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Nadia	  Barbara	  Figueroa	  Fernandez	  3D Computer Vision and Applications in Robotics and Multimedia	  Reconstruct your...
•  BACKGROUND	  	  •  3D	  COMPUTER	  VISION	  	  •  APPLICATIONS	  IN	  ROBOTICS	  Research	  Projects	  at	  TU	  Dortmu...
EducaEon	  and	  Research	  PosiEons	  BACKGROUND	  
Fundamentals	  1	  General	  DefiniEon	  2	  My	  DefiniEon	  3	  What	  if	  a	  point	  cloud?	  “Generate	  3D	  represen...
•  Primesense	  3D	  sensor	  •  MicrosoP	  KinectExample	  text	  3	   Light	  Coding	  –	  Structured	  Light	  •  Stere...
APPLICATIONS	  IN	  ROBOTICS	  CalibraEon	  and	  VerificaEon	   Mapping	  and	  NavigaEonObject	  RecogniEon	  and	  Mobil...
Nadia	  Figueroa	  and	  JiVu	  Kurian	  OBJECT	  RECOGNITION	  FOR	  A	  MOBILE	  MANIPULATION	  PLATFORM	  GOAL:	  Detec...
Pre-­‐Requisite	  1:	  CalibraEon	  of	  PMD-­‐CCD	  rig	  OBJECT	  RECOGNITION	  FOR	  A	  MOBILE	  MANIPULATION	  PLATFO...
Pre-­‐Requisite	  2:	  Object	  Database	  	  OBJECT	  RECOGNITION	  FOR	  A	  MOBILE	  MANIPULATION	  PLATFORM	  Object	 ...
Object	  RecogniEon	  Algorithm	  	  OBJECT	  RECOGNITION	  FOR	  A	  MOBILE	  MANIPULATION	  PLATFORM	  
PMD	  Data	  FlaVening	  and	  Variance	  SegmentaEon	  Algorithm	  	  OBJECT	  RECOGNITION	  FOR	  A	  MOBILE	  MANIPULAT...
OBJECT	  RECOGNITION	  FOR	  A	  MOBILE	  MANIPULATION	  PLATFORM	  
DLR’S	  ROLLIN’	  JUSTIN	  Built	  of	  light-­‐weight	  structures	  and	  joints	  with	  mechanical	  compliances	  and...
ROLLIN’	  JUSTIN’S	  LOW	  POSITION	  ACCURACY	  
MASTER	  THESIS	  MOTIVATION	  Problem	  Goal	  Requirements	  Create	  a	  verificaBon	  rouBne	  to	  idenBfy	  the	  max...
Supervisors:	  Florian	  Schmidt	  and	  Haider	  Ali	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	...
3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	  BODY	  KINEMATICS	  3D	  point	  clouds	  of	 ...
Data	  AcquisiEon:	  Dense	  3D	  point	  cloud	  generated	  from	  Stereo	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	...
3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	  BODY	  KINEMATICS	  Point	  Cloud	  Processing...
3D	  RegistraEon	  Methods	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	  BODY	  KINEMATICS...
Model	  GeneraEon	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	  BODY	  KINEMATICS	  
Model	  GeneraEon	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	  BODY	  KINEMATICS	  Extend...
VerificaEon	  RouEne	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	  BODY	  KINEMATICS	  €ek ...
VerificaEon	  RouEne	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	  BODY	  KINEMATICS	  
Method	  EvaluaEon	  (Ground	  Truth)	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	  BODY	 ...
Method	  EvaluaEon	  (Ground	  Truth)	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	  BODY	 ...
 Two	  step	  calibraEon:	  I.	  Center	  of	  RotaEon	  EsEmaEon:	  Non-­‐rigid	  geometrically	  constrained	  	  sphere...
3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	  BODY	  KINEMATICS	  CalibraEon	  of	  Tracking...
Method	  EvaluaEon	  (Ground	  Truth)	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	  BODY	 ...
Method	  EvaluaEon	  (Ground	  Truth)	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	  BODY	 ...
Experimental	  Results	  (TranslaEonal	  Error)	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPE...
Experimental	  Results	  (RotaEonal	  Error)	  3D	  REGISTRATION	  FOR	  VERIFICATION	  OF	  HUMANOID	  JUSTIN’S	  UPPER	 ...
