Skip to footer
Home Research Artificial Intelligence / Machine Learning DARNet: Deep Active Ray Network for Building Segmentation

DARNet: Deep Active Ray Network for Building Segmentation


In this paper, we propose a Deep Active Ray Network (DARNet) for automatic building segmentation. Taking an image as input, it first exploits a deep convolutional neural network (CNN) as the backbone to predict energy maps, which are further utilized to construct an energy function. A polygon-based contour is then evolved via minimizing the energy function, of which the minimum defines the final segmentation. Instead of parameterizing the contour using Euclidean coordinates, we adopt polar coordinates, i.e., rays, which not only prevents self-intersection but also simplifies the design of the energy function. Moreover, we propose a loss function that directly encourages the contours to match building boundaries. Our DARNet is trained end-to-end by back-propagating through the energy minimization and the backbone CNN, which makes the CNN adapt to the dynamics of the contour evolution. Experiments on three building instance segmentation datasets demonstrate our DARNet achieves either state-of-the-art or comparable performances to other competitors.


Dominic Cheng, Renjie Liao, Sanja Fidler, Raquel Urtasun


CVPR 2019

Full Paper

‘DARNet_ Deep Active Ray Network for Building Segmentation’ (PDF)

Uber ATG

Previous article Building Together: When Research Went Viral at Uber
Next article Dimensionality Reduction for Representing the Knowledge of Probabilistic Models
Renjie Liao is a PhD student in Machine Learning Group, Department of Computer Science, University of Toronto, supervised by Prof. Raquel Urtasun and Prof. Richard Zemel. He is also a Research Scientist in Uber Advanced Technology Group Toronto. He is also affiliated with Vector Institute. He received M.Phil. degree from Department of Computer Science and Engineering, Chinese University of Hong Kong, under the supervision of Prof. Jiaya Jia. He got B.Eng. degree from School of Automation Science and Electrical Engineering in Beihang University (former Beijing University of Aeronautics and Astronautics).
Raquel Urtasun is the Chief Scientist for Uber ATG and the Head of Uber ATG Toronto. She is also a Professor at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision and a co-founder of the Vector Institute for AI. She is a recipient of an NSERC EWR Steacie Award, an NVIDIA Pioneers of AI Award, a Ministry of Education and Innovation Early Researcher Award, three Google Faculty Research Awards, an Amazon Faculty Research Award, a Connaught New Researcher Award, a Fallona Family Research Award and two Best Paper Runner up Prize awarded CVPR in 2013 and 2017. She was also named Chatelaine 2018 Woman of the year, and 2018 Toronto’s top influencers by Adweek magazine