3D Shape and Indirect Appearance By Structured Light Transport. Authors: O Toole, Mather, and Kutulakos Presented by: Harrison Billmers, Allen Hawkes

Similar documents
3D Shape and Indirect Appearance By Structured Light Transport

Epipolar Geometry CSE P576. Dr. Matthew Brown

3D graphics, raster and colors CS312 Fall 2010

Inverse Light Transport (and next Separation of Global and Direct Illumination)

Depth Sensors Kinect V2 A. Fornaser

CS635 Spring Department of Computer Science Purdue University

12/3/2009. What is Computer Vision? Applications. Application: Assisted driving Pedestrian and car detection. Application: Improving online search

3D Sensing. 3D Shape from X. Perspective Geometry. Camera Model. Camera Calibration. General Stereo Triangulation.

Image Formation. Ed Angel Professor of Computer Science, Electrical and Computer Engineering, and Media Arts University of New Mexico

Depth. Common Classification Tasks. Example: AlexNet. Another Example: Inception. Another Example: Inception. Depth

Epipolar geometry contd.

3D Sensing and Reconstruction Readings: Ch 12: , Ch 13: ,

CV: 3D to 2D mathematics. Perspective transformation; camera calibration; stereo computation; and more

Introduction to Computer Graphics with WebGL

Global Illumination CS334. Daniel G. Aliaga Department of Computer Science Purdue University

Stereo Vision. MAN-522 Computer Vision

Computational light transport

Radiance. Pixels measure radiance. This pixel Measures radiance along this ray

Three-Dimensional Sensors Lecture 2: Projected-Light Depth Cameras

More computational light transport

Machine vision. Summary # 11: Stereo vision and epipolar geometry. u l = λx. v l = λy

Finally: Motion and tracking. Motion 4/20/2011. CS 376 Lecture 24 Motion 1. Video. Uses of motion. Motion parallax. Motion field

(Sample) Final Exam with brief answers

CPSC GLOBAL ILLUMINATION

Multiple View Geometry

Last update: May 4, Vision. CMSC 421: Chapter 24. CMSC 421: Chapter 24 1

Lecture 14: Basic Multi-View Geometry

The end of affine cameras

Depth Camera for Mobile Devices

calibrated coordinates Linear transformation pixel coordinates

Other approaches to obtaining 3D structure

1 (5 max) 2 (10 max) 3 (20 max) 4 (30 max) 5 (10 max) 6 (15 extra max) total (75 max + 15 extra)

3D Modeling using multiple images Exam January 2008

CHAPTER 3 DISPARITY AND DEPTH MAP COMPUTATION

Project 4 Results. Representation. Data. Learning. Zachary, Hung-I, Paul, Emanuel. SIFT and HoG are popular and successful.

Multiple View Geometry

Stereo and structured light

3D Scanning. Qixing Huang Feb. 9 th Slide Credit: Yasutaka Furukawa

Srikumar Ramalingam. Review. 3D Reconstruction. Pose Estimation Revisited. School of Computing University of Utah

Game Programming. Bing-Yu Chen National Taiwan University

L2 Data Acquisition. Mechanical measurement (CMM) Structured light Range images Shape from shading Other methods

Lecture 16: Computer Vision

PERFORMANCE CAPTURE FROM SPARSE MULTI-VIEW VIDEO

Structure from motion

What have we leaned so far?

Multi-view stereo. Many slides adapted from S. Seitz

Occlusion Detection of Real Objects using Contour Based Stereo Matching

The main problem of photogrammetry

Epipolar Geometry Prof. D. Stricker. With slides from A. Zisserman, S. Lazebnik, Seitz

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

Range Imaging Through Triangulation. Range Imaging Through Triangulation. Range Imaging Through Triangulation. Range Imaging Through Triangulation

BIL Computer Vision Apr 16, 2014

Two-View Geometry (Course 23, Lecture D)

Stereo. 11/02/2012 CS129, Brown James Hays. Slides by Kristen Grauman

Projector Calibration for Pattern Projection Systems

Recap from Previous Lecture

3D Computer Vision. Structured Light I. Prof. Didier Stricker. Kaiserlautern University.

A three-step system calibration procedure with error compensation for 3D shape measurement

TRASLO 3D Reconstruction with Active Stereo for picking and mapping in Automated Warehouses.

3D Computer Vision. Depth Cameras. Prof. Didier Stricker. Oliver Wasenmüller

CS201 Computer Vision Camera Geometry

Geometry of Multiple views

SAMPLING AND NOISE. Increasing the number of samples per pixel gives an anti-aliased image which better represents the actual scene.

Lecture 6 Stereo Systems Multi- view geometry Professor Silvio Savarese Computational Vision and Geometry Lab Silvio Savarese Lecture 6-24-Jan-15

Lecture 16: Computer Vision

Srikumar Ramalingam. Review. 3D Reconstruction. Pose Estimation Revisited. School of Computing University of Utah

Fundamentals of Stereo Vision Michael Bleyer LVA Stereo Vision

CSE 252B: Computer Vision II

Project 3 Path Tracing

Structured Light. Tobias Nöll Thanks to Marc Pollefeys, David Nister and David Lowe

CS4495/6495 Introduction to Computer Vision

Lecture 19: Depth Cameras. Visual Computing Systems CMU , Fall 2013

3D shape from the structure of pencils of planes and geometric constraints

Multiple View Geometry in Computer Vision

Structured Light II. Guido Gerig CS 6320, Spring (thanks: slides Prof. S. Narasimhan, CMU, Marc Pollefeys, UNC)

