Photo Tourism: Exploring Photo Collections in 3D
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1 Photo Tourism: Exploring Photo Collections in 3D Noah Snavely Steven M. Seitz University of Washington Richard Szeliski Microsoft Research 15,464 37,383 76, Noah Snavely 15,464 37,383 76,389 Reproduced with permission of Yahoo! Inc by Yahoo! Inc. YAHOO! and the YAHOO! logo are trademarks of Yahoo! Inc.
2 Photo Tourism Photo Tourism overview Input photographs Scene reconstruction Relative camera positions and orientations Point cloud Sparse correspondence Photo Explorer Related work Image-based modeling Related work Image browsing Debevec, et al. SIGGRAPH 1996 Schaffalitzky and Zisserman ECCV 2002 Brown and Lowe 3DIM 2005 Image-based rendering Aspen Movie Map Lippman, et al., 1978 Photorealistic IBR: Levoy and Hanrahan, SIGGRAPH 1996 Gortler, et al, SIGGRAPH 1996 Seitz and Dyer, SIGGRAPH 1996 Aliaga, et al, SIGGRAPH 2001 and many others Toyama, et al, Int. Conf. Multimedia, 2003 McCurdy and Griswold Mobisys 2003 Sivic and Zisserman ICCV 2003
3 Photo Tourism overview Scene reconstruction Automatically estimate position, orientation, and focal length of cameras 3D positions of feature points Scene reconstruction Feature detection Input photographs Photo Explorer Pairwise feature matching Incremental structure from motion Correspondence estimation Feature detection Detect features using SIFT [Lowe, IJCV 2004] Feature detection Detect features using SIFT [Lowe, IJCV 2004]
4 Pairwise feature matching Match features between each pair of images Pairwise feature matching Refine matching using RANSAC [Fischler & Bolles 1987] to estimate fundamental matrices between pairs Feature detection Detect features using SIFT [Lowe, IJCV 2004] Feature detection Detect features using SIFT [Lowe, IJCV 2004]
5 Feature detection Detect features using SIFT [Lowe, IJCV 2004] Feature matching Match features between each pair of images Feature matching Refine matching using RANSAC [Fischler & Bolles 1987] to estimate fundamental matrices between pairs Correspondence estimation Link up pairwise matches to form connected components of matches across several images Image 1 Image 2 Image 3 Image 4
6 Structure from motion Incremental structure from motion p 1 p 4 p 3 p 2 minimize f(r,t,p) p 5 p 6 p 7 Camera 1 Camera 3 R 1,t 1 Camera 2 R 3,t 3 R 2,t 2 Incremental structure from motion Incremental structure from motion
7 Incremental structure from motion Incremental structure from motion Incremental structure from motion Reconstruction performance For photo sets from the Internet, 20% to 75% of the photos were registered Most unregistered photos belonged to different connected components Running time: < 1 hour for 80 photos > 1 week for 2600 photo
8 Photo Tourism overview Photo Explorer Input photographs Scene reconstruction Photo Explorer Demo Photo Tourism overview Input photographs Scene reconstruction Photo Explorer Navigation Rendering Annotations
9 Navigation controls Object-based based browsing Free-flight navigation Object-based browsing Relation-based browsing Overhead map Object-based based browsing Relation-based browsing Visibility Resolution Head-on view Find all similar images Find all details Find all zoom outs Zoom in Move left Move right Zoom out
10 Relation-based browsing Relation-based browsing is to the left of Image B is to the left of Image B is detail of is detail of Image A is a zoom out of Image C Image A is a zoom out of Image C Image D Image D Relation-based browsing Relation-based browsing Image A Image A Image B Image B
11 Relation-based browsing Relation-based browsing to the right of to the right of Image A Image B Image A Image B Image C Image C Relation-based browsing Relation-based browsing to the right of to the right of Image A Image B Image A Image B is detail of is detail of Image C Image C Image D Image D
12 Relation-based browsing Overhead map to the left of to the right of Image A Image B is zoom-out of is detail of is detail of is detail of is zoom-out of is zoom-out of Image C is detail of Image D Prague Old Town Square Prague Rome
13 Photo Tourism overview Rendering Input photographs Scene reconstruction Photo Explorer Navigation Rendering Annotations Rendering Rendering
14 Rendering transitions Rendering transitions Rendering transitions Rendering transitions Camera A Camera B
15 Photo Tourism overview Input photographs Scene reconstruction Photo Explorer Navigation Rendering Annotations Paris Rome Prague Annotations Annotations Reproduced with permission of Yahoo! Inc by Yahoo! Inc. YAHOO! and the YAHOO! logo are trademarks of Yahoo! Inc.
16 Annotations Paris Prague Yosemite Rome Yosemite Yosemite Topographic data courtesy USGS
17 Contributions Automated system for registering photo collections in 3D for interactive exploration Structure from motion algorithm demonstrated on hundreds of photos from the Internet Photo exploration system combining new imagebased rendering and photo navigation techniques Limitations / Future work Not all photos can be reliably matched Better feature detection / matching Integrating GPS & other localization info. Structure from motion scalability More efficient (sparse) algorithms Plane-based transitions lack parallax Limitations / Future work Limitations / Future work Not all photos can be reliably matched Better feature detection / matching Integrating GPS & other localization info. Structure from motion scalability More efficient (sparse) algorithms Plane-based transitions lack parallax Richer transitions Photo explorer scalability
18 Future work Photosynth Photo explorer scalability Design client-server architecture for streaming images and geometry at required resolution Scale to all of the world s photos (and videos ) Photosynth project at Microsoft Live Labs (live demo) Future work Photo explorer scalability Scale to all of the world s photos (and videos ) Computational complexity: avoid matching all images to all other images vocabulary trees [Nister] graphical models, nested dissection [Dellaert] serendipitous (?) probabilistic inference Acknowledgements National Science Foundation Achievement Rewards for College Scientists (ARCS) The many people who allowed use of their photos UW GRAIL Lab MSR Interactive Visual Media Lab Kevin Chiu and Andy Hou for writing the Java applet
19 Conclusion Indexing the world s photos in 3D provides a new way to share and experience our world To find out more: Saint Basil's Cathedral Trafalgar Square Rockefeller Center Mount Rushmore Statistics Dataset # input # registered Trevi Fountain Yosemite 325 1,893 Notre Dame 597 2,635 Prague Reconstruction running time Great Wall: 82 / 120 photos registered Running time: ~ 3 hours Notre Dame: 597 / 2,635 photos registered Running time: ~ 2 weeks Great Wall Trafalgar Square 278 1,893
20 Future work Visibility Incorporate other metadata (e.g., time, photographer) and media (e.g., panoramas, video) Enhanced morphing Scale up structure from motion algorithm Visibility Advantages of 3D over 2D 3D geometry has multi-image consistency Can annotate point cloud directly Can import annotations from georeferenced sources (e.g., landmark databases) Can use depth as cue for rejecting outliers in selection
21 Post-processing the reconstruction Compute gravity direction Center point cloud at the origin Scale model to unit variance
Noah Snavely Steven M. Seitz. Richard Szeliski. University of Washington. Microsoft Research. Modified from authors slides
Photo Tourism: Exploring Photo Collections in 3D Noah Snavely Steven M. Seitz University of Washington Richard Szeliski Microsoft Research 2006 2006 Noah Snavely Noah Snavely Modified from authors slides
Photo Tourism: Exploring Photo Collections in 3D
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