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Rome's Pantheon 3D Reconstruction by EveryPoint, using Crowd Sourced Images
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Rebuilding the World’s Most Beloved Landmarks in Six Days
New Process Constructs 12,903 3D Models from 100 Million Crowd-Sourced Photos
June 8 – June 10, 2015
EveryPoint has built a 3D reconstruction of the world’s landmarks using computer vision and 3D modeling techniques, in partnership with the UNC Department of Computer Science. Using Yahoo’s publicly available collection of 100 million crowd-sourced photos and photogrammetry, UNC and EveryPoint created a new software process able to rebuild 12,903 3D models after six days. A demonstration will be presented during the 2015 CVPR Computer Vision Conference in Boston, Massachusetts.
Jared Heinly, Johannes L. Schönberger, Enrique Dunn and Jan-Michael Frahm of UNC’s 3D Computer Vision Group have created software that processes world-scale datasets to create 3D models of locations all over the world. Unlike maps and aerial images, these models can be directly used for VR applications such as virtual tourism.
Previous projects have built 3D models of landmarks from entire cities based on datasets of up to a few million images, but reconstructing the 3D models of the landmarks of the entire world requires the ability to process orders of magnitudes more data.
The focus of the new framework is to enable processing on datasets of arbitrary size. It streams each image consecutively, assigning it to a cluster of related images. The streaming process provides for greater scalability by analyzing each image only once. The key of the new algorithm is to efficiently decide which images to remember and which to discard as their information is already represented.
UNC and EveryPoint's researchers applied the framework to Yahoo’s publicly available collection of 100 million crowd-sourced photos, containing images geographically distributed throughout the entire world. The program took 4.4 days to stream and cluster the entire 14-terabyte dataset on a single computer before building the 3D models of each of those sites. Stacked on top of each other, these photos would reach into the middle of the stratosphere of the earth (twice as high as airplane cruise altitude).
After the data association process is complete, sparse 3D models are built using the images. This process takes less than a day, bringing the total process to slightly more than five days.
EveryPoint specializes in solving industry challenges through real-time digitization of the physical world. URCV used the algorithm’s output to construct the 3D models via world-scale stereo modeling technology. Model results are based on EveryPoint's novel accuracy-driven view selection for precision scene reconstruction. To further improve the realism of the 3D scene models, a robust consensus-based depth map fusion is leveraged, along with an appearance correction. The world-scale stereo leverages a scalable, efficient multi-threaded implementation for faster modeling.
David Boardman, CEO of EveryPoint said, “Being able to process billions of images into actionable data will power new business solutions that transform industries. Reconstructing the world’s landmarks so quickly is only one example of social application, pointing the way towards many future possibilities.”
Boardman described industry applications: “For example, imagine imagery streaming in from UAV, planes, cell phones, truck mounted cameras, and hard hat cameras enabling the reconstruction of mining and construction operations at any point in time. Or imagine imagery from the millions of self-driving cars in the future being leveraged to create up-to-the-second street maps. Think of the lives that would be saved if First Responders could see an up-to-the-minute model of an emergency scene before arriving.”
EveryPoint was recently honored to have assisted Smithsonian and Chilean scientists with technical assistance in 2014, by rapid reconstructing massive image sets from a large fossil site of ancient marine mammal skeletons in the Atacama Desert of Northern Chile.
New Process Constructs 12,903 3D Models from 100 Million Crowd-Sourced Photos
June 8 – June 10, 2015
EveryPoint has built a 3D reconstruction of the world’s landmarks using computer vision and 3D modeling techniques, in partnership with the UNC Department of Computer Science. Using Yahoo’s publicly available collection of 100 million crowd-sourced photos and photogrammetry, UNC and EveryPoint created a new software process able to rebuild 12,903 3D models after six days. A demonstration will be presented during the 2015 CVPR Computer Vision Conference in Boston, Massachusetts.
Jared Heinly, Johannes L. Schönberger, Enrique Dunn and Jan-Michael Frahm of UNC’s 3D Computer Vision Group have created software that processes world-scale datasets to create 3D models of locations all over the world. Unlike maps and aerial images, these models can be directly used for VR applications such as virtual tourism.
Previous projects have built 3D models of landmarks from entire cities based on datasets of up to a few million images, but reconstructing the 3D models of the landmarks of the entire world requires the ability to process orders of magnitudes more data.
The focus of the new framework is to enable processing on datasets of arbitrary size. It streams each image consecutively, assigning it to a cluster of related images. The streaming process provides for greater scalability by analyzing each image only once. The key of the new algorithm is to efficiently decide which images to remember and which to discard as their information is already represented.
UNC and EveryPoint's researchers applied the framework to Yahoo’s publicly available collection of 100 million crowd-sourced photos, containing images geographically distributed throughout the entire world. The program took 4.4 days to stream and cluster the entire 14-terabyte dataset on a single computer before building the 3D models of each of those sites. Stacked on top of each other, these photos would reach into the middle of the stratosphere of the earth (twice as high as airplane cruise altitude).
After the data association process is complete, sparse 3D models are built using the images. This process takes less than a day, bringing the total process to slightly more than five days.
EveryPoint specializes in solving industry challenges through real-time digitization of the physical world. URCV used the algorithm’s output to construct the 3D models via world-scale stereo modeling technology. Model results are based on EveryPoint's novel accuracy-driven view selection for precision scene reconstruction. To further improve the realism of the 3D scene models, a robust consensus-based depth map fusion is leveraged, along with an appearance correction. The world-scale stereo leverages a scalable, efficient multi-threaded implementation for faster modeling.
David Boardman, CEO of EveryPoint said, “Being able to process billions of images into actionable data will power new business solutions that transform industries. Reconstructing the world’s landmarks so quickly is only one example of social application, pointing the way towards many future possibilities.”
Boardman described industry applications: “For example, imagine imagery streaming in from UAV, planes, cell phones, truck mounted cameras, and hard hat cameras enabling the reconstruction of mining and construction operations at any point in time. Or imagine imagery from the millions of self-driving cars in the future being leveraged to create up-to-the-second street maps. Think of the lives that would be saved if First Responders could see an up-to-the-minute model of an emergency scene before arriving.”
EveryPoint was recently honored to have assisted Smithsonian and Chilean scientists with technical assistance in 2014, by rapid reconstructing massive image sets from a large fossil site of ancient marine mammal skeletons in the Atacama Desert of Northern Chile.
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