Computational Imaging for Self-Driving Vehicles
|
|
|
- Aubrie Cross
- 5 years ago
- Views:
Transcription
1 CVPR 2018 Computational Imaging for Self-Driving Vehicles Jan Kautz Ramesh Raskar Achuta Kadambi Guy Satat
2 Computational Imaging for Self-Driving Vehicles Jan Kautz Ramesh Raskar Achuta Kadambi Guy Satat Computational Imaging Self Driving Cars Novel Sensors LIDAR Challenging Weather Open Problems Deep Learning
3 Course Schedule imagingav.media.mit.edu 13:30 13:50 Course Introduction Ramesh Raskar (MIT) 13:50 15:00 Existing Sensors and Their Limits Guy Satat (MIT), Achuta Kadambi (UCLA) 15:00 15:10 Break 15:10 15:50 Emerging 3D Sensors Achuta Kadambi (UCLA) 15:50 16:30 Imaging in Bad Weather Guy Satat (MIT) 16:30 16:40 Break 16:40 17:20 Deep Learning-based Computational Imaging Jan Kautz (NVIDIA) 17:20 17:30 Conclusion and Open Problems
4
5
6
7 Classification Resolution Localization Cost Any Weather Radar Sonar Camera LiDAR
8 Photon Hacking Optics Computational Light Transport Sensors Illumination Computational Imaging Signal Processing Computer Vision Machine Learning Bit Hacking
9 Plenoptic Light Transport Viewpoint Diversity (Light Field Cam) Bounce Index (Scattering) Wavelength Diversity (Hyperspectral Cam) Polarization Diversity (Photos, Shape, Scatter) Time of Flight (3D, Scattering) Adelson and Bergen The Plenoptic Function MIT Press 1991
10
11
12 Visible X rays UV IR Microwave Radio Waves Wavelength Resolution Optical Contrast Non ionizing Availability of fluorophores
13 Optical Contrast Visible light X-Ray Wildlife Rehabilitation Center of Minnesota
14 Visible X rays UV IR Microwave Radio Waves Wavelength
15 Light Interaction Object Lens Absorption Scattering
16 Diffraction Dynamic Range Descattering Defocus Depth
17 1964
18 Conquer.. Time Milli Micro Nano Pico Femto Atto
19 Conquer.. Time Noise Milli Signal Micro Nano Pico Femto-graphy Atto
20 Light in Slow Motion.. 10 Billion x Slow
21
22 Velten et al Siggraph 2013
23 Can you see around corners?
24 Velten et al. Nature Comm 2012 Multi-path Analysis 2 nd Bounce 1 st Bounce 3 rd Bounce
25 Raw Data
26 Velten et al, Nature Communications 2012 Hidden Femto-Camera Mannequin Wall Door
27
28
29 Collision avoidance, robot navigation, DARPA REVEAL Program by Dr. Predrag Milojkovic 29
30 Identify Materials
31
32 BRDF from a single viewpoint Naik, Zhao, Velten, Raskar, Bala, (SIGGRAPH Asia 2011)
33 Single Photon Sensitive Imaging (SPAD) Gariepy et al. Nature Comm (2015)
34
35
36 Nanophotography 70 picosecond resolution [Kadambi et al 2013]
37 5-D Transport Multi-Dimensional Light Transport
38 Question: How does Imaging influence Autonomy? Level 0: Driver only: the human driver controls everything independently, Level 1: Assisted driving: assistance systems help during vehicle operation (Cruise Control, ACC). Level 2: Partial automation: the operator must monitor the system at all times. Level 3: Conditional automation: the operator monitors the system and can intervene when necessary. Level 4: High automation: there is no monitoring by the driver required. Vehicles are designed to operate safety-critical functions and monitor road conditions for an entire trip. However, the functions do not cover all every driving scenario. Level 5: Full automation: operator-free driving.
