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Multi-perspective Images from Real World Scenes Introduction - - PowerPoint PPT Presentation

Multi- perspective Images from Real World Scenes Multi-perspective Images from Real World Scenes Introduction Artistic Uses Multiprojection Rendering Leonardo K. Sacht The Software leo-ks@visgraf.impa.br Results Future Work Visgraf -


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Multi- perspective Images from Real World Scenes Introduction Artistic Uses Multiprojection Rendering The Software Results Future Work

Multi-perspective Images from Real World Scenes

Leonardo K. Sacht leo-ks@visgraf.impa.br

Visgraf - IMPA

June 30, 2009

Multi-perspective Images from Real World Scenes

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Multi- perspective Images from Real World Scenes Introduction Artistic Uses Multiprojection Rendering The Software Results Future Work

Main Reference

  • M. Agrawala, D. Zorin, T. Munzner:

Artistic Multiprojection Rendering. In Eurographics Rendering Workshop, 2000.

Multi-perspective Images from Real World Scenes

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Multi- perspective Images from Real World Scenes Introduction Artistic Uses Multiprojection Rendering The Software Results Future Work

Artistic Uses of Multiple Projections

Artistic expression;

Multi-perspective Images from Real World Scenes

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Artistic Uses of Multiple Projections

Artistic expression; Representation of the scene.

Multi-perspective Images from Real World Scenes

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Artistic expression: viewing anomalies

Title: The Mystery and Melancholy of a Street. Author: Giorgio de Chirico (1888-1978). Date: 1914. Medium: Oil on canvas. Style: Surrealist.

Multi-perspective Images from Real World Scenes

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Artistic expression: viewing anomalies

Title: Still Life with Fruit Basket. Author: Paul Cezanne (1839-1906). Date: 1880-1890. Medium: Oil on canvas. Style: French Post- Impressionist.

Multi-perspective Images from Real World Scenes

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Reference’s Result

Multi-perspective Images from Real World Scenes

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Our Result

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Representation: best views

Title: Sir John Hawk- wood. Author: Paolo Uc- cello (1397-1475). Date: 1436.

Multi-perspective Images from Real World Scenes

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Representation: best views

Multi-perspective Images from Real World Scenes

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Our Result

Multi-perspective Images from Real World Scenes

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Representation: reducing wide-angle distortions

Title: School of Athens. Author: Raphael Sanzio (1483-1520). Date: 1510-1511. Style: Intalian Re- naissantist.

Multi-perspective Images from Real World Scenes

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Representation: reducing wide-angle distortions

Multi-perspective Images from Real World Scenes

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Our Result

Multi-perspective Images from Real World Scenes

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Visibility Ordering

For a single linear perspective, visibility is defined unambiguosly;

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Visibility Ordering

For a single linear perspective, visibility is defined unambiguosly; For multiperspective, however, the set of points that map to a same pixel on the image surface may be a union of straight lines.

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Master Camera

For all points that would map to a same pixel on the multiperspective final image, we render the one that is closest to the center of projection of a chosen Master

  • Camera. This process is called depth compositing;

Multi-perspective Images from Real World Scenes

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Master Camera

For all points that would map to a same pixel on the multiperspective final image, we render the one that is closest to the center of projection of a chosen Master

  • Camera. This process is called depth compositing;

But problems can occur:

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Object-Level Occlusion Constraints

The occlusions will be considered for each pair of objects

  • n the scene;

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Object-Level Occlusion Constraints

The occlusions will be considered for each pair of objects

  • n the scene;

An occlusion constraint detection in relation to the Master Camera is performed;

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Object-Level Occlusion Constraints

The occlusions will be considered for each pair of objects

  • n the scene;

An occlusion constraint detection in relation to the Master Camera is performed; There are two possibilities for ambiguities: object A

  • ccludes object B in some regions and object B occludes

A in other ones; objects A and B do not overlap on the master camera but overlap on the final result;

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Object-Level Occlusion Constraints

The occlusions will be considered for each pair of objects

  • n the scene;

An occlusion constraint detection in relation to the Master Camera is performed; There are two possibilities for ambiguities: object A

  • ccludes object B in some regions and object B occludes

A in other ones; objects A and B do not overlap on the master camera but overlap on the final result; The user imposes hard constraints and decides abut the

  • cclusion.

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Compositing

If two objects map to the same pixel and there is an

  • cclusion contraint, we use it to solve visibiliy;

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Compositing

If two objects map to the same pixel and there is an

  • cclusion contraint, we use it to solve visibiliy;

If there are no occlusion constraints, use depth compositing;

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Compositing

If two objects map to the same pixel and there is an

  • cclusion contraint, we use it to solve visibiliy;

If there are no occlusion constraints, use depth compositing; If a cycle occurs (A occludes B, B occludes C and C

  • ccludes A), an arbitrary choice of what object will be

seen is taken.

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The Software

I developed a software for the construction of multiperspective images;

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The Software

I developed a software for the construction of multiperspective images; This sofwtare is based on windows and the interface was built with FLTK (Fast Light Toolkit). The programming languages used were C and C++.

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The Software

I developed a software for the construction of multiperspective images; This sofwtare is based on windows and the interface was built with FLTK (Fast Light Toolkit). The programming languages used were C and C++. It is divided in 4 windows and they’re ran by a command line script.

