3D Modeling and Visualization By Morteza Daneshmand iCV Group, - - PowerPoint PPT Presentation

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3D Modeling and Visualization By Morteza Daneshmand iCV Group, - - PowerPoint PPT Presentation

Oct 12, 2023 107 likes •471 views

3D Modeling and Visualization By Morteza Daneshmand iCV Group, Leader of the 3D Modeling and Computer Graphics Division Institute of Technology University of Tartu Literature Review Contents Paper Extracting 2D landmark facial

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SLIDE 1

3D Modeling and Visualization

By Morteza Daneshmand iCV Group, Leader of the 3D Modeling and Computer Graphics Division Institute of Technology University of Tartu

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SLIDE 2

Paper

  • Automatic

facial feature extraction and 3D face modeling using two orthogonal views with application to 3D face recognition

Literature Review

Contents

  • Extracting 2D

landmark facial features

  • Computing the

corresponding 3D coordinates

  • Estimation of the

coordinates of the features hidden in the profile to align and locally deform the facial vertices

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SLIDE 3

Paper

  • Automatic

facial feature extraction and 3D face modeling using two orthogonal views with application to 3D face recognition

Literature Review

Contents

  • Extracting 2D

landmark facial features

  • Computing the

corresponding 3D coordinates

  • Estimation of the

coordinates of the features hidden in the profile to align and locally deform the facial vertices

slide-4
SLIDE 4

Paper

  • Automatic

facial feature extraction and 3D face modeling using two orthogonal views with application to 3D face recognition

Literature Review

Contents

  • Extracting 2D

landmark facial features

  • Computing the

corresponding 3D coordinates

  • Estimation of the

coordinates of the features hidden in the profile to align and locally deform the facial vertices

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SLIDE 5

Paper

  • SUN3D: A

Database of Big Spaces Reconstructe d using SfM and Object Labels

Literature Review

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SLIDE 6

Literature Review

Overview

  • Large-scale RGB-

D video databases

  • Frames fully

describing big scenes

  • Basic idea is

useful for

  • bject

reconstruction

  • Goal is to

reconstruct a 3D point cloud

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SLIDE 7

Literature Review

Overview

  • Large-scale RGB-

D video databases

  • Frames fully

describing big scenes

  • Basic idea is

useful for

  • bject

reconstruction

  • Goal is to

reconstruct a 3D point cloud

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SLIDE 8

Literature Review

Overview

  • Large-scale RGB-

D video databases

  • Frames fully

describing big scenes

  • Basic idea is

useful for

  • bject

reconstruction

  • Goal is to

reconstruct a 3D point cloud

slide-9
SLIDE 9

Literature Review

Overview

  • Large-scale RGB-

D video databases

  • Frames fully

describing big scenes

  • Basic idea is

useful for

  • bject

reconstruction

  • Goal is to

reconstruct a 3D point cloud

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SLIDE 10

Literature Review

Reconstructio n

  • Multiple Kinect

frames of the scene are taken

  • The same idea

could be used through taking frames of the same object from different

  • rientations
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SLIDE 11

Literature Review

Reconstructio n

  • Multiple Kinect

frames of the scene are taken

  • The same idea

could be used through taking frames of the same object from different

  • rientations
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SLIDE 12

Literature Review

Annotation

  • Camera poses
  • Object labels
  • Online object

annotation

  • Structure from

Motion (SfM)

  • Propagating the

labels through the frames

  • This is not our

concern right now!

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SLIDE 13

Literature Review

Annotation

  • Camera poses
  • Object labels
  • Online object

annotation

  • Structure from

Motion (SfM)

  • Propagating the

labels through the frames

  • This is not our

concern right now!

slide-14
SLIDE 14

Literature Review

Annotation

  • Camera poses
  • Object labels
  • Online object

annotation

  • Structure from

Motion (SfM)

  • Propagating the

labels through the frames

  • This is not our

concern right now!

slide-15
SLIDE 15

Literature Review

Annotation

  • Camera poses
  • Object labels
  • Online object

annotation

  • Structure from

Motion (SfM)

  • Propagating the

labels through the frames

  • This is not our

concern right now!

slide-16
SLIDE 16

Literature Review

Annotation

  • Camera poses
  • Object labels
  • Online object

annotation

  • Structure from

Motion (SfM)

  • Propagating the

labels through the frames

  • This is not our

concern right now!

slide-17
SLIDE 17

Literature Review

Annotation

  • Camera poses
  • Object labels
  • Online object

annotation

  • Structure from

Motion (SfM)

  • Propagating the

labels through the frames

  • This is not our

concern right now!