39
40 Kadambi, Bhandari et al
41 Course Schedule imagingav.media.mit.edu 13:30 13:50 Course Introduction Ramesh Raskar (MIT) 13:50 15:00 Existing Sensors and Their Limits Guy Satat (MIT), Achuta Kadambi (UCLA) 15:00 15:10 Break 15:10 15:50 Emerging 3D Sensors Achuta Kadambi (UCLA) 15:50 16:30 Imaging in Bad Weather Guy Satat (MIT) 16:30 16:40 Break 16:40 17:20 Deep Learning-based Computational Imaging Jan Kautz (NVIDIA) 17:20 17:30 Conclusion and Open Problems
42 CVPR 2018 Computational Imaging for Self-Driving Vehicles Jan Kautz Ramesh Raskar Achuta Kadambi Guy Satat
Computational Imaging for Self-Driving Vehicles
CVPR 2018 Computational Imaging for Self-Driving Vehicles Jan Kautz--------Ramesh Raskar--------Achuta Kadambi--------Guy Satat Computational Imaging for Self-Driving Vehicles Jan Kautz--------Ramesh Raskar--------Achuta
Active Light Time-of-Flight Imaging
Active Light Time-of-Flight Imaging Time-of-Flight Pulse-based time-of-flight scanners [Gvili03] NOT triangulation based short infrared laser pulse is sent from camera reflection is recorded in a very
Non-line-of-sight imaging
Non-line-of-sight imaging http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 25 Course announcements Homework 6 will be posted tonight. - Will
ToF Camera for high resolution 3D images with affordable pricing
ToF Camera for high resolution 3D images with affordable pricing Basler AG Jana Bartels, Product Manager 3D Agenda Coming next I. Basler AG II. 3D Purpose and Time-of-Flight - Working Principle III. Advantages
TRANSIENT IMAGING. Freshman Imaging Project 2014
1 TRANSIENT IMAGING Freshman Imaging Project 2014 2 Outline What is transient imaging? Previous work: MIT, UBC Possible applications of transient imaging Theory behind transient imaging Our goals Technology
Overview of Active Vision Techniques
SIGGRAPH 99 Course on 3D Photography Overview of Active Vision Techniques Brian Curless University of Washington Overview Introduction Active vision techniques Imaging radar Triangulation Moire Active
Phys 1020, Day 18: Questions? Cameras, Blmfld Reminders: Next Up: digital cameras finish Optics Note Final Project proposals next week!
Lights. Action. Phys 1020, Day 18: Questions? Cameras, Blmfld 15.1 Reminders: Next Up: digital cameras finish Optics Note Final Project proposals next week! 1 What have we learned in this section: 1) Lasers
Multi-View Stereo for Community Photo Collections Michael Goesele, et al, ICCV Venus de Milo
Vision Sensing Multi-View Stereo for Community Photo Collections Michael Goesele, et al, ICCV 2007 Venus de Milo The Digital Michelangelo Project, Stanford How to sense 3D very accurately? How to sense
Sensor Modalities. Sensor modality: Different modalities:
Sensor Modalities Sensor modality: Sensors which measure same form of energy and process it in similar ways Modality refers to the raw input used by the sensors Different modalities: Sound Pressure Temperature
Photonic Technologies for LiDAR in Autonomous/ADAS. Jake Li (Market Specialist SiPM & Automotive) Hamamatsu Corporation
Photonic Technologies for LiDAR in Autonomous/ADAS Jake Li (Market Specialist SiPM & Automotive) 03-2018 Hamamatsu Corporation Outline of Presentation 1. Introduction to Hamamatsu 2. Autonomous Levels
Non-line-of-sight imaging
Non-line-of-sight imaging http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 23 Course announcements Homework 6 posted, due November 30 th. -
Modeling Light. Michal Havlik : Computational Photography Alexei Efros, CMU, Fall 2007
Modeling Light Michal Havlik 15-463: Computational Photography Alexei Efros, CMU, Fall 2007 The Plenoptic Function Figure by Leonard McMillan Q: What is the set of all things that we can ever see? A: The
Computational Cameras: Exploiting Spatial- Angular Temporal Tradeoffs in Photography
Mitsubishi Electric Research Labs (MERL) Computational Cameras Computational Cameras: Exploiting Spatial- Angular Temporal Tradeoffs in Photography Amit Agrawal Mitsubishi Electric Research Labs (MERL)
DUAL MODE SCANNER for BROKEN RAIL DETECTION
DUAL MODE SCANNER for BROKEN RAIL DETECTION ROBERT M. KNOX President; Epsilon Lambda Electronics Dr. BENEDITO FONSECA Northern Illinois University Presenting a concept for improved rail safety; not a tested
Other Reconstruction Techniques
Other Reconstruction Techniques Ruigang Yang CS 684 CS 684 Spring 2004 1 Taxonomy of Range Sensing From Brain Curless, SIGGRAPH 00 Lecture notes CS 684 Spring 2004 2 Taxonomy of Range Scanning (cont.)