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Input Data

The user inputs a set of equirectangular images from a scene, taken from different points of view. For each image, he gives a corrsepondent image with marked objects and another wich is taken from a close point of view:

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Input Data

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Window 1 (Step 1)

The first window allows the user to choose the look-at vector of each camera;

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Window 1 (Step 1)

The first window allows the user to choose the look-at vector of each camera; the zoom of each photograph;

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Window 1 (Step 1)

The first window allows the user to choose the look-at vector of each camera; the zoom of each photograph; and which camera will be the master camera;

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Results produced by Window 1

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Intermediate Step - Computing stereo

I decided to use an available algorithm to compute stereo (the pair of images is also output of window 1);

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Intermediate Step - Computing stereo

I decided to use an available algorithm to compute stereo (the pair of images is also output of window 1); The information returned by the algorithm would be used for determining depth to the Master Camera;

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Intermediate Step - Computing stereo

In most cases, the results are not satisfactory, what lets most of

  • cclucions to the user to determine;

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Step 2 - Initial Occlusions detection

For each pair of objects A and B, determine zmin(A), zmax(A), zmin(B), zmax(B);

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Step 2 - Initial Occlusions detection

For each pair of objects A and B, determine zmin(A), zmax(A), zmin(B), zmax(B); If zmax(A) < zmin(B), set A occludes B;

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Step 2 - Initial Occlusions detection

For each pair of objects A and B, determine zmin(A), zmax(A), zmin(B), zmax(B); If zmax(A) < zmin(B), set A occludes B; If zmax(B) < zmin(S), set B occludes A;

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Window 2 (Step 3) - User occlusion setting

The user see the Master Camera image and the corresponding objects and chooses the occlusions clicking

  • n buttons;

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Window 2 (Step 3) - User occlusion setting

The user see the Master Camera image and the corresponding objects and chooses the occlusions clicking

  • n buttons;

Cycles are forced to not happen (if A occludes B and the user sets C to occlude A, then the program set that C

  • ccludes A).

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Window 2 (Step 3) - User occlusion setting

The user see the Master Camera image and the corresponding objects and chooses the occlusions clicking

  • n buttons;

Cycles are forced to not happen (if A occludes B and the user sets C to occlude A, then the program set that C

  • ccludes A).

The output of this window is a matrix that represent the

  • cclusion:

  1 1 −1 1 −1 −1   ⇒ A occludes B and C and B occludes C.

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Window 3 (Step 4) - Local Camera setting

The objects given as input are highlited on the images and user chooses a camera for each one;

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Window 3 (Step 4) - Local Camera setting

The objects given as input are highlited on the images and user chooses a camera for each one; On the main reference, the local cameras for each object were given also as input;

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Window 3 (Step 4) - Local Camera setting

The objects given as input are highlited on the images and user chooses a camera for each one; On the main reference, the local cameras for each object were given also as input; The output of this window is a matrix:   1 1 1   ⇒ A from cam 2 B from 2 and C from 3 .

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Step 5 and Window 4 - Compositing

For each (local) object, align its bounding box with its corresponding bounding box on the master camera image;

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Step 5 and Window 4 - Compositing

For each (local) object, align its bounding box with its corresponding bounding box on the master camera image; For each pixel, if there’s no object (with new coordinates)

  • n this pixel, put the color from the Master Camera if

there was no object there or paint it with the object’s indicator color;

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Step 5 and Window 4 - Compositing

For each (local) object, align its bounding box with its corresponding bounding box on the master camera image; For each pixel, if there’s no object (with new coordinates)

  • n this pixel, put the color from the Master Camera if

there was no object there or paint it with the object’s indicator color; If there is one object, put the color of this object;

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Step 5 and Window 4 - Compositing

For each (local) object, align its bounding box with its corresponding bounding box on the master camera image; For each pixel, if there’s no object (with new coordinates)

  • n this pixel, put the color from the Master Camera if

there was no object there or paint it with the object’s indicator color; If there is one object, put the color of this object; If there is more than one object, use the occlusion constraints and put the color of the object that occludes the others;

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Step 5 and Window 4 - Compositing

For each (local) object, align its bounding box with its corresponding bounding box on the master camera image; For each pixel, if there’s no object (with new coordinates)

  • n this pixel, put the color from the Master Camera if

there was no object there or paint it with the object’s indicator color; If there is one object, put the color of this object; If there is more than one object, use the occlusion constraints and put the color of the object that occludes the others; A window displays the final result;

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Post Processing

The final image has holes caused by taking off objects from the Master Camera and putting nothing on its place.

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Post Processing

The final image has holes caused by taking off objects from the Master Camera and putting nothing on its place. Inpainting techniques will be usefull for post processing, but I did it manually.

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Results

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Panoramas

Use of this program for panoramas: the wider the field of view the greater the need of correcting the perspective for the objects

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Intermediate and post processing

Find a more efficient way to compute order between

  • bjects automatically;

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Intermediate and post processing

Find a more efficient way to compute order between

  • bjects automatically;

Inpainting technique for the result;

Multi-perspective Images from Real World Scenes

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Intermediate and post processing

Find a more efficient way to compute order between

  • bjects automatically;

Inpainting technique for the result; Correction of disparities caused by putting different perspectives on the same image;

Multi-perspective Images from Real World Scenes

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View Interpolation

Parallax Photography: Creating 3D Cinematic Effects from Stills

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Thanks!

Check the website: http : //www.impa.br/ ∼ leo-ks/s3d

Multi-perspective Images from Real World Scenes