Exploiting the Power of Stereo Confidences

By David Pfeiffer, Stefan K. Gehrig, Nicolai Schneider. 2013

105 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, 3D Vision & Pose

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In 3D Vision & Pose

Citations in the corpus

The Cityscapes Dataset for Semantic Urban Scene Understanding (2016, background): “We deliberately did not record in adverse weather conditions, such as heavy rain or snow, as we believe such conditions to require specialized techniques and datasets [51].”
Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching (2014, background): “Pfeiffer et al. [19] integrated three conﬁdence measures into a mid-level representation for 3D reconstruction and showed that Bayesian reasoning outperforms sparsiﬁcation by thresholding.”
Ensemble Learning for Confidence Measures in Stereo Vision (2013, background): “Applications where accurate stereo confidence measures are essential in raising reliability of computer vision include sparse [19] or dense [16] 3D scene reconstructions.”
1 year, 1000 km: The Oxford RobotCar dataset (2017, background): “…vision-based autonomous driving datasets have been released (BlancoClaraco et al., 2014; Brostow et al., 2009; Dolláar et al., 2009; Pandey et al., 2011; Pfeiffer et al., 2013, and others), notably the KITTI dataset in Geiger et al. (2013) and the recent Cityscapes dataset in Cordts et al. (2016).”
The Cityscapes Dataset (2015, unknown): “such as used in [17].”
Deep hierarchical parsing for semantic segmentation (2015, background): “The resulting architectures provide promising improvements over the previous state-of-the-art on three semantic segmentation datasets: Stanford background [7], SIFT ﬂow [12] and Daimler urban [17].”