Structured Light in Scattering Media Srinivasa Narasimhan Sanjeev Koppal Robotics Institute Carnegie Mellon University Sponsor: ONR Shree Nayar Bo Sun.

Slides:

Advertisements

Similar presentations

DEPTH FROM DISTORTION: PERFORMING 3D RECONSTRUCTION USING A CURVED MIRROR AND SINGLE CAMERA DREXEL UNIVERSITY DEARTMENT OF MATHEMATICS ASST. PROF. ANDREW.

Advertisements

Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5403 T-R 3:00pm – 4:20pm Lecture #12.

3D Modeling from a photograph

Fast Separation of Direct and Global Images Using High Frequency Illumination Shree K. Nayar Gurunandan G. Krishnan Columbia University SIGGRAPH Conference.

Procam and Campro Shree K. Nayar Computer Science Columbia University Support: NSF, ONR Procams 2006 PROCAMS Shree K. Nayar,

Micro Phase Shifting Mohit Gupta and Shree K. Nayar Computer Science Columbia University Supported by: NSF and ONR.

--- some recent progress Bo Fu University of Kentucky.

Capturing light Source: A. Efros. Image formation How bright is the image of a scene point?

Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5403 T-R 3:00pm – 4:20pm Lecture #17.

Vision Sensing. Multi-View Stereo for Community Photo Collections Michael Goesele, et al, ICCV 2007 Venus de Milo.

Acquiring Scattering Properties of Participating Media by Dilution SIGGRAPH Conference July 2006, Boston, USA Sponsors: NSF, ONR, Sloan Srinivasa Narasimhan.

16421: Vision Sensors Lecture 6: Radiometry and Radiometric Calibration Instructor: S. Narasimhan Wean 5312, T-R 1:30pm – 2:50pm.

Basic Principles of Surface Reflectance

Shadow Scanning [Bouguet 1999] Desk Lamp Camera Stick or pencil Desk The idea [Bouguet and Perona’98] J.-Y. Bouguet and P. Perona, “3D Photography on your.

Torrance Sparrow Model of Reflectance + Oren Nayar Model of Reflectance.

Shape-from-X Class 11 Some slides from Shree Nayar. others.

Lecture 23: Photometric Stereo CS4670/5760: Computer Vision Kavita Bala Scott Wehrwein.

Shree Nayar and Srinivasa Narasimhan Computer Science Columbia University ICCV Conference Korfu, Greece, September 1999 Sponsors: NSF Vision in Bad Weather.

Multiple View Geometry : Computational Photography Alexei Efros, CMU, Fall 2005 © Martin Quinn …with a lot of slides stolen from Steve Seitz and.

When Does a Camera See Rain? Department of Computer Science Columbia University Kshitiz Garg Shree K. Nayar ICCV Conference October 2005, Beijing, China.

Stereoscopic Light Stripe Scanning: Interference Rejection, Error Minimization and Calibration By: Geoffrey Taylor Lindsay Kleeman Presented by: Ali Agha.

Visibility Subspaces: Uncalibrated Photometric Stereo with Shadows Kalyan Sunkavalli, Harvard University Joint work with Todd Zickler and Hanspeter Pfister.

Introduction to Computer Vision CS / ECE 181B Tues, May 18, 2004 Ack: Matthew Turk (slides)

Basic Principles of Surface Reflectance

Image-based Rendering of Real Objects with Complex BRDFs.

Chromatic Framework for Vision in Bad Weather Srinivasa G. Narasimhan and Shree K. Nayar Computer Science Department Columbia University IEEE CVPR Conference.

Basic Principles of Surface Reflectance Lecture #3 Thanks to Shree Nayar, Ravi Ramamoorthi, Pat Hanrahan.

Stereo Guest Lecture by Li Zhang

Removing Weather Effects from Monochrome Images Srinivasa Narasimhan and Shree Nayar Computer Science Department Columbia University IEEE CVPR Conference.

Basic Principles of Imaging and Photometry Lecture #2 Thanks to Shree Nayar, Ravi Ramamoorthi, Pat Hanrahan.