Nadia	  Figueroa	  and	  Haider	  Ali	  (DLR)	  SEGMENTATION	  AND	  POSE	  ESTIMATION	  OF	  PLANAR	  METALLIC	  OBJECTS	...
SEGMENTATION	  AND	  POSE	  ESTIMATION	  OF	  PLANAR	  METALLIC	  OBJECTS	  3D	  point	  clouds	  of	  the	  cloud	  from	...
CONTEXTUAL	  OBJECT	  CATEGORY	  RECOGNITION	  IN	  RGB-­‐D	  SCENES	  PROBLEM:	  Object	  category	  recogniBon	  in	  RG...
CONTEXTUAL	  OBJECT	  CATEGORY	  RECOGNITION	  IN	  RGB-­‐D	  SCENES	  ...
CONTEXTUAL	  OBJECT	  CATEGORY	  RECOGNITION	  IN	  RGB-­‐D	  SCENES	  RGB-­‐D	  Object	  Features	  and	  Classifier	  ...
APPLICATIONS	  IN	  MULTIMEDIA	  World,	  object,	  human	  reconstrucEon	   Rapid	  ReplicaEon	  (3D	  prinEng)Gaming	  
Kinect	  Fusion	  Uses	  Truncated	  Signed	  Distance	  FuncEon	  (TSDF)	  to	  represent	  the	  3D	  data.	  What	  is	...
RGB-­‐D	  KINECT	  FUSION	  FOR	  CONSISTENT	  RECONSTRUCTIONS	  OF	  INDOOR	  SPACES	  Nadia	  Figueroa,	  Haiwei	  Dong	...
RGB-­‐D	  KINECT	  FUSION	  FOR	  CONSISTENT	  RECONSTRUCTIONS	  OF	  INDOOR	  SPACES	  6D	  RGB-­‐D	  ODOMETRY	  
FROM	  SENSE	  TO	  PRINT	  Nadia	  Figueroa,	  Haiwei	  Dong	  and	  Abdulmotaleb	  El	  Saddik	  
FROM	  SENSE	  TO	  PRINT	  SegmentaEon	  based	  on	  Camera	  Pose	  SemanEcs	  Object	  on	  Table	  Top	  SegmentaEon	...
THANK	  YOU!	  
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Nadia2013 research

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Nadia2013 research

  1. 1. Nadia  Barbara  Figueroa  Fernandez  3D Computer Vision and Applications in Robotics and Multimedia  Reconstruct your world  Reconstruct yourself  
  2. 2. •  BACKGROUND    •  3D  COMPUTER  VISION    •  APPLICATIONS  IN  ROBOTICS  Research  Projects  at  TU  Dortmund  Master’s  Thesis  at  DLR    •  APPLICATIONS  IN  MULTIMEDIA  Research  Projects  at  NYU  Abu  Dhabi        DLR’s  rollin’  JusEn  Humanoid      AGENDA  
  3. 3. EducaEon  and  Research  PosiEons  BACKGROUND  
  4. 4. Fundamentals  1  General  DefiniEon  2  My  DefiniEon  3  What  if  a  point  cloud?  “Generate  3D  representaBons  of  the  world  from  the  viewpoint  of  a  sensor,  generally  in  the  form  of  3D  point  clouds.”  “Ability  of  powered  devices  to  acquire  a  real  Bme  picture  of  the  world  in  three  dimensions”.  -­‐  Wikipedia  3D  COMPUTER  VISION  €p ∈ P€p = (x,y,z,r,g,b)“A  point  cloud  is  a  set  of  points                          where                                                                .”  
