Program schedule announced. Papers will be presented on the 18th, while the challenge winners will present their work on the 22nd. In addition to the regular program, we will also host a reception on the 18th evening with a live music band. Stay tuned for the location.
The CVPR 2018 Workshop on Autonomous Driving (WAD) is the combined venue for The 9th international Workshop on Computer Vision in Vehicle Technology (CVVT) and perception challenges with newly collected and fine-annotated large scale datasets. It aims to get together researchers and engineers from academia and industries to discuss computer vision applications in autonomous driving. In this one and half day work, we will have regular paper presentations, invited speakers, panel discussions, and technical benchmark challenges to present the current state of the art, as well as the limitations and future directions for computer vision in autonomous driving, arguably the most promising application of computer vision and AI in general.
We invite the submission of original research contributions in computer vision addressed to:
· Autonomous navigation and exploration based on vision and 3D.
· Vision based driving assistance, driver monitoring and advanced interfaces.
· Vision systems for unmanned aerial vehicles.
· Deep Learning, machine learning, mathematical imaging and image analysis techniques in vehicle technology.
· Non-verbal and graphical information for remote-driver assistance of long-distance exploration.
· Performance evaluation without ground truth and reconstruction from one time measurements in natural environments.
· On-board calibration of multi-camera acquisition systems (stereo rig, multimodal, networks).
· Reconstruction without classical features such as planes, lines and linear objects and terrain generation from multi-view and omnidirectional camera networks.
· Large-scale computer vision and geo-localization for driving, navigation and exploration.
For details, please check WAD2018 Call for Papers
Submission details can be found at http://www.wad.ai/paper.html
We will host a challenge to understand the current status of computer vision algorithms in solving the environmental perception problems for autonomous driving. We have prepared a number of large scale datasets with fine annotation, collected and annotated by Berkeley Deep Driving Consortium or Baidu Inc. Based on the datasets, we have define a set of four realistic problems and encourage new algorithms and pipelines to be invented for autonomous driving. More specifically, they are
(1) Drivable Area Segmentation
(2) Road Object Detection
(3) Domain Adaptation of Semantic Segmentation