View interpolation for image synthesis
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling Dynamic Scenes by Registering Multi-View Image Sequences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
Unstructured video-based rendering: interactive exploration of casually captured videos
ACM SIGGRAPH 2010 papers
Accurate, Dense, and Robust Multiview Stereopsis
IEEE Transactions on Pattern Analysis and Machine Intelligence
High Resolution Image Correspondences for Video Post-Production
CVMP '10 Proceedings of the 2010 Conference on Visual Media Production
Making of Who Cares? HD Stereoscopic Free Viewpoint Video
CVMP '11 Proceedings of the 2011 Conference for Visual Media Production
KinectFusion: Real-time dense surface mapping and tracking
ISMAR '11 Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
DTAM: Dense tracking and mapping in real-time
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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High-quality dense image correspondence estimation between two images is an essential prerequisite for many tasks in visual media production, one prominent example being view interpolation. Due to the ill-posed nature of the correspondence estimation problem, errors occur frequently for a number of problematic conditions, among them occlusions, large displacements and low-textured regions. In this paper, we propose to use approximate depth data from low-resolution depth sensors or coarse geometric proxies to guide the high-resolution image correspondence estimation. We counteract the effect of uncertainty in the prior by exploiting the coarse-to-fine image pyramid used in our estimation algorithm. Our results show that even with only approximate priors, visual quality improves considerably compared to an unguided algorithm or a pure depth-based interpolation.