  5. 5. •  Primesense  3D  sensor  •  MicrosoP  KinectExample  text  3   Light  Coding  –  Structured  Light  •  Stereo  Systems            •  MulB-­‐Camera  Stereo  2   TriangulaEon-­‐based  Systems  1   Time-­‐Of-­‐Flight  Sensors  Sensing  Devices  3D  COMPUTER  VISION  •  LIDAR  (Light  DetecBon  and  Ranging)  •  Radar  •  Sonar  •  TOF  Cameras  •  PMD  (Photonic  Mixing  Device)
  6. 6. APPLICATIONS  IN  ROBOTICS  CalibraEon  and  VerificaEon   Mapping  and  NavigaEonObject  RecogniEon  and  Mobile  ManipulaEon  
  7. 7. Nadia  Figueroa  and  JiVu  Kurian  OBJECT  RECOGNITION  FOR  A  MOBILE  MANIPULATION  PLATFORM  GOAL:  Detect  and  esBmate  the  pose  of  a  wanted  object  in  a  table  top  scenario.      PROPOSED  APPROACH:  Use  CCD  and  PMD  cameras.  PRE-­‐REQUISITES:  1.-­‐  CalibraBon  of  PMD-­‐CCD  Camera  Rig  2.-­‐  Object  Database    
  8. 8. Pre-­‐Requisite  1:  CalibraEon  of  PMD-­‐CCD  rig  OBJECT  RECOGNITION  FOR  A  MOBILE  MANIPULATION  PLATFORM  CalibraEon  and  camera  set-­‐up  (CCD-­‐PMD)  •  Binocular  camera  setup  of  PMD  and  CCD  Camera.  •  Stereo  System  CalibraBon  Method.  –  MathemaBcally  align  the  2  cameras  in  1  viewing  plane.  –  Using  epipolar  geometry,  calculate  essenBal  and  fundamental  matrices.    
  9. 9. Pre-­‐Requisite  2:  Object  Database    OBJECT  RECOGNITION  FOR  A  MOBILE  MANIPULATION  PLATFORM  Object  model  generaEon  • Each  object  is  matched  with  20  training  images.  • The  keypoints  (SURF)  that  are  repeatedly  matched  are  selected  as  the  „best“  keypoints.  • APer  training  each  object,  we  get  100  keypoints  per  object.  Object  1   Object  2   Object  3  
  10. 10. Object  RecogniEon  Algorithm    OBJECT  RECOGNITION  FOR  A  MOBILE  MANIPULATION  PLATFORM  
  11. 11. PMD  Data  FlaVening  and  Variance  SegmentaEon  Algorithm    OBJECT  RECOGNITION  FOR  A  MOBILE  MANIPULATION  PLATFORM  Original  PMD  Segmented  PMD  Fla^ened  PMD  
  12. 12. OBJECT  RECOGNITION  FOR  A  MOBILE  MANIPULATION  PLATFORM  
  13. 13. DLR’S  ROLLIN’  JUSTIN  Built  of  light-­‐weight  structures  and  joints  with  mechanical  compliances  and  flexibiliEes.  (+)  Compliant  behavior  of  the  arm  (-­‐)  Low  posiEong  accuracy  at  the    TCP  (Tool-­‐Center-­‐Point)  end  pose.Designed  to  interact  with  humans  and  unknown  environments.  How  is  this  low  posiEon  accuracy  compensated  in  this  lightweight  design?    Using  the  torque  sensors.  (+)  An  approximaBon  of  a  joint’s  deflecBon  is  obtained  by:                              :measured  torque                              :sBffness  coefficient  of  the  gear  (-­‐)   This   approx.   is   insufficient.   It   cannot   measure   the   remaining  mechanical  flexibiliBes.  €Θi = θi +τiKi€τ€K
  14. 14. ROLLIN’  JUSTIN’S  LOW  POSITION  ACCURACY  
  15. 15. MASTER  THESIS  MOTIVATION  Problem  Goal  Requirements  Create  a  verificaBon  rouBne  to  idenBfy  the  maximum  bounds  of  the  TCP  posiBoning  errors  of  humanoid  JusBn’s  upper  kinemaBc  chains.  The  feasibility  of  moBon  planning  is  highly  dependent  on  the  posiBoning  accuracy.    1.  Avoid  using  any  external  sensory  system.  2.  Avoid  any  human  intervenBon  
  16. 16. Supervisors:  Florian  Schmidt  and  Haider  Ali  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  €TCP = TwhThaTatcpTCP measured byforward kinematics:€TCP = TwhThsTstcpTCP measured bystereo vision system:€Tstcp€Ths€Tatcp€Tha€TCPTwhTCP End-Pose Error:Proposed  Approach:  Use  the  on-­‐board  stereo  vision  system  to  esBmate  the  TCP  end-­‐pose.    
  17. 17. 3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  3D  point  clouds  of  the  hand  from  the  stereo  cameras.  EsBmate  TCP  by  using  registraBon  between  a  point  cloud  of  the  hand  and  a  model.    RegistraEon  method  evaluaEon  1.  Keypoint  extracBon  (SIFT)  &  point-­‐to-­‐point  correspondence.  2.  Local  descriptor  (FPFH/SHOT/CSHOT)  matching  using  Ransac-­‐based  correspondence  search.  Model  GeneraEon  Data  AcquisiEon  Pose  EsEmaEon   Model  generated  from  an  extended  metaview  registraBon  method  from  a  selected  subset  of  views  generated  by  analyzing  the  distribuBon  of  max/min  depth  values.  
