Presentation is loading. Please wait.

Presentation is loading. Please wait.

Siggraph’2000, July 27, 2000 Jin-Xiang Chai Xin Tong Shing-Chow Chan Heung-Yeung Shum Microsoft Research, China Plenoptic Sampling SIGGRAPH’2000.

Published by Modified over 9 years ago

Similar presentations

Presentation on theme: "Siggraph’2000, July 27, 2000 Jin-Xiang Chai Xin Tong Shing-Chow Chan Heung-Yeung Shum Microsoft Research, China Plenoptic Sampling SIGGRAPH’2000."— Presentation transcript:

1 Siggraph’2000, July 27, 2000 Jin-Xiang Chai Xin Tong Shing-Chow Chan Heung-Yeung Shum Microsoft Research, China Plenoptic Sampling SIGGRAPH’2000

2 Siggraph’2000, July 27, 2000 3D Graphics vs. IBR Traditional 3D graphics approaches Interactive Hardware support Image-based approaches Realistic Fast Easier to construct

3 Siggraph’2000, July 27, 2000 Rendering with No Geometry Plenoptic functions 7D: Complete function (Adelson & Bergen) 5D: Ignore time and wavelength (McMillan & Bishop) 4D: Inside bounding box (Lightfield/Lumigraph) 3D: Moving in a planar region (Concentric Mosaics) 2D: At the same viewpoint (Panorama)

4 Siggraph’2000, July 27, 2000 Light Field/Lumigraph Rendering Image Plane Camera Plane Light Field Capture Rendering

5 Siggraph’2000, July 27, 2000 The Minimum Sampling Problem How many images are needed for anti-aliased light field rendering?

6 Siggraph’2000, July 27, 2000 The Minimum Sampling Problem Complexity of the scene Depth Texture Number of input images Output resolution

7 Siggraph’2000, July 27, 2000 The Minimum Sampling Problem Previous work on light field sampling Holographic Stereogram (Halle ’ 94) Prefiltering light field (Levoy and Hanrahan ’ 96 )

8 Siggraph’2000, July 27, 2000 Our Approach Light field rendering = Signal reconstruction Minimum sampling from Nyquist limits High dimensionality Nonlinear A spectral analysis of 2D light field signal

9 Siggraph’2000, July 27, 2000 A Spectral Analysis Spectral analysis of 2D continuous light field

10 Siggraph’2000, July 27, 2000 A Constant Plane Z vt t v Z1Z1 Z1Z1

11 Siggraph’2000, July 27, 2000 Two Constant Planes Z vt Z1Z1 Z2Z2 t v Z1Z1 Z2Z2

12 Siggraph’2000, July 27, 2000 Between Two Planes Z vt t v Z1Z1 Z1Z1 Z2Z2 Z2Z2

13 Siggraph’2000, July 27, 2000 Between Two Planes Z vt t v Z1Z1 Z1Z1 Z2Z2 Z2Z2

14 Siggraph’2000, July 27, 2000 A Spectral Analysis Spectral analysis of sampled light field

15 Siggraph’2000, July 27, 2000 Sampled: Camera Spacing

16 Siggraph’2000, July 27, 2000 Sampled: Image Resolution

17 Siggraph’2000, July 27, 2000 Light Field Reconstruction

18 Siggraph’2000, July 27, 2000 Light Field Reconstruction

19 Siggraph’2000, July 27, 2000 Light Field Reconstruction

20 Siggraph’2000, July 27, 2000 Rendering With Optimal Plane

21 Siggraph’2000, July 27, 2000 Light Field Reconstruction

22 Siggraph’2000, July 27, 2000 Minimum Sampling Curve Joint Image and Geometry Space Minimum Sampling Curve Number of Depth Layers 1 2 36 12 Accurate Depth Number of Images 2x2 8x8 4x4 16x16 32x32

23 Siggraph’2000, July 27, 2000 Minimum Sampling Curve Number of Depth Layers 1 2 36 12 Accurate Depth Number of Images 2x2 8x8 4x4 16x16 32x32

24 Siggraph’2000, July 27, 2000 Minimum Sampling Curve Number of Depth Layers 1 2 36 12 Accurate Depth Number of Images 2x2 8x8 4x4 16x16 32x32

25 Siggraph’2000, July 27, 2000 Minimum Sampling Curve Number of Depth Layers 1 2 36 12 Accurate Depth Number of Images 2x2 8x8 4x4 16x16 32x32