Light and shading Source: A. Efros.

RAY TRACING WITH DISPERSION CSS552 – Topics in Rendering Winter 2011 Final Project by: Kohei Ueda Shivani Srikanteshwara Mary Ann Chiramattel Kunjachan.

Novel Depth Cues from Uncalibrated Near-field Lighting Sanjeev J. Koppal and Srinivasa Narasimhan Carnegie Mellon University Sponsors: ONR and NSF.

Computer Vision Spring ,-685 Instructor: S. Narasimhan PH A18B T-R 10:30am – 11:50am Lecture #13.

1 Intelligent Robotics Research Centre (IRRC) Department of Electrical and Computer Systems Engineering Monash University, Australia Visual Perception.

Lecture 12 Stereo Reconstruction II Lecture 12 Stereo Reconstruction II Mata kuliah: T Computer Vision Tahun: 2010.

Shedding Light on the Weather

Multiple Scattering in Vision and Graphics Lecture #21 Thanks to Henrik Wann Jensen.

Y. Moses 11 Combining Photometric and Geometric Constraints Yael Moses IDC, Herzliya Joint work with Ilan Shimshoni and Michael Lindenbaum, the Technion.

Recap from Monday Image Warping – Coordinate transforms – Linear transforms expressed in matrix form – Inverse transforms useful when synthesizing images.

What Does Motion Reveal About Transparency ? Moshe Ben-Ezra and Shree K. Nayar Columbia University ICCV Conference October 2003, Nice, France This work.

Capturing light Source: A. Efros.

Week 10 - Wednesday.  What did we talk about last time?  Shadow volumes and shadow mapping  Ambient occlusion.

Computer Graphics Global Illumination: Photon Mapping, Participating Media Lecture 12 Taku Komura.

Stereo Readings Szeliski, Chapter 11 (through 11.5) Single image stereogram, by Niklas EenNiklas Een.

Dynamic Refraction Stereo 7. Contributions Refractive disparity optimization gives stable reconstructions regardless of surface shape Require no geometric.

Radiometry and Photometric Stereo 1. Estimate the 3D shape from shading information Can you tell the shape of an object from these photos ? 2.

Lec 22: Stereo CS4670 / 5670: Computer Vision Kavita Bala.

Course 10 Shading. 1. Basic Concepts: Light Source: Radiance: the light energy radiated from a unit area of light source (or surface) in a unit solid.

Compressive Structured Light for Recovering Inhomogeneous Participating Media Jinwei Gu, Shree Nayar, Eitan Grinspun Peter Belhumeur, and Ravi Ramamoorthi.

Interreflections : The Inverse Problem Lecture #12 Thanks to Shree Nayar, Seitz et al, Levoy et al, David Kriegman.

Diffuse Reflections from Rough Surfaces Lecture #5

Announcements Office hours today 2:30-3:30 Graded midterms will be returned at the end of the class.

Photo-realistic Rendering and Global Illumination in Computer Graphics Spring 2012 Material Representation K. H. Ko School of Mechatronics Gwangju Institute.

Multiple Light Source Optical Flow Multiple Light Source Optical Flow Robert J. Woodham ICCV’90.

Project 2 due today Project 3 out today Announcements TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAA.

Tal Amir Advanced Topics in Computer Vision May 29 th, 2015 COUPLED MOTION- LIGHTING ANALYSIS.

Global Illumination (3) Photon Mapping (1). Overview Light Transport Notation Path Tracing Photon Mapping –Photon Tracing –The Photon Map.

Bo Sun Kalyan Sunkavalli Ravi Ramamoorthi Peter Belhumeur Shree Nayar Columbia University Time-Varying BRDFs.

Computer vision: geometric models Md. Atiqur Rahman Ahad Based on: Computer vision: models, learning and inference. ©2011 Simon J.D. Prince.