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SLIDE 18

Literature Review

Post- processing

  • Using object

labels for error reduction

  • Generalized

bundle adjustment

  • Object-to-
  • bject

correspondence

  • Bounding boxes
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SLIDE 19

Literature Review

Post- processing

  • Using object

labels for error reduction

  • Generalized

bundle adjustment

  • Object-to-
  • bject

correspondence

  • Bounding boxes
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SLIDE 20

Literature Review

Post- processing

  • Using object

labels for error reduction

  • Generalized

bundle adjustment

  • Object-to-
  • bject

correspondence

  • Bounding boxes
slide-21
SLIDE 21

Literature Review

Post- processing

  • Using object

labels for error reduction

  • Generalized

bundle adjustment

  • Object-to-
  • bject

correspondence

  • Bounding boxes
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SLIDE 22

Methodology and Results

The figure has been taken from http://www.cemyuksel.com/.

Garment Model Reconstruction

  • Generating a 3D model of a garment

from multiple shots

  • Preliminary works using ICP
  • 3D reconstruction of a cup
  • 3D reconstruction of a statue
  • 3D mesh based on the point cloud
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SLIDE 23

Methodology and Results

Garment Model Reconstruction

  • Generating a 3D model of a garment

from multiple shots

  • Preliminary works using ICP
  • 3D reconstruction of a cup
  • 3D reconstruction of a statue
  • 3D mesh based on the point cloud
The figure is created by Lembit.
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SLIDE 24

Methodology and Results

Garment Model Reconstruction

  • Generating a 3D model of a garment

from multiple shots

  • Preliminary works using ICP
  • 3D reconstruction of a cup
  • 3D reconstruction of a statue
  • 3D mesh based on the point cloud
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SLIDE 25

Methodology and Results

Garment Model Reconstruction

  • Generating a 3D model of a garment

from multiple shots

  • Preliminary works using ICP
  • 3D reconstruction of a cup
  • 3D reconstruction of a statue
  • 3D mesh based on the point cloud
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SLIDE 26

Garment Model Reconstruction

  • Generating a 3D model of a garment

from multiple shots

  • Preliminary works using ICP
  • 3D reconstruction of a cup
  • 3D reconstruction of a statue
  • 3D mesh based on the point cloud

Methodology and Results

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SLIDE 27

Existing Challenges

  • Too few points
  • Minimum and

maximum distances from the camera

  • Noisy data and

wrong scales

  • Missing data
  • Loop closure
  • Local minimums
  • Fallacious

transformation

  • Adjusting

thresholds

Methodology and Results

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SLIDE 28

Existing Challenges

  • Too few points
  • Minimum and

maximum distances from the camera

  • Noisy data and

wrong scales

  • Missing data
  • Loop closure
  • Local minimums
  • Fallacious

transformation

  • Adjusting

thresholds

Methodology and Results

slide-29
SLIDE 29

Existing Challenges

  • Too few points
  • Minimum and

maximum distances from the camera

  • Noisy data and

wrong scales

  • Missing data
  • Loop closure
  • Local minimums
  • Fallacious

transformation

  • Adjusting

thresholds

Methodology and Results

slide-30
SLIDE 30

Existing Challenges

  • Too few points
  • Minimum and

maximum distances from the camera

  • Noisy data and

wrong scales

  • Missing data
  • Loop closure
  • Local minimums
  • Fallacious

transformation

  • Adjusting

thresholds

Methodology and Results

slide-31
SLIDE 31

Existing Challenges

  • Too few points
  • Minimum and

maximum distances from the camera

  • Noisy data and

wrong scales

  • Missing data
  • Loop closure
  • Local minimums
  • Fallacious

transformation

  • Adjusting

thresholds

Methodology and Results

slide-32
SLIDE 32

Existing Challenges

  • Too few points
  • Minimum and

maximum distances from the camera

  • Noisy data and

wrong scales

  • Missing data
  • Loop closure
  • Local minimums
  • Fallacious

transformation

  • Adjusting

thresholds

Methodology and Results

slide-33
SLIDE 33

Existing Challenges

  • Too few points
  • Minimum and

maximum distances from the camera

  • Noisy data and

wrong scales

  • Missing data
  • Loop closure
  • Local minimums
  • Fallacious

transformation

  • Adjusting

thresholds

Methodology and Results

slide-34
SLIDE 34

Existing Challenges

  • Too few points
  • Minimum and

maximum distances from the camera

  • Noisy data and

wrong scales

  • Missing data
  • Loop closure
  • Local minimums
  • Fallacious

transformation

  • Adjusting

thresholds

Methodology and Results

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SLIDE 35

Thank You