Multi-View Stereo : A Parametric Study Number of images, their angular separation and the like.

Slides:

Advertisements

Similar presentations

Assignment 4 Cloning Yourself

Advertisements

For Internal Use Only. © CT T IN EM. All rights reserved. 3D Reconstruction Using Aerial Images A Dense Structure from Motion pipeline Ramakrishna Vedantam.

Recent work in image-based rendering from unstructured image collections and remaining challenges Sudipta N. Sinha Microsoft Research, Redmond, USA.

Computer vision: models, learning and inference

3D Face Modeling Michaël De Smet.

A Global Linear Method for Camera Pose Registration

Instructor: Mircea Nicolescu Lecture 13 CS 485 / 685 Computer Vision.

December 5, 2013Computer Vision Lecture 20: Hidden Markov Models/Depth 1 Stereo Vision Due to the limited resolution of images, increasing the baseline.

Reconstructing Building Interiors from Images Yasutaka Furukawa Brian Curless Steven M. Seitz University of Washington, Seattle, USA 2011/01/16 蔡禹婷.

Structure from motion.

Constructing immersive virtual space for HAI with photos Shingo Mori Yoshimasa Ohmoto Toyoaki Nishida Graduate School of Informatics Kyoto University GrC2011.

A new approach for modeling and rendering existing architectural scenes from a sparse set of still photographs Combines both geometry-based and image.

Computer Vision : CISC 4/689

3-D Depth Reconstruction from a Single Still Image 何開暘

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

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

Introduction to Computer Vision 3D Vision Topic 9 Stereo Vision (I) CMPSCI 591A/691A CMPSCI 570/670.

The plan for today Camera matrix

A Novel 2D To 3D Image Technique Based On Object- Oriented Conversion.

Quan Yu State Key Lab of CAD&CG Zhejiang University

Constructing immersive virtual space for HAI with photos Shingo Mori Yoshimasa Ohmoto Toyoaki Nishida Graduate School of Informatics Kyoto University GrC2011.

Camera Parameters and Calibration. Camera parameters From last time….

Accurate, Dense and Robust Multi-View Stereopsis Yasutaka Furukawa and Jean Ponce Presented by Rahul Garg and Ryan Kaminsky.

Manhattan-world Stereo Y. Furukawa, B. Curless, S. M. Seitz, and R. Szeliski 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.

Multi-view geometry. Multi-view geometry problems Structure: Given projections of the same 3D point in two or more images, compute the 3D coordinates.

Automatic Camera Calibration

Computer vision: models, learning and inference

Lecture 11 Stereo Reconstruction I Lecture 11 Stereo Reconstruction I Mata kuliah: T Computer Vision Tahun: 2010.

Final Exam Review CS485/685 Computer Vision Prof. Bebis.

1/20 Obtaining Shape from Scanning Electron Microscope Using Hopfield Neural Network Yuji Iwahori 1, Haruki Kawanaka 1, Shinji Fukui 2 and Kenji Funahashi.

Multi-view geometry.

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

Xiaoguang Han Department of Computer Science Probation talk – D Human Reconstruction from Sparse Uncalibrated Views.

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

Advanced Computer Technology II FTV and 3DV KyungHee Univ. Master Course Kim Kyung Yong 10/10/2015.

December 4, 2014Computer Vision Lecture 22: Depth 1 Stereo Vision Comparing the similar triangles PMC l and p l LC l, we get: Similarly, for PNC r and.

Ray Divergence-Based Bundle Adjustment Conditioning for Multi-View Stereo Mauricio Hess-Flores 1, Daniel Knoblauch 2, Mark A. Duchaineau 3, Kenneth I.

Presented by Matthew Cook INFO410 & INFO350 S INFORMATION SCIENCE Paper Discussion: Dynamic 3D Avatar Creation from Hand-held Video Input Paper Discussion:

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

Peripheral drift illusion. Multiple views Hartley and Zisserman Lowe stereo vision structure from motion optical flow.

Vincent DeVito Computer Systems Lab The goal of my project is to take an image input, artificially blur it using a known blur kernel, then.

Raquel A. Romano 1 Scientific Computing Seminar May 12, 2004 Projective Geometry for Computer Vision Projective Geometry for Computer Vision Raquel A.

CSE 140: Computer Vision Camillo J. Taylor Assistant Professor CIS Dept, UPenn.

55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.

COS429 Computer Vision =++ Assignment 4 Cloning Yourself.

55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.

High Resolution Surface Reconstruction from Overlapping Multiple-Views

Lecture 9 Feature Extraction and Motion Estimation Slides by: Michael Black Clark F. Olson Jean Ponce.

1Ellen L. Walker 3D Vision Why? The world is 3D Not all useful information is readily available in 2D Why so hard? “Inverse problem”: one image = many.

Advanced Computer Vision Chapter 11 Stereo Correspondence Presented by: 蘇唯誠指導教授 : 傅楸善博士.

Image-Based Rendering Geometry and light interaction may be difficult and expensive to model –Think of how hard radiosity is –Imagine the complexity of.

DISCRIMINATIVELY TRAINED DENSE SURFACE NORMAL ESTIMATION ANDREW SHARP.

Outline ● Introduction – What is the problem ● Generate stochastic textures ● Improve realism ● High level approach - Don't just jump into details – Why.

Problem Set 2 Reconstructing a Simpler World COS429 Computer Vision Due October (one week from today)13 th.

MASKS © 2004 Invitation to 3D vision. MASKS © 2004 Invitation to 3D vision Lecture 1 Overview and Introduction.

Center for Machine Perception Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Segmentation Based Multi-View.

11/25/03 3D Model Acquisition by Tracking 2D Wireframes Presenter: Jing Han Shiau M. Brown, T. Drummond and R. Cipolla Department of Engineering University.

A Globally Optimal Algorithm for Robust TV-L 1 Range Image Integration Christopher Zach VRVis Research Center Thomas Pock, Horst Bischof.

Main research interest

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

Semi-Global Matching with self-adjusting penalties

Answering ‘Where am I?’ by Nonlinear Least Squares

Pre-Production Determine the overall purpose of the project.

Common Classification Tasks

Part One: Acquisition of 3-D Data 2019/1/2 3DVIP-01.

Course 6 Stereo.

Chapter 11: Stereopsis Stereopsis: Fusing the pictures taken by two cameras and exploiting the difference (or disparity) between them to obtain the depth.

Revision 4 CSMSC5711 Revision 4: CSMC5711 v.9a.

Presentation transcript:

Multi-View Stereo : A Parametric Study Number of images, their angular separation and the like.

What is Multi-View Stereo.  Take multiple 2D (i.e. ordinary) images from different views and convert them into 3D models.  3D Surface is generated from point clouds extracted from 2D images by pairing 2 different images. (i.e. stereo)  Useful for  Preservation and reconstruction of archeological sites, sculptures.  Speeding up creation of 3D face models for CG Animation purposes.  Making better player generated in-game avatars, quicker.

Main objectives.  Attempt to generalize, i.e. parameterize, the input requirement by analysis.  Mainly considering a time sensitive input acquisition phase.  Time available for taking source pictures is presumed to be limited.  Post processing and 3D Structure generation has fewer constraints.  Parameters of most importance :  Number of images.  Angular Separation between two consecutive images.

The Process 1. Input acquisition : 1. Any camera that produces standard format digital still images. 2. Sparse Point Cloud : ( Bundler, Snavely ) 1. Using Bundler, a Structure from Motion based tool, generate a sparse point cloud. 3. Dense Point Cloud – Pre-processing (CMVS, Furukawa) 1. Applying Clustering methods on large number of images. 4. Dense Point Cloud – (PMVS, Furukawa) 1. From the output cluster, get the final point cloud. 5. Poisson reconstruction – (Meshlab) 1. From points generate 3D Model.

What is Bundler?  Main purpose is to generate Structure from Motion.  Structure from Motion.  Camera Co-ordinates are figured out by using multiple images assumed to be taken in a sequential order.  Most prominent features are extracted and given normal directions. (point clouds)  Additional usages  Ease of inputting Camera Parameters.  Processes images and provides a standard output usable by CMVS/PMVS.  Sparse Cloud can be used for time-sensitive preview.

CMVS and PMVS  Multi-View Stereo :  Assume stereo vision and pair two images. (i.e. assume they’re taken at the same time)  Analyze depth and direction from these views.  After computing on numerous pairs, estimate a 3D model.  Clustering MVS :  Useful largely in cases when number of images is large.  Speeds up the process for regular number of images.  Patch-based MVS :  Creates Dense point cloud from input data.  Pre-processing reduced when paired with Bundler.

Progress Report.

Progress Report (cont.)  Able to generate average results from 10 images at 5 degree separation.  Lighting conditions and other parameters to be tested.  Dataset Used :  Images taken by self.  Plan to use 3D Photography Dataset  html html

Extended Goals.  Try to put harder constraints on 3D point cloud generation.  Derive a parametric formula based on Vehicular motion, i.e.  “What should be the time gap between images”  “What should be the angular motion of vehicle around the object of interest”  “If path known and fixed, what should be the optimal timing”  Try different algorithms cited in Middlebury 

Thank You for Listening.  Any Questions?