Introduction to 3D Imaging: Perceiving 3D from 2D Images

Computer Graphics. Bing-Yu Chen National Taiwan University The University of Tokyo

MITOCW MIT6_172_F10_lec18_300k-mp4

Complex Sensors: Cameras, Visual Sensing. The Robotics Primer (Ch. 9) ECE 497: Introduction to Mobile Robotics -Visual Sensors

Supplementary Materials for

CS 4495 Computer Vision A. Bobick. Motion and Optic Flow. Stereo Matching

Lecture 9: Epipolar Geometry

Live Metric 3D Reconstruction on Mobile Phones ICCV 2013

Structure from Motion. Introduction to Computer Vision CSE 152 Lecture 10

Camera Calibration. Schedule. Jesus J Caban. Note: You have until next Monday to let me know. ! Today:! Camera calibration

Lecture 10: Multi view geometry

Motion Analysis. Motion analysis. Now we will talk about. Differential Motion Analysis. Motion analysis. Difference Pictures

Local features: detection and description. Local invariant features

Math 414 Lecture 30. The greedy algorithm provides the initial transportation matrix.

Image Based Reconstruction II

55:148 Digital Image Processing Chapter 11 3D Vision, Geometry

CEE598 - Visual Sensing for Civil Infrastructure Eng. & Mgmt.

Augmenting Reality, Naturally:

Direct Plane Tracking in Stereo Images for Mobile Navigation

Estimation of common groundplane based on co-motion statistics

CS231A Course Notes 4: Stereo Systems and Structure from Motion

Capturing, Modeling, Rendering 3D Structures

Today. Stereo (two view) reconstruction. Multiview geometry. Today. Multiview geometry. Computational Photography

Vision-based Mobile Robot Localization and Mapping using Scale-Invariant Features

Intro to Ray-Tracing & Ray-Surface Acceleration

Transcription:

3D Shape and Indirect Appearance By Structured Light Transport Authors: O Toole, Mather, and Kutulakos Presented by: Harrison Billmers, Allen Hawkes

Background - Indirect Light - Indirect light can be dominant in natural scenes - - Large contributor to actual appearance This paper develops framework for measuring/removing indirect light - General, unknown scene Unrestricted motion of camera (video, scene changing) 1 or more controllable sources (projectors) Frames from indirect lighting video results

4 Imaging Problems Addressed A. B. C. D. 1-shot Indirect-Only image 1-shot Indirect-Invariant image 2-shot Direct-Only image 1-shot Multi-Pattern image

A. 1-Shot Indirect-Only

B. 1-Shot Indirect-Invariant - Keep indirect light constant Appearance of direct light improves reconstruction

C. 2-Shot Direct-Only Full Illumination = Indirect-only Direct-Only

D. 1-Shot Multi-Pattern

Setup - 1 Camera: 28 Hz 2 Digital micromirror devices (DMDs): 2.7-24 khz Do simultaneous projection & masking within single camera image - Geometric light analysis

Stereo Transport Matrix 3 types of light that can reach a camera (left) from the projector (right) 1. Epipolar + Direct (black line, 1 bounce) 2. Epipolar + Indirect (green line, 2+ bounces but in-plane) 3. Non-epipolar + Indirect (red line, 2+ bounces out of plane)

Stereo Transport Matrix Order of pixels rearranged to be in epipolar lines Direct/epipolar: contours Indirect/epipolar: off-contour points

Dominance of Non-Epipolar Transport In practical applications, indirect + non-epipolar far outweighs indirect + epipolar transport. Intuition: non-direct light mostly leaves epipolar plane Result: all epipolar light is direct light (easily separate epipolar/non-epipolar)

Structured Light Transport: Probing Matrix To make full use of T s structure, we structure the flow of light itself Probe the light transport matrix using a pattern Use the a technique inspired by the Primal-dual method Epipolar light/mask

Structured Light Imaging Pattern is projected across entirety of the probing matrix (no consideration of epipolar structure)

Indirect-invariant Imaging Utilize the structure to separate components

Indirect-only and Epipolar-only imaging

One-shot,multi-pattern, indirect-invariant imaging Allow for a whole array of structured light patterns (spatially multiplexed) in a single capture.

Digital Micromirror Device (DMD)-based setup Rewrite probing equation as: And now: Which is a ROD

Indirect-only Imaging For a given epipolar line e, q(e) is 1 only along line e m(e) is 1 everywhere except line e Thus, This is slow. Instead, use randomly selected lines, giving:

Epipolar-Only Imaging Special case where no short ROD exists. However, Meaning that this probing matrix can be generated from a captured white image and subtracting indirect-only result. Two video frames are used.

Indirect-invariant imaging Allow for both direct and indirect components to be viewed. Epipolar light + mask for indirect-invariant imaging (i.e. for reconstruction)

One-shot, multi-pattern, indirect-invariant imaging

Results Holding indirect lighting constant greatly improves 3D existing reconstruction algorithm Multiplexed patterns in 1 image allow for single-capture depth reconstruction

Review of Work Score: 1 Contributions: - Indirect video streaming Greatly improve shape-from-shading algorithms Dynamic 3D shape capture for single images

Questions? 3D Shape and Indirect Appearance By Structured Light Transport Authors: O Toole, Mather, and Kutulakos Presented by: Harrison Billmers, Allen Hawkes

Reference: Epipolar Illumination+Masks Epipolar light + mask for indirect-only imaging Epipolar light + mask for indirect-invariant imaging (i.e. for reconstruction)

Designs for the probing matrix

Designs for the probing matrix