1st Workshop on Scene Understanding
in Unstructured Environments

In conjunction with GCPR 2021, September 28 - October 1, 2021

Important Dates

[Update June 14, 2021] Please notice that we have extended the extended abstract submission deadline until July 18, 2021 (23:59 CEST).

    Extended Abstract Submission Deadline: June 17, 2021 July 18, 2021 (23:59 CEST)
    Decision to