Large-scale 3D Modeling from Crowdsourced Data
Large-scale image-based 3D modeling has been a major goal of computer vision, enabling a wide range of applications including virtual reality, image-based localization, and autonomous navigation. One of the most diverse data sources for modeling is Internet photo collections. In the last decade the computer vision community has made tremendous progress in large-scale structure from motion and multi-view stereo from Internet datasets. However, utilizing this wealth of information for 3D modeling remains a challenging problem due to the ever-increasing amount of image data. In a short period of time, research in large-scale modeling has progressed from modeling using several thousand images, to modeling from city-scale datasets of several million, and recently to reconstructing an Internet-scale dataset comprising 100 million images. This tutorial will present the main underlying technologies enabling these innovations.
Time: October 28, 2016 2:00pm-5:00pm
- Motivation and basic principles (PDF Slides)
- Building blocks of a 3D modeling pipeline
- Challenges and opportunities of using crowd-sourced imagery
- Large-scale visual data association (PDF Slides)
- Image registration
- Connected-component discovery
- Sparse modeling (PDF Slides)
- Structure-from-motion pipeline
- Challenges in Large-Scale Structure-from-motion
- Dense modeling (PDF Slides)
- Depth map estimation
- Depth map fusion and surface generation
jmf (at) cs.unc.edu
University of North Carolina at Chapel Hill
edunn (at) stevens.edu
Stevens Institute of Technology
jared.heinly (at) urcventures.com
Senior Research Engineer
Johannes L. Schönberger
jsch (at) inf.ethz.ch
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