Using Clouds Shadows to Infer Scene Structure and Camera Calibration

MAN-522 Computer Vision Spring

Instant Dehazing of Images using Polarization

A Practical Analytic Single Scattering Model for Real Time Rendering

CS4670 / 5670: Computer Vision Kavita Bala Lec 27: Stereo.

Image Restoration using Model-based Tracking

Merle Norman Cosmetics, Los Angeles

Lecture 28: Photometric Stereo

Shape from Shading and Texture

Presentation transcript:

Structured Light in Scattering Media Srinivasa Narasimhan Sanjeev Koppal Robotics Institute Carnegie Mellon University Sponsor: ONR Shree Nayar Bo Sun Computer Science Columbia University ICCV Conference October 2005, Beijing, China

Natural illumination in Scattering Media [ Narasimhan and Nayar, , Schechner et al, 01, 04 ]

Active illumination in Scattering Media [Levoy et al., Narasimhan-Nayar, Kocak-Caimi, Jaffe et al., Schechner et al., Negahdaripour et al. ]

Floodlighting is Bad in Scattering Media Structured Light Critical for Good Visibility

Light Stripe Range Finding in Clear Air Camera Source Surface Light plane Camera Source Surface Light plane Light Stripe Range Finding in Scattering Media

Light Striping Model in Scattering Media Extinction coefficient D v Irradiance due to Medium: Camera Source Surface Light plane Radiance α x y D s Irradiance due to Surface: Final Image Irradiance: Phase Function

Light Striping Algorithm in Scattering Media Surface Intersection from Brightness Profile: 3D by Triangulation or Temporal Analysis : Same as in clear air. Medium from Fall-off : “Clear-Air” Scene Appearance: No Scattering Moderate Scattering Significant Scattering

Experimental Setup Calibration technique similar in spirit to [Grossberg-Nayar 01 ]

Experimental Setup and Calibration Light PlaneViewing Ray Glass No Refractive index and location of glass or medium No explicit calibration of camera and projector Similar in spirit to [ Levoy-Hanrahan 96, Grossberg-Nayar 01 ]

Floodlit ImageComputed Appearance

Triangulation IssueSurface Reflectance Issue CameraProjector Surface CameraProjector Still a problem  Solved if Light Plane is visible In Scattering Media: Surface How to Place the Camera and Projector?

Smoke and MirrorsMilk and Mirrors [Discussions with Marc Levoy] Planar Mirror seen through Dilute Milk Light Striping of Mirrors (Dark Intersections) Reconstruct surfaces with any BRDF if light plane visible

Three images required. Photometric Stereo in Clear Air [ Woodham 80, Horn 86 ] Distant Source Orthographic Camera n s P Surface Pure Air Image Irradiance: Surface normal Source directionAlbedo

Photometric Stereo in Scattering Media Scattering Medium Parallel Rays from Distant Source Orthographic Camera α n s P D s D v Surface Image Irradiance: + Optical Thickness Phase Function

Photometric Stereo in Scattering Media Scattering Medium Parallel Rays from Distant Source Orthographic Camera α n s P D s D v Surface 5 Parameter Non-linear Optimization (4 per pixel, 1 global) : Five Non-degenerate Sources are Necessary and Sufficient

Simulations: Error Histograms ( x 10 ) Fractional Error for Albedo Fractional Error for Phase Function, g Fractional Error for Optical Thickness Angular Error for Normals ( x 10 ) ( x 10 ) ( x 10 ) Zero error with zero noise. Robust estimation with 5% uniform noise. Trials

Experiments: Teapot in Pure Water

Experiments: Teapot in Dilute Milk Low Contrast, Flat Appearance

Results: Traditional Photometric Stereo 3D Shape from Normals Too Flat Albedos Scattering effects present

Results: Our Five-Source Algorithm 3D Shape from NormalsAlbedos

Results: Depth from Photometric Stereo 3D Shape from Normals Depth map Impossible using traditional method % RMS Error 3 ml4 ml5 ml6 ml12 ml15 ml Milk Concentration

Surprising results possible because of scattering Structured light improves visibility Summary Physics of scattering crucial