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Results for Self-Driving Vehicles

Robust Dense Mapping for Large-Scale Dynamic Environments

I. Bârsan, P. Liu, M. Pollefeys, A. Geiger
We present a stereo-based dense mapping algorithm for large-scale dynamic urban environments. In contrast to other existing methods, we simultaneously reconstruct the static background, the moving objects, and the potentially moving but currently stationary objects separately, which is desirable for high-level mobile robotic tasks such as path planning in crowded environments. […] [PDF]
Video: [LINK]
Project Page: [LINK]
International Conference on Robotics and Automation (ICRA), 2018

DeepRoadMapper: Extracting Road Topology From Aerial Images

G. Máttyus, W. Luo, R. Urtasun
Creating road maps is essential for applications such as autonomous driving and city planning. Most approaches in industry focus on leveraging expensive sensors mounted on top of a fleet of cars. This results in very accurate estimates when exploiting a user in the loop. […] [PDF]
International Conference on Computer Vision (ICCV), 2017

SGN: Sequential Grouping Networks for Instance Segmentation

S. Liu, J. Jia, S. Fidler, R. Urtasun
In this paper, we propose Sequential Grouping Networks (SGN) to tackle the problem of object instance segmentation. SGNs employ a sequence of neural networks, each solving a sub-grouping problem of increasing semantic complexity in order to gradually compose objects out of pixels. […] [PDF]
International Conference on Computer Vision (ICCV), 2017

End-To-End Instance Segmentation With Recurrent Attention

M. Ren, R. Zemel
While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene. Instance segmentation is very important in a variety of applications, such as autonomous driving, image captioning, and visual question answering. […] [PDF]
Supplementary Materials: [LINK]
Code: [LINK]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017

Annotating Object Instances with a Polygon-RNN

L. Castrejón, K. Kundu, R. Urtasun, S. Fidler
We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017

TorontoCity: Seeing the World With a Million Eyes

S. Wang; M. Bai; G. Mattyus; H. Chu; W. Luo; B. Yang; J. Liang; J. Cheverie; R. Urtasun; D. Lin.
Despite the substantial progress in recent years, the image captioning techniques are still far from being perfect. Sentences produced by existing methods, e.g. those based on RNNs, are often overly rigid and lacking in variability. […] [PDF]
International Conference on Computer Vision (ICCV), 2017

Deep Watershed Transform for Instance Segmentation

M. Bai, R. Urtasun
Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes. In our paper, we present a simple yet powerful end-to-end convolutional neural network to tackle this task. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017

Forecasting Interactive Dynamics of Pedestrians with Fictitious Play

W. Ma, D. Huang, N. Lee, K. Kitani
We develop predictive models of pedestrian dynamics by encoding the coupled nature of multi-pedestrian interaction using game theory, and deep learning-based visual analysis to estimate person-specific behavior parameters. Building predictive models for multi-pedestrian interactions however, is very challenging due to two reasons […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017

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