Optical Sensors: Key Technology for the Autonomous Car
Optical Sensors: Key Technology for the Autonomous Car Rajeev Thakur, P.E., Product Marketing Manager, Infrared Business Unit, Osram Opto Semiconductors Autonomously driven cars will combine a variety
Compressive Sensing of High-Dimensional Visual Signals. Aswin C Sankaranarayanan Rice University
Compressive Sensing of High-Dimensional Visual Signals Aswin C Sankaranarayanan Rice University Interaction of light with objects Reflection Fog Volumetric scattering Human skin Sub-surface scattering
Introduction to Computer Vision. Week 8, Fall 2010 Instructor: Prof. Ko Nishino
Introduction to Computer Vision Week 8, Fall 2010 Instructor: Prof. Ko Nishino Midterm Project 2 without radial distortion correction with radial distortion correction Light Light Light! How do you recover
Outline. ETN-FPI Training School on Plenoptic Sensing
Outline Introduction Part I: Basics of Mathematical Optimization Linear Least Squares Nonlinear Optimization Part II: Basics of Computer Vision Camera Model Multi-Camera Model Multi-Camera Calibration
Digital Reality: Using Trained Models of Reality to Synthesize Data for the Training & Validating Autonomous Systems Philipp Slusallek
Digital Reality: Using Trained Models of Reality to Synthesize Data for the Training & Validating Autonomous Systems Philipp Slusallek German Research Center for Artificial Intelligence (DFKI) Saarland
Computational Photography: Advanced Topics. Paul Debevec
Computational Photography: Advanced Topics Paul Debevec Class: Computational Photography, Advanced Topics Module 1: 105 minutes Debevec,, Raskar and Tumblin 1:45: A.1 Introduction and Overview (Raskar,
W4. Perception & Situation Awareness & Decision making
W4. Perception & Situation Awareness & Decision making Robot Perception for Dynamic environments: Outline & DP-Grids concept Dynamic Probabilistic Grids Bayesian Occupancy Filter concept Dynamic Probabilistic
Solid State LiDAR for Ubiquitous 3D Sensing
April 6, 2016 Solid State LiDAR for Ubiquitous 3D Sensing Louay Eldada, Ph.D. CEO, Co-founder Quanergy Systems New Paradigm in 3D Sensing Disruptive Technologies: Solid State 3D LiDAR sensors Embedded
Lecture 26, March 16, Chapter 35, Polarization
Physics 5B Lecture 26, March 16, 2012 Chapter 35, Polarization Simple Spectrometer d sin m Resolving power, to separate two lines closely spaced in wavelength by : R mn Resolving Power Two lines not resolved.
rendering equation computer graphics rendering equation 2009 fabio pellacini 1
rendering equation computer graphics rendering equation 2009 fabio pellacini 1 physically-based rendering synthesis algorithms that compute images by simulation the physical behavior of light computer
Radiometry and reflectance
Radiometry and reflectance http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 16 Course announcements Homework 4 is still ongoing - Any questions?
Measuring the World: Designing Robust Vehicle Localization for Autonomous Driving. Frank Schuster, Dr. Martin Haueis
Measuring the World: Designing Robust Vehicle Localization for Autonomous Driving Frank Schuster, Dr. Martin Haueis Agenda Motivation: Why measure the world for autonomous driving? Map Content: What do
Computer Vision. 3D acquisition
è Computer 3D acquisition Acknowledgement Courtesy of Prof. Luc Van Gool 3D acquisition taxonomy s image cannot currently be displayed. 3D acquisition methods Thi passive active uni-directional multi-directional
CLASSIFICATION OF NONPHOTOGRAPHIC REMOTE SENSORS
CLASSIFICATION OF NONPHOTOGRAPHIC REMOTE SENSORS PASSIVE ACTIVE DIGITAL CAMERA THERMAL (e.g. TIMS) VIDEO CAMERA MULTI- SPECTRAL SCANNERS VISIBLE & NIR MICROWAVE HYPERSPECTRAL (e.g. AVIRIS) SLAR Real Aperture
Decomposing global light transport using time of flight imaging
Decomposing global light transport using time of flight imaging The MIT Faculty has made this article openly available Please share how this access benefits you Your story matters Citation As Published
Introduction, Overview, Fast Forward!
Introduction, Overview, Fast Forward! EE367/CS448I: Computational Imaging and Display! stanford.edu/class/ee367! Lecture 1! Gordon Wetzstein! Stanford University! Image: National Geographic! Nature!
Final Project: Real-Time Global Illumination with Radiance Regression Functions
Volume xx (200y), Number z, pp. 1 5 Final Project: Real-Time Global Illumination with Radiance Regression Functions Fu-Jun Luan Abstract This is a report for machine learning final project, which combines
Why the Self-Driving Revolution Hinges on one Enabling Technology: LiDAR
Why the Self-Driving Revolution Hinges on one Enabling Technology: LiDAR Markus Prison Director Business Development Europe Quanergy ID: 23328 Who We Are The leader in LiDAR (laser-based 3D spatial sensor)
Focal stacks and lightfields
Focal stacks and lightfields http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 11 Course announcements Homework 3 is out. - Due October 12 th.
Payloads. Which one(s) are right for your organization?
Payloads Which one(s) are right for your organization? What is a payload anyway? Not as simple as it may sound and critically important. Integrated and fixed? Interchangeable? Field changeable? Impact
3D Computer Vision 1
3D Computer Vision 1 Multiview Stereo Multiview Stereo Multiview Stereo https://www.youtube.com/watch?v=ugkb7itpnae Shape from silhouette Shape from silhouette Shape from silhouette Shape from silhouette
ESPROS Photonics Corporation
Next generation pulsed time-of-flight sensors for autonomous driving Beat De Coi 1 Topics ADAS requirements Sensor technology overview ESPROS CCD/CMOS technology OHC15LTM Technology comparison of receiver
UNIT 22. Remote Sensing and Photogrammetry. Start-up. Reading. In class discuss the following questions:
UNIT 22 Remote Sensing and Photogrammetry Start-up In class discuss the following questions: A. What do you know about remote sensing? B. What does photogrammetry do? C. Is there a connection between the
Supplier Business Opportunities on ADAS and Autonomous Driving Technologies
AUTOMOTIVE Supplier Business Opportunities on ADAS and Autonomous Driving Technologies 19 October 2016 Tokyo, Japan Masanori Matsubara, Senior Analyst, +81 3 6262 1734, Masanori.Matsubara@ihsmarkit.com
Sensor technology for mobile robots
Laser application, vision application, sonar application and sensor fusion (6wasserf@informatik.uni-hamburg.de) Outline Introduction Mobile robots perception Definitions Sensor classification Sensor Performance
Cybersecurity Challenges for Connected and Automated Vehicles. Robert W. Heller, Ph.D. Program Director R&D, Southwest Research Institute
Cybersecurity Challenges for Connected and Automated Vehicles Robert W. Heller, Ph.D. Program Director R&D, Southwest Research Institute Cars are becoming complex (and CAV is only part of it) 1965: No
Automotive LiDAR. General Motors R&D. Ariel Lipson
Automotive LiDAR General Motors R&D Ariel Lipson Overview How LiDARs work (Automotive) State of play current devices, costs Alternative technologies / approaches and future outlook Advantages of LiDAR-enabled
Precision Roadway Feature Mapping Jay A. Farrell, University of California-Riverside James A. Arnold, Department of Transportation
Precision Roadway Feature Mapping Jay A. Farrell, University of California-Riverside James A. Arnold, Department of Transportation February 26, 2013 ESRA Fed. GIS Outline: Big picture: Positioning and
X-COP R700 NEOLINE DVR WITH GPS BASE OF POLICE RADARS AND CAMERAS
X-COP R700 NEOLINE DVR WITH GPS BASE OF POLICE RADARS AND CAMERAS NEOLINE X-COP R700 Neoline X-COP R700 the new generation of DVR with GPS base of police radars and cameras. Touch-screen display, manage
LiDAR for ADAS and Autonomous Driving (Intro to LiDAR Face-Off at Sensors EXPO 2018)
LiDAR for ADAS and Autonomous Driving (Intro to LiDAR Face-Off at Sensors EXPO 2018) Jake Li (Business Development Manager Auto LiDAR) 06-2018 Hamamatsu Corporation The NEXT Generation of LiDAR LiDAR Faceoff
Light Field Spring
Light Field 2015 Spring Recall: Light is Electromagnetic radiation (EMR) moving along rays in space R(l) is EMR, measured in units of power (watts) l is wavelength Useful things: Light travels in straight
AUTOMATIC 3D HUMAN ACTION RECOGNITION Ajmal Mian Associate Professor Computer Science & Software Engineering
AUTOMATIC 3D HUMAN ACTION RECOGNITION Ajmal Mian Associate Professor Computer Science & Software Engineering www.csse.uwa.edu.au/~ajmal/ Overview Aim of automatic human action recognition Applications
Transient Rendering. Adam Smith James Skorupski. University of California, Santa Cruz December 11, 2007 CMPS 290B
Transient Rendering Adam Smith James Skorupski University of California, Santa Cruz December 11, 2007 CMPS 290B {amsmith,jskorups}@cs.ucsc.edu 2/32 Motivation There is growing interest in time-of-flight
Connected Car. Dr. Sania Irwin. Head of Systems & Applications May 27, Nokia Solutions and Networks 2014 For internal use
Connected Car Dr. Sania Irwin Head of Systems & Applications May 27, 2015 1 Nokia Solutions and Networks 2014 For internal use Agenda Introduction Industry Landscape Industry Architecture & Implications
UV-NIR LASER BEAM PROFILER
CinCam CMOS - Technical Data - CMOS-1201 CMOS-1202 CMOS-1203 CMOS-1204 Standard Series Standard Series Standard Series Standard Series SENSOR DATA Format: 1/2 1/1.8 1/1.8 1/2.5 Active area (without cover
Visual Perception Sensors
G. Glaser Visual Perception Sensors 1 / 27 MIN Faculty Department of Informatics Visual Perception Sensors Depth Determination Gerrit Glaser University of Hamburg Faculty of Mathematics, Informatics and
3D sensing technology for smart space
CEATEC 2018 3D sensing technology for smart space Panasonic Semiconductor Solutions Co.,Ltd. 18.Oct.2018 The product is under development. Product specifications described in this document are subject
Solid-State Hybrid LiDAR for Autonomous Driving Product Description
Solid-State Hybrid LiDAR for Autonomous Driving Product Description What is LiDAR Sensor Who is Using LiDARs How does LiDAR Work Hesai LiDAR Demo Features Terminologies Specifications What is LiDAR A LiDAR
New Sony DepthSense TM ToF Technology
ADVANCED MATERIAL HANDLING WITH New Sony DepthSense TM ToF Technology Jenson Chang Product Marketing November 7, 2018 1 3D SENSING APPLICATIONS Pick and Place Drones Collision Detection People Counting
S7316: Real-Time Robotics Control and Simulation for Deformable Terrain Applications Using the GPU
S7316: Real-Time Robotics Control and Simulation for Deformable Terrain Applications Using the GPU Daniel Melanz Copyright 2017 Energid Technology Overview 1. Who are we? 2. What do we do? 3. How do we
Human Detection. A state-of-the-art survey. Mohammad Dorgham. University of Hamburg
Human Detection A state-of-the-art survey Mohammad Dorgham University of Hamburg Presentation outline Motivation Applications Overview of approaches (categorized) Approaches details References Motivation
Range Sensors (time of flight) (1)
Range Sensors (time of flight) (1) Large range distance measurement -> called range sensors Range information: key element for localization and environment modeling Ultrasonic sensors, infra-red sensors
Outline Radiometry of Underwater Image Formation
Outline - Introduction - Features and Feature Matching - Geometry of Image Formation - Calibration - Structure from Motion - Dense Stereo - Radiometry of Underwater Image Formation - Conclusion 1 pool
Depth Sensors Kinect V2 A. Fornaser
Depth Sensors Kinect V2 A. Fornaser alberto.fornaser@unitn.it Vision Depth data It is not a 3D data, It is a map of distances Not a 3D, not a 2D it is a 2.5D or Perspective 3D Complete 3D - Tomography
3 Interactions of Light Waves
CHAPTER 22 3 Interactions of Light Waves SECTION The Nature of Light BEFORE YOU READ After you read this section, you should be able to answer these questions: How does reflection affect the way we see
Range Imaging Through Triangulation. Range Imaging Through Triangulation. Range Imaging Through Triangulation. Range Imaging Through Triangulation
Obviously, this is a very slow process and not suitable for dynamic scenes. To speed things up, we can use a laser that projects a vertical line of light onto the scene. This laser rotates around its vertical
Computational Photography
Computational Photography Photography and Imaging Michael S. Brown Brown - 1 Part 1 Overview Photography Preliminaries Traditional Film Imaging (Camera) Part 2 General Imaging 5D Plenoptic Function (McMillan)
IMPROVING ADAS VALIDATION WITH MBT
Sophia Antipolis, French Riviera 20-22 October 2015 IMPROVING ADAS VALIDATION WITH MBT Presented by Laurent RAFFAELLI ALL4TEC laurent.raffaelli@all4tec.net AGENDA What is an ADAS? ADAS Validation Implementation
MOVEMENT DETECTOR WITH INBUILT CAMERA USER GUIDE
Easy to install wireless technology MOVEMENT DETECTOR WITH INBUILT CAMERA USER GUIDE www.my-serenity.ch What s in the Box? 1 x Wireless Movement Detector with inbuilt camera (also called PIRCAM or PIR
What is light? This question sparked a huge debate in physics.
Optics Sol PH. 11 What is light? This question sparked a huge debate in physics. Light is a Stream of Particles. Newton called corpuscles Colors travel at different speeds. Einstein called quanti Light
L 32 Light and Optics [3]
![L 32 Light and Optics [3]](/thumbs/78/77867745.jpg "L 32 Light and Optics [3]")
L 32 Light and Optics [3] Measurements of the speed of light The bending of light refraction Total internal reflection Dispersion Dispersion Rainbows Atmospheric scattering Blue sky red sunsets Light and
D3.1. System specification. Dissemination level CO Version 1.0 Lead contractor ADS Due date Version date
D3.1 System specification Dissemination level CO Version 1.0 Lead contractor ADS Due date 28.02.2017 Version date 01.07.2017 Co-funded by the European Union Document information AUTHORS Anders Cassel -
The Question. What are the 4 types of interactions that waves can have when they encounter an object?
The Question What are the 4 types of interactions that waves can have when they encounter an object? Waves, Wave fronts and Rays Wave Front: Crests of the waves. Rays: Lines that are perpendicular to the
New Sony DepthSense TM ToF Technology
ADVANCED MATERIAL HANDLING WITH New Sony DepthSense TM ToF Technology Jenson Chang Product Marketing November 7, 2018 1 3D SENSING APPLICATIONS Pick and Place Drones Collision Detection People Counting
Techniques for the Application of Radar in Off- Road Robotics
Techniques for the Application of Radar in Off- Road Robotics M. Sc. Paul Fritsche http://www.rts.uni-hannover.de Page 1 Motivation Why we want to use RADAR in Field Robotics? + - Immune against rain,
Technical Bulletin Global Vehicle Target Specification Version 1.0 May 2018 TB 025
Technical Bulletin Global Vehicle Target Specification Version 1.0 May 2018 TB 025 Title Global Vehicle Target Specification Version 1.0 Document Number TB025 Author Euro NCAP Secretariat Date May 2018
VIDEO FOR VIRTUAL REALITY LIGHT FIELD BASICS JAMES TOMPKIN
VIDEO FOR VIRTUAL REALITY LIGHT FIELD BASICS JAMES TOMPKIN WHAT IS A LIGHT FIELD? Light field seems to have turned into a catch-all term for many advanced camera/display technologies. WHAT IS A LIGHT FIELD?
Recovering temporal PSF using ToF camera with delayed light emission
Kitano et al. IPSJ Transactions on Computer Vision and Applications (2017) 9:15 DOI 10.1186/s41074-017-0026-3 IPSJ Transactions on Computer Vision and Applications EXPRESS PAPER Open Access Recovering
10.5 Polarization of Light
10.5 Polarization of Light Electromagnetic waves have electric and magnetic fields that are perpendicular to each other and to the direction of propagation. These fields can take many different directions
Real-time Generation and Presentation of View-dependent Binocular Stereo Images Using a Sequence of Omnidirectional Images
Real-time Generation and Presentation of View-dependent Binocular Stereo Images Using a Sequence of Omnidirectional Images Abstract This paper presents a new method to generate and present arbitrarily
Modeling Light. On Simulating the Visual Experience
Modeling Light 15-463: Rendering and Image Processing Alexei Efros On Simulating the Visual Experience Just feed the eyes the right data No one will know the difference! Philosophy: Ancient question: Does
Coding and Modulation in Cameras
Mitsubishi Electric Research Laboratories Raskar 2007 Coding and Modulation in Cameras Ramesh Raskar with Ashok Veeraraghavan, Amit Agrawal, Jack Tumblin, Ankit Mohan Mitsubishi Electric Research Labs
Colorado School of Mines. Computer Vision. Professor William Hoff Dept of Electrical Engineering &Computer Science.
Professor William Hoff Dept of Electrical Engineering &Computer Science http://inside.mines.edu/~whoff/ 1 Introduction to 2 What is? A process that produces from images of the external world a description
Pattern Recognition for Autonomous. Pattern Recognition for Autonomous. Driving. Freie Universität t Berlin. Raul Rojas
Pattern Recognition for Autonomous Pattern Recognition for Autonomous Driving Raul Rojas Freie Universität t Berlin FU Berlin Berlin 3d model from Berlin Partner Freie Universitaet Berlin Outline of the
Simulation: A Must for Autonomous Driving
Simulation: A Must for Autonomous Driving NVIDIA GTC 2018 (SILICON VALLEY) / Talk ID: S8859 Rohit Ramanna Business Development Manager Smart Virtual Prototyping, ESI North America Rodolphe Tchalekian EMEA
Physics 11 - Waves Extra Practice Questions
Physics - Waves xtra Practice Questions. Wave motion in a medium transfers ) energy, only ) mass, only. both mass and energy. neither mass nor energy. single vibratory disturbance that moves from point
Lenses & Exposure. Lenses. Exposure. Lens Options Depth of Field Lens Speed Telephotos Wide Angles. Light Control Aperture Shutter ISO Reciprocity
Lenses & Exposure Lenses Lens Options Depth of Field Lens Speed Telephotos Wide Angles Exposure Light Control Aperture Shutter ISO Reciprocity The Viewfinder Camera viewfinder Image Sensor shutter lens
Advanced Driver Assistance: Modular Image Sensor Concept
Vision Advanced Driver Assistance: Modular Image Sensor Concept Supplying value. Integrated Passive and Active Safety Systems Active Safety Passive Safety Scope Reduction of accident probability Get ready
Vision based autonomous driving - A survey of recent methods. -Tejus Gupta
Vision based autonomous driving - A survey of recent methods -Tejus Gupta Presently, there are three major paradigms for vision based autonomous driving: Directly map input image to driving action using
arxiv: v1 [cs.cv] 3 Nov 2016
![arxiv: v1 [cs.cv] 3 Nov 2016](/thumbs/86/93942780.jpg "arxiv: v1 [cs.cv] 3 Nov 2016")
Recent Advances in Transient Imaging: A Computer Graphics and Vision Perspective arxiv:1611.00939v1 [cs.cv] 3 Nov 2016 Adrian Jarabo Belen Masia Julio Marco Universidad de Zaragoza, I3A Diego Gutierrez
Vehicle Detection Using Gabor Filter
Vehicle Detection Using Gabor Filter B.Sahayapriya 1, S.Sivakumar 2 Electronics and Communication engineering, SSIET, Coimbatore, Tamilnadu, India 1, 2 ABSTACT -On road vehicle detection is the main problem
Applying AI to Mapping
Applying AI to Mapping Dr. Ryan Wolcott Manager, Simultaneous Localization and Mapping (SLAM) 1 TRI: Who We Are Our mission is to improve the quality of human life through advances in artificial intelligence,
Time-of-flight basics
Contents 1. Introduction... 2 2. Glossary of Terms... 3 3. Recovering phase from cross-correlation... 4 4. Time-of-flight operating principle: the lock-in amplifier... 6 5. The time-of-flight sensor pixel...
CS4495/6495 Introduction to Computer Vision
CS4495/6495 Introduction to Computer Vision 9C-L1 3D perception Some slides by Kelsey Hawkins Motivation Why do animals, people & robots need vision? To detect and recognize objects/landmarks Is that a
Pedestrian Detection with Radar and Computer Vision
Pedestrian Detection with Radar and Computer Vision camera radar sensor Stefan Milch, Marc Behrens, Darmstadt, September 25 25 / 26, 2001 Pedestrian accidents and protection systems Impact zone: 10% opposite
Lecture 24: TUE 20 APR 2010 Ch : E&M Waves
Physics 2102 Jonathan Dowling Lecture 24: TUE 20 APR 2010 Ch.33.6 10: E&M Waves Radiation Pressure Waves not only carry energy but also momentum. The effect is very small (we don t ordinarily feel pressure
9/14/2011. Contents. Lecture 3: Spatial Data Acquisition in GIS. Dr. Bo Wu Learning Outcomes. Data Input Stream in GIS
Contents Lecture 3: Spatial Data Acquisition in GIS Dr. Bo Wu lsbowu@polyu.edu.hk 1. Learning outcomes. Data acquisition: Manual digitization 3. Data acquisition: Field survey 4. Data acquisition: Remote
Integrated Radar-Camera Sensors: The Next Generation of Sensor Fusion. William A. Bauson Delphi Electronics and Safety Jan.
Integrated RadarCamera Sensors: The Next Generation of Sensor Fusion William A. Bauson Delphi Electronics and Safety Jan. 27, 2010 Active Safety Sensor Functions Detect objects There s something out there
Physical or wave optics
Physical or wave optics In the last chapter, we have been studying geometric optics u light moves in straight lines u can summarize everything by indicating direction of light using a ray u light behaves
Depth Camera for Mobile Devices
Depth Camera for Mobile Devices Instructor - Simon Lucey 16-423 - Designing Computer Vision Apps Today Stereo Cameras Structured Light Cameras Time of Flight (ToF) Camera Inferring 3D Points Given we have
TomTom Innovation. Hans Aerts VP Software Development Business Unit Automotive November 2015
TomTom Innovation Hans Aerts VP Software Development Business Unit Automotive November 2015 Empower Movement Simplify complex technology From A to BE Innovative solutions Maps Consumer Connect people and
Low Cost Infra Red 'X-ray' Imaging a Useful Research Tool for Luthiers? - Part 1.
FoMRHI Comm. 1991 John Downing Low Cost Infra Red 'X-ray' Imaging a Useful Research Tool for Luthiers? - Part 1. This Comm reports on an investigation, using a modified low cost digital camera, to determine
VibroFlex Compact Compactness meets versatility Preliminary datasheet. VibroFlex Compact
VibroFlex Compact The Polytec VibroFlex laser Doppler vibrometer is a modular high-performance solution for non-contact vibration mea sure ment. It offers unrivalled measurement performance and versatility
A Review of Image- based Rendering Techniques Nisha 1, Vijaya Goel 2 1 Department of computer science, University of Delhi, Delhi, India
A Review of Image- based Rendering Techniques Nisha 1, Vijaya Goel 2 1 Department of computer science, University of Delhi, Delhi, India Keshav Mahavidyalaya, University of Delhi, Delhi, India Abstract
Modeling Light. Slides from Alexei A. Efros and others
Project 3 Results http://www.cs.brown.edu/courses/cs129/results/proj3/jcmace/ http://www.cs.brown.edu/courses/cs129/results/proj3/damoreno/ http://www.cs.brown.edu/courses/cs129/results/proj3/taox/ Stereo