  18. 18. Data  AcquisiEon:  Dense  3D  point  cloud  generated  from  Stereo  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  
  19. 19. 3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  Point  Cloud  Processing  Pass-­‐through  filter  (remove  background).  StaBsBcal  Outlier  Removal  (remove  outliers)  Voxel  Grid  Filter  (downsample).  
  20. 20. 3D  RegistraEon  Methods  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  
  21. 21. Model  GeneraEon  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  
  22. 22. Model  GeneraEon  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  Extended  Metaview  RegistraEon  Method  Consists  of  3  steps:  Global  Thresholding  Process:  Reject  the  views  that  lie  in  unstable  areas.  Next  Best  View  Ordering  Algorithm:  Find  an  order  for  incrementally  registering  the  subset  of  point  clouds.  Metaview  RegistraEon:  The  resulBng  subset  of  views  are  registered  and  merged.          
  23. 23. VerificaEon  RouEne  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  €ek = 〈et ,eθ 〉€fk = 3dRMSE = (e1,..,eN )F = ( f1,..., fN )€F* = RANSAC(F)eb = 〈max(et ∈ E*),max(eθ ∈ E*)〉
  24. 24. VerificaEon  RouEne  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  
  25. 25. Method  EvaluaEon  (Ground  Truth)  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  Pose  EsEmaEon  using  IR  ART  tracking  system  (Ground  Truth)  ART  System  Set-­‐up  –  MulB-­‐camera   setup   that  esBmates   the   6DOF   pose   of  the  tracking  targets.  –  Mean   accuracy   of   0.04  pixels.    –  Speed  of  100  fps.  
  26. 26. Method  EvaluaEon  (Ground  Truth)  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  Implicit  loop  closure  with  tracking  system  (Ground  Truth)  –  By  expressing                                              in  ART  coordinate  system  a  double  loop  closure  is  generated.  €TCPfk = TartheTTheThThaTatcp€TCPreg = TartheTTheThThsTstcp€TCPart = (TartheTTheTh)−1TarthaTThaTtcp§  Error  IdenBficaBon  €Tatcp€Tha€TCP€ART€TartheT€TarthaT€ThaTtcp€TheTh€Tstcp€Ths€TCPfk,TCPreg
  27. 27.  Two  step  calibraEon:  I.  Center  of  RotaEon  EsEmaEon:  Non-­‐rigid  geometrically  constrained    sphere-­‐fimng      min  subject  to        :spherical  fit        :measurements        :spherical  constraint  II.  Axis  of  RotaEons  EsEmaEon  Combined  plane/circle  fimng  for  each  axis.    min                :planar              :radial    3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  CalibraEon  of  Tracking  targets  to  JusEn  –  The  esBmaBon  of                      relies  on    the  idenBficaBon  of                and                €TCPart€TheTh€ThaTtcp€f = (δk2+εk2)k=1N∑€εk =||vk − m ||2−r2€uTDTDu€uTCu =1εkδk€u€C€D
  28. 28. 3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  CalibraEon  of  Tracking  targets  to  JusEn  (cont’d)  –  Create  spherical  trajectories  around                              and                    .      –  CoR  is  the  posiBon  of  the  joint                                                                                                                                                                                              deviaBons  throughout  10  calibraBons.  –  AoRs  are  the  rotaBons  –  Moun*ng  frames:                                                                                              deviaBons  throughout  10  calibraBons.                      €R = [AoRx,AoRy,AoRz ]€t = [mx,my,mz ]T€head€TCPThaTtcp= TCP(R,t)−1TarthaTTheTh= head(R,t)−1TartheT€ThaTtcp€TheTh
  29. 29. Method  EvaluaEon  (Ground  Truth)  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  
  30. 30. Method  EvaluaEon  (Ground  Truth)  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  
  31. 31. Experimental  Results  (TranslaEonal  Error)  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  
  32. 32. Experimental  Results  (RotaEonal  Error)  3D  REGISTRATION  FOR  VERIFICATION  OF  HUMANOID  JUSTIN’S  UPPER  BODY  KINEMATICS  
  33. 33. Nadia  Figueroa  and  Haider  Ali  (DLR)  SEGMENTATION  AND  POSE  ESTIMATION  OF  PLANAR  METALLIC  OBJECTS  PROBLEM:  Pose  esBmaBon  of  planar  metallic  objects  in  a  pile.                          PROPOSED  APPROACH:      (i)    SegmentaBon  using  Euclidean  clustering  (ii)  Pose  EsBmaBon  using  RegistraBon  
  34. 34. SEGMENTATION  AND  POSE  ESTIMATION  OF  PLANAR  METALLIC  OBJECTS  3D  point  clouds  of  the  cloud  from  a  range  sensor.  Cluster  RegistraEon  Euclidean  Clustering  We  extract  n-­‐clusters  C  from  pile  P  that  represent  the  planar   objects   by   analyzing   the   angle   deviaBons  between  the  surface  normal  vectors.    Model   PosiEve  aligned  clusters  3D  point  clouds  of  the  cloud  from  a  range  sensor.  Data  AcquisiEon  Euclidean  Clustering  
  35. 35. CONTEXTUAL  OBJECT  CATEGORY  RECOGNITION  IN  RGB-­‐D  SCENES  PROBLEM:  Object  category  recogniBon  in  RGB-­‐D  Data                      PROPOSED  APPROACH:      (i)    Novel  combinaBon  of  depth  and  color  features.  (ii)  Scene  segmentaBon  based  on  table  detecBon  and  euclidean  clustering.  (iii)  ClassificaBon  results  augmented  by  a  context  model  learnt  from  social  media.      
  36. 36. CONTEXTUAL  OBJECT  CATEGORY  RECOGNITION  IN  RGB-­‐D  SCENES  System  Architecture  
  37. 37. CONTEXTUAL  OBJECT  CATEGORY  RECOGNITION  IN  RGB-­‐D  SCENES  RGB-­‐D  Object  Features  and  Classifier  We  use  a  linear  SVM  to  train  6  object  categories.  The  accuracy    of  our  classicaBon  framework  (63.91%)  is  four-­‐Bmes  the  minimum  baseline  generated  by  a  random  guess  (16.67%).    MulE-­‐object  ClassificaEon  
  38. 38. APPLICATIONS  IN  MULTIMEDIA  World,  object,  human  reconstrucEon   Rapid  ReplicaEon  (3D  prinEng)Gaming  
  39. 39. Kinect  Fusion  Uses  Truncated  Signed  Distance  FuncEon  (TSDF)  to  represent  the  3D  data.  What  is  a  TSDF?  A  TSDF  cloud  is  a  point  cloud  which  use  of  how  the  data  is  stored  within  GPU  at  KinFu  runBme.                Each  element  in  the  grid  represents  a  voxel,  and  the  value  inside  it  represents  the  TSDF  value.  The  TSDF  value  is  the  distance  to  the  nearest  isosurface.    
  40. 40. RGB-­‐D  KINECT  FUSION  FOR  CONSISTENT  RECONSTRUCTIONS  OF  INDOOR  SPACES  Nadia  Figueroa,  Haiwei  Dong  and  Abdulmotaleb  El  Saddik  PROBLEM:  GeneraBng  geometric  models  of  environments  for  interior  design,  architectural  and  re-­‐pair  or  remodeling  of  indoor  spaces.                          PROPOSED  APPROACH:    RGB-­‐D  Kinect  Fusion,  which  is  a  combined  approach  towards  consistent  reconstrucBons  of  indoor  Spaces  based  on  Kinect  Fusion  and  6D  RGB-­‐D  Odometry  based  on  efficient  feature  matching.    
  41. 41. RGB-­‐D  KINECT  FUSION  FOR  CONSISTENT  RECONSTRUCTIONS  OF  INDOOR  SPACES  6D  RGB-­‐D  ODOMETRY  
  42. 42. FROM  SENSE  TO  PRINT  Nadia  Figueroa,  Haiwei  Dong  and  Abdulmotaleb  El  Saddik  
  43. 43. FROM  SENSE  TO  PRINT  SegmentaEon  based  on  Camera  Pose  SemanEcs  Object  on  Table  Top  SegmentaEon   Human  Bust  SegmentaEon  
  44. 44. THANK  YOU!  

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