Joint Optimization for Object Class Segmentation and Dense Stereo Reconstruction
International Journal of Computer Vision
Extracting 3d scene-consistent object proposals and depth from stereo images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Multiple view object cosegmentation using appearance and stereo cues
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Tree structural watershed for stereo matching
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
A game-theoretical approach to image segmentation
CVM'12 Proceedings of the First international conference on Computational Visual Media
Occlusion filling in stereo: Theory and experiments
Computer Vision and Image Understanding
Scene reconstruction from high spatio-angular resolution light fields
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Cross image inference scheme for stereo matching
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Depth synthesis and local warps for plausible image-based navigation
ACM Transactions on Graphics (TOG)
Consistent depth maps recovery of video via object segmentation
ACM SIGGRAPH 2013 Posters
Stereo matching by using the global edge constraint
Neurocomputing
Technical Section: High-resolution depth for binocular image-based modeling
Computers and Graphics
Depth manipulation using disparity histogram analysis for stereoscopic 3D
The Visual Computer: International Journal of Computer Graphics
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This paper presents a method for joint stereo matching and object segmentation. In our approach a 3D scene is represented as a collection of visually distinct and spatially coherent objects. Each object is characterized by three different aspects: a color model, a 3D plane that approximates the object's disparity distribution, and a novel 3D connectivity property. Inspired by Markov Random Field models of image segmentation, we employ object-level color models as a soft constraint, which can aid depth estimation in powerful ways. In particular, our method is able to recover the depth of regions that are fully occluded in one input view, which to our knowledge is new for stereo matching. Our model is formulated as an energy function that is optimized via fusion moves. We show high-quality disparity and object segmentation results on challenging image pairs as well as standard benchmarks. We believe our work not only demonstrates a novel synergy between the areas of image segmentation and stereo matching, but may also inspire new work in the domain of automatic and interactive object-level scene manipulation.