Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6937774 | Image and Vision Computing | 2017 | 36 Pages |
Abstract
In this paper, we summarize the ideas behind the model and generalize it to take into account multiple dense input streams: the image itself, stereo depth maps, and semantic class probability maps that can be generated, e.g., by deep convolutional neural networks. Our generalization is embedded into a novel mathematical formulation for the Stixel model. We further sketch how the free parameters of the model can be learned using structured SVMs.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Marius Cordts, Timo Rehfeld, Lukas Schneider, David Pfeiffer, Markus Enzweiler, Stefan Roth, Marc Pollefeys, Uwe Franke,