26 Siggraph’2000, July 27, 2000 Minimum Sampling Curve Number of Depth Layers 1 2 36 12 Accurate Depth Number of Images 2x2 8x8 4x4 16x16 32x32

27 Siggraph’2000, July 27, 2000 Minimum Sampling Curve Number of Depth Layers 1 2 36 12 Accurate Depth Number of Images 2x2 8x8 4x4 16x16 32x32

28 Siggraph’2000, July 27, 2000 Minimum Sampling Curve Redundant for Rendering Number of Depth Layers 1 2 36 12 Accurate Depth Number of Images 2x2 8x8 4x4 16x16 32x32

29 Siggraph’2000, July 27, 2000 More Geometry: 3 Layers Number of Depth Layers 1 2 36 12 Accurate Depth Number of Images 2x2 8x8 4x4 16x16 32x32

30 Siggraph’2000, July 27, 2000 3 Layers Number of Depth Layers 1 2 36 12 Accurate Depth Number of Images 2x2 8x8 4x4 16x16 32x32

31 Siggraph’2000, July 27, 2000 3 Layers Number of Depth Layers 1 2 36 12 Accurate Depth Number of Images 2x2 8x8 4x4 16x16 32x32

32 Siggraph’2000, July 27, 2000 3 Layers Number of Depth Layers 1 2 36 12 Accurate Depth Number of Images 2x2 8x8 4x4 16x16 32x32

33 Siggraph’2000, July 27, 2000 3 Layers Number of Depth Layers 1 2 36 12 Accurate Depth Number of Images 2x2 8x8 4x4 16x16 32x32

34 Siggraph’2000, July 27, 2000 A Geometrical Intuition Z min Z opt Camera iCamera i+1

35 Siggraph’2000, July 27, 2000 A Geometrical Intuition Z min Z opt Camera iCamera i+1 Disparity Error < 1 Pixel Rendering Camera

36 Siggraph’2000, July 27, 2000 A Geometrical Intuition Z min Z opt Camera iCamera i+1 Rendering Camera

37 Siggraph’2000, July 27, 2000 A Geometrical Intuition Z min Z opt Camera i Camera i+1 Rendering Camera

38 Siggraph’2000, July 27, 2000 A Geometrical Intuition Z min Z opt Camera i Camera i+1 Rendering Camera

39 Siggraph’2000, July 27, 2000 Optimal Distance Rendering Camera A Geometrical Intuition Camera iCamera i+1 Zmax Zmin Depth Layer 1 Depth Layer 2 Optimal Distance

40 Siggraph’2000, July 27, 2000 Plenoptic Sampling 48X48 Images No Depth 16X16 Images 3Bits Depth

41 Siggraph’2000, July 27, 2000 Plenoptic Sampling 48X48 Images without Depth24X24 Images with 7Bits Depth Antialiasing Rendering Needs 2930X2930 images = 5,000GB Antialiasing Rendering Needs 24X24 RGBD images = 0.5GB

42 Siggraph’2000, July 27, 2000 Plenoptic Sampling Number of Depth Layers 1 Number of Images Light Field Rendering Redundancy Minimum Sampling Rate

43 Siggraph’2000, July 27, 2000 Plenoptic Sampling Number of Depth Layers Number of Images Redundant for Rendering Maximum Resources Joint Image and Geometry Space 1

44 Siggraph’2000, July 27, 2000 Plenoptic Sampling Number of Depth Layers Number of Images Higher Output Resolution

45 Siggraph’2000, July 27, 2000 Unifying 3D Graphics and IBR? The same rendering quality can be achieved with a combination of images and geometry

46 Siggraph’2000, July 27, 2000 IBR: Sampling Sampling: design principles for IBR Application 1: Geometry-assisted image dataset reduction Application 2: Image-based geometry simplification Application 3: Rendering-driven vision reconstruction

47 Siggraph’2000, July 27, 2000 Future Work Occlusion: More accurate model of spectral support Surface model: Remove the limitation of constant depth model BRDF: Beyond Lambertian model

48 Siggraph’2000, July 27, 2000 End of the Talk Thank you

Download ppt "Siggraph’2000, July 27, 2000 Jin-Xiang Chai Xin Tong Shing-Chow Chan Heung-Yeung Shum Microsoft Research, China Plenoptic Sampling SIGGRAPH’2000."

Similar presentations

Computer Graphics Inf4/MSc Computer Graphics Lecture Notes #16 Image-Based Modelling, Rendering and Lighting.

Computer Graphics Inf4/MSc Computer Graphics Lecture Notes #16 Image-Based Modelling, Rendering and Lighting.
Introduction to Image-Based Rendering Jian Huang, CS 594, Spring 2002 A part of this set of slides reference slides used at Standford by Prof. Pat Hanrahan.

Introduction to Image-Based Rendering Jian Huang, CS 594, Spring 2002 A part of this set of slides reference slides used at Standford by Prof. Pat Hanrahan.
Measuring BRDFs. Why bother modeling BRDFs? Why not directly measure BRDFs? True knowledge of surface properties Accurate models for graphics.

Measuring BRDFs. Why bother modeling BRDFs? Why not directly measure BRDFs? True knowledge of surface properties Accurate models for graphics.
Light Fields PROPERTIES AND APPLICATIONS. Outline  What are light fields  Acquisition of light fields  from a 3D scene  from a real world scene 

Light Fields PROPERTIES AND APPLICATIONS. Outline  What are light fields  Acquisition of light fields  from a 3D scene  from a real world scene 
Light Field Rendering Shijin Kong Lijie Heng.

Light Field Rendering Shijin Kong Lijie Heng.
Lightfields, Lumigraphs, and Image-based Rendering.

Lightfields, Lumigraphs, and Image-based Rendering.
Dana Cobzas-PhD thesis Image-Based Models with Applications in Robot Navigation Dana Cobzas Supervisor: Hong Zhang.

Dana Cobzas-PhD thesis Image-Based Models with Applications in Robot Navigation Dana Cobzas Supervisor: Hong Zhang.
Unstructured Lumigraph Rendering

Unstructured Lumigraph Rendering
Advanced Computer Graphics CSE 190 [Spring 2015], Lecture 14 Ravi Ramamoorthi

Advanced Computer Graphics CSE 190 [Spring 2015], Lecture 14 Ravi Ramamoorthi
Copyright  Philipp Slusallek Cs448.98.fall IBR: Model-based Methods Philipp Slusallek.

Copyright  Philipp Slusallek Cs fall IBR: Model-based Methods Philipp Slusallek.
Light Readings Forsyth, Chapters 4, 6 (through 6.2) by Ted Adelson.

Light Readings Forsyth, Chapters 4, 6 (through 6.2) by Ted Adelson.
Computational Photography: Image-based Modeling Jinxiang Chai.

Computational Photography: Image-based Modeling Jinxiang Chai.
Advanced Computer Graphics (Fall 2010) CS 283, Lecture 16: Image-Based Rendering and Light Fields Ravi Ramamoorthi

Advanced Computer Graphics (Fall 2010) CS 283, Lecture 16: Image-Based Rendering and Light Fields Ravi Ramamoorthi
CSCE 641 Computer Graphics: Image-based Modeling Jinxiang Chai.

CSCE 641 Computer Graphics: Image-based Modeling Jinxiang Chai.
Advanced Computer Graphics (Spring 2005) COMS 4162, Lecture 21: Image-Based Rendering Ravi Ramamoorthi

Advanced Computer Graphics (Spring 2005) COMS 4162, Lecture 21: Image-Based Rendering Ravi Ramamoorthi
Representations of Visual Appearance COMS 6160 [Spring 2007], Lecture 4 Image-Based Modeling and Rendering

Representations of Visual Appearance COMS 6160 [Spring 2007], Lecture 4 Image-Based Modeling and Rendering
Image-Based Modeling and Rendering CS 6998 Lecture 6.

Image-Based Modeling and Rendering CS 6998 Lecture 6.
Advanced Computer Graphics (Fall 2010) CS 283, Lecture 17: Frequency Analysis and Signal Processing for Rendering Ravi Ramamoorthi

Advanced Computer Graphics (Fall 2010) CS 283, Lecture 17: Frequency Analysis and Signal Processing for Rendering Ravi Ramamoorthi
Image-Based Rendering Produce a new image from real images. Combining images Interpolation More exotic methods.

Image-Based Rendering Produce a new image from real images. Combining images Interpolation More exotic methods.
Approximate Soft Shadows on Arbitrary Surfaces using Penumbra Wedges Tomas Akenine-Möller Ulf Assarsson Department of Computer Engineering, Chalmers University.

Approximate Soft Shadows on Arbitrary Surfaces using Penumbra Wedges Tomas Akenine-Möller Ulf Assarsson Department of Computer Engineering, Chalmers University.

Similar presentations

Ads by Google