calibration method depth paul mckay

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Slide1

3D full object reconstruction from kinect

Yoni

Choukroun

Elie

Semmel

Advisor

:

Yonathan

AfflaloSlide2

Agenda

Project’s Goals

Project Development

Kinect

Registration

Reconstruction

Performances

DemonstrationSlide3

Project’s Goals

Understand Kinect working

C++ development

Rigid Iterative closest point implementation

Unknown libraries integration on Windows Platform

Deal with Reconstruction problemsSlide4

Development of the project

Papers read

Kinect depth maps acquisition

Simple rigid ICP implementation

Body reconstructionSlide5

Papers

The

model Project

3D SELF-PORTRAITS

Hao

Li, Etienne

Vouga

, Anton

Gudym

, Jonathan T. Barron,

Linjie

Luo,

Gleb

Gusev

A

Method for Registration of 3-D Shapes

Besl

, Paul J.; N.D. McKay (1992).

Efficient Variants of the ICP Algorithm

Szymon

Rusinkiewicz

, Marc

LevoySlide6

The model Project

3D SELF-PORTRAITS

Hao

Li, Etienne

Vouga

, Anton

Gudym

, Jonathan T. Barron,

Linjie

Luo,

Gleb Gusev

Full body reconstruction in 3-D with multiples scan frames of the Kinect

Pipelined reconstruction

First pipeline station:

scanning and fusion

Rigid ICP of all the scans to get the entire body

Poisson reconstruction for a better quality Slide7
Slide8

KinectSlide9

Kinect

Some facts:

The Kinect moves from [+27 ; -27 ]

Our Kinect motor is controlled by an OPENNI 1.5 code.

The Kinect provides

a

480X640

pixels

resolution depth map .

The depth map returned by the Kinect is of a poor quality.Slide10

Kinect

Intrinsic

Matrix Calibration

application

requires maximum

accuracy rigorous

calibration.

different

users with different Kinect

average

calibration with an average intrinsic matrix calibration.

Pros / Cons:

More precise calibration will improve the performances and the final result but

it asks

from the user a tiresome calibration step

.Slide11

Registration Problem

Given:

Two shapes

P

and

Q

which partially overlap.

Goal

:

Using only

rigid transforms

,

register

Q

against

P

by minimizing the squared distance between them

.Slide12

Simple rigid ICP theory

Mathematical preliminaries

Calculation tools

Algorithm

Performances

Optimization

A Method for Registration of 3-D Shapes

Besl

, Paul J.; N.D. McKay (1992).Slide13

ICP Problematic

Given set of corresponding points it is easy to find the transformation which stitch the two images together.

Here, we do not have any previous prior about the shapes (maybe just good initial conditions enough).

We need then to find the best correlation between points:

ITERATIVE CLOSEST POINT!Slide14

Iteratively find the pairs of closest points

Solve:

Correspondence Space

P

QSlide15

A Method for Registration of 3-D Shapes

Besl

, Paul J.; N.D. McKay (1992).

Sampling (~1:30) the cloud (sparse matrix array of coordinates)

Slide16

A Method for Registration of 3-D Shapes

Besl

, Paul J.; N.D. McKay (1992).

Euclidean Metric used to find the Closest pointSlide17

Transformation

Calculus

:

Find

rotation and translation:

Apply

registration

until

threshold

is

reached

.Slide18

ICP Algorithm

Init the error to ∞

Compute correspondences

Compute alignment

Apply alignment

Update error

If error > threshold

Y =

CP(P,Q)

(

Rot,Trans,d

)

Q`= Rot(Q)+TransSlide19

Efficient Variants of the ICP

Algorithm

Szymon

Rusinkiewicz

, Marc

Levoy

Rigid ICP variant performances

Selection of points

Matching points

Weighting pairs

Rejecting pairsSlide20

Selection of points

Simple variants give good enough results: we implemented both random and uniform samplingSlide21

Matching points

Simple implementation for good enough results (suppose to be small Kinect angles): we chose the Closest Point for pair correspondingSlide22

Reconstruction

Methods

:

Perform ICP between a frame and the result of the all precedent frames bond together

 bad results because of noise accumulation.

Improvement: Perform ICP between pair of frames and apply transitivity to get the registrations to the first frame:

Fn

, the n-

th

frame

Tr

, the transformation between Fn+1 and

Fn

Rn and

Tn

, the corresponding Rotation and Translation.Slide23

Project Algorithm

Kinect

Depth maps acquisition

OpenNi

interface

OpenCV

conversion

Filtering

Registration

Displaying

After

#scans

registrations

Body reconstructionSlide24

ICP Performances

Bad performances for big moves

Not too big rotations required

Number of iterations: 1-3 for small registrations to about 10 for big moves (in general converges to wrong local minimum )

Noisy depth map

Bad

Kinect

resolution affects image’s acquisition: holes in the target shape

.

For complexity purpose, search the closest point only in small radius near the point: finding closest point complexity Slide25

Performances Good fusion result vs time

Tradeoff: Fusion result Vs Execution time

Number of sampling between 8000 and 12000 (static memory allocation)

Time for ICP running :

1

~ 3 min (

depended on number of samples)

Scanning and reconstructing time less than one minute.

Total time between

1.2

mn

to 3mnSlide26

Fusion Results

No pre/post processing used (noise filtering etc.)

Conversion to

MeshLab

and

Matlab

files for displaying (and for post processing like texturing…)

Matlab

Implementation for triangulation.(Not used because of poor quality: points cloud looks better) Slide27

Demonstration

https

://

www.dropbox.com/s/ep2r8ajsodmhw7v/My%20Movie.mp4?dl=0Slide28

References

" 3D SELF-PORTRAITS "

Hao

Li, Etienne

Vouga

, Anton

Gudym

, Jonathan T.

Barron

, Linjie Luo, Gleb Gusev ACM Transactions on Graphics, Proceedings of the 6th ACM SIGGRAPH Conference and Exhibition in Asia 2013, 11/2013 – SIGGRAPH ASIA 2013

"A Method for Registration of 3-D Shapes"

Besl

, Paul J.; N.D. McKay (1992).

IEEE

Trans

. on Pattern

Analysis

and Machine Intelligence (Los

Alamitos

, CA, USA: IEEE Computer Society)

"Efficient Variants of the ICP Algorithm"

Szymon

Rusinkiewicz

, Marc

Levoy

Stanford UniversitySlide29