Wenjie Luo
University of Toronto
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Publication
Featured researches published by Wenjie Luo.
computer vision and pattern recognition | 2016
Wenjie Luo; Alexander G. Schwing; Raquel Urtasun
In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation. However, current architectures rely on siamese networks which exploit concatenation followed by further processing layers, requiring a minute of GPU computation per image pair. In contrast, in this paper we propose a matching network which is able to produce very accurate results in less than a second of GPU computation. Towards this goal, we exploit a product layer which simply computes the inner product between the two representations of a siamese architecture. We train our network by treating the problem as multi-class classification, where the classes are all possible disparities. This allows us to get calibrated scores, which result in much better matching performance when compared to existing approaches.
european conference on computer vision | 2016
Min Bai; Wenjie Luo; Kaustav Kundu; Raquel Urtasun
We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving. We build on the observation that the scene is typically composed of a static background, as well as a relatively small number of traffic participants which move rigidly in 3D. We propose to estimate the traffic participants using instance-level segmentation. For each traffic participant, we use the epipolar constraints that govern each independent motion for faster and more accurate estimation. Our second contribution is a new convolutional net that learns to perform flow matching, and is able to estimate the uncertainty of its matches. This is a core element of our flow estimation pipeline. We demonstrate the effectiveness of our approach in the challenging KITTI 2015 flow benchmark, and show that our approach outperforms published approaches by a large margin.
Remote Sensing | 2017
Nina Merkle; Wenjie Luo; Stefan Auer; Rupert Müller; Raquel Urtasun
Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like monitoring by image time series or scene analysis after sudden events. These tasks require geo-referenced and precisely co-registered multi-sensor data. Images captured by the high resolution synthetic aperture radar (SAR) satellite TerraSAR-X exhibit an absolute geo-location accuracy within a few decimeters. These images represent therefore a reliable source to improve the geo-location accuracy of optical images, which is in the order of tens of meters. In this paper, a deep learning-based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data is investigated. Image registration between SAR and optical images requires few, but accurate and reliable matching points. These are derived from a Siamese neural network. The network is trained using TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe, in order to learn the two-dimensional spatial shifts between optical and SAR image patches. Results confirm that accurate and reliable matching points can be generated with higher matching accuracy and precision with respect to state-of-the-art approaches.
neural information processing systems | 2016
Wenjie Luo; Yujia Li; Raquel Urtasun; Richard S. Zemel
international conference on computer vision | 2017
Shenlong Wang; Min Bai; Gellert Mattyus; Hang Chu; Wenjie Luo; Bin Yang; Justin Liang; Joel Cheverie; Sanja Fidler; Raquel Urtasun
arXiv: Computation and Language | 2017
Wenyuan Zeng; Wenjie Luo; Sanja Fidler; Raquel Urtasun
neural information processing systems | 2013
Wenjie Luo; Alexander G. Schwing; Raquel Urtasun
international conference on computer vision | 2017
Gellert Mattyus; Wenjie Luo; Raquel Urtasun
computer vision and pattern recognition | 2018
Wenjie Luo; Bin Yang; Raquel Urtasun
computer vision and pattern recognition | 2018
Bin Yang; Wenjie Luo; Raquel Urtasun