Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score
International Journal of Computer Vision
Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detailed Real-Time Urban 3D Reconstruction from Video
International Journal of Computer Vision
Using Multiple Hypotheses to Improve Depth-Maps for Multi-View Stereo
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
Fast low-memory streaming MLS reconstruction of point-sampled surfaces
Proceedings of Graphics Interface 2009
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate, Dense, and Robust Multiview Stereopsis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building Rome on a cloudless day
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Volumetric stereo and silhouette fusion for image-based modeling
The Visual Computer: International Journal of Computer Graphics
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Fusion of depth maps with multiple scales
Proceedings of the 2011 SIGGRAPH Asia Conference
Carved visual hulls for image-based modeling
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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We present a Multi View Stereo approach for huge unstructured image datasets that can deal with large variations in surface sampling rate of single images. Our method reconstructs surface parts always in the best available resolution. It considers scaling not only for large scale differences, but also between arbitrary small ones for a weighted merging of the best partial reconstructions. We create depth maps with our GPU based depth map algorithm, that also performs normal optimization. It matches several images that are found with a heuristic image selection method, to a reference image. We remove outliers by comparing depth maps against each other with a fast but reliable GPU approach. Then, we merge the different reconstructions from depth maps in 3D space by selecting the best points and optimizing them with not selected points. Finally, we create the surface by using a Delaunay graph cut.