Sponsored by


The 2016 edition of the Computer Vision for Road Scene Understanding and Autonomous Driving will be held in conjunction with ECCV 2016.

Invited Speakers


NVIDIA sponsored the best paper award with a TitanX board to:


8:30 – 9:30 KeyNote
  Andreas Geiger, (MPI, Germany)

Towards Holistic 3D Scene Understanding for Autonomous Driving.

9:30 – 10:00 Oral Session 1
Position Interpolation using Feature Point Scale for Decimeter Visual Localization

David Wong,Daisuke Deguchi, Ichiro Ide, Hiroshi Murase (Nagoya Univ.).

Direct Visual Localisation and Calibration for Road Vehicles in Changing City Environments

Geoffrey Pascoe (Univ. of Oxford), William Maddern ( Univ. of Oxford), Paul Newman.

The Statistics of Driving Sequences - and what we can learn from them.

Henry Bradler (Goethe Univ. Frankfurt), Birthe Wiegand, Rudolf Mester.

10:00 – 10:30 Coffee Break
10:30 – 11:30 KeyNote
  Antonio M Lopez, (Computer Vision Center, Spain)

Learning to See in Virtual Worlds

11:30 – 12:10 Oral Session 2
Latent Hierarchical Part Based Models for Road Scene Understanding.

Suhas Kashetty Venkateshkumar (Continental), Muralikrishna Sridhar (Continental), Patrick Ott (School of Computing, Univ. of Leeds).

Semantic Mapping of Large-Scale Outdoor Scenes for Autonomous Off-Road Driving.

Fernando Bernuy (AMTC Universidad de Chile), Javier Ruiz del Solar (AMTC Univ. de Chile).

Sequential Score Adaptation with Extreme Value Theory for Robust Railway Track Inspection.

Xavier Gibert (Univ. of Maryland), Vishal Patel (Rutgers Univ.), Rama Chellappa (Univ. of Maryland).

Goal-Directed Pedestrian Prediction.

Eike Rehder (Inst. f. Meas.- and Control S.), Horst Kloeden (BMW Forschung und Technik GmbH).

12:10 – 14:00 Lunch Break
14:00 – 15:00 KeyNote
  Rudolf Mester, (Visual Sensorics and Information Processing, Goethe University, Germany)

Towards visual surround sensing: challenges, test data, and emerging methods

15:00 – 15:30 Coffee Break
15:30 – 16:30 Ashesh Jain, (Department of Computer Science at Cornell University, US)

Deep Learning for Sensor Rich Spatio-Temporal Problems: On Cars, Humans, and Robots

16:30 – -- Poster session

Topics of Interest

Analyzing road scenes using cameras could have a crucial impact in many domains, such as autonomous driving, advanced driver assistance systems (ADAS), personal navigation, mapping of large scale environments, and road maintenance. For instance, vehicle infrastructure, signage, and rules of the road have been designed to be interpreted fully by visual inspection. As the field of computer vision becomes increasingly mature, practical solutions to many of these tasks are now within reach. Nonetheless, there still seems to exist a wide gap between what is needed by the automotive industry and what is currently possible using computer vision techniques. The goal of this workshop is to allow researchers in the fields of road scene understanding and autonomous driving to present their progress and discuss novel ideas that will shape the future of this area. In particular, we would like this workshop to bridge the large gap between the community that develops novel theoretical approaches for road scene understanding and the community that builds working real-life systems performing in real-world conditions. To this end, we encourage submissions of original and unpublished work in the area of vision-based road scene understanding. The topics of interest include (but are not limited to):

We encourage researchers to submit not only theoretical contributions, but also work more focused on applications. Each paper will receive 3 double blind reviews, which will be moderated by the workshop chairs.

Important Dates

Organizing Committee

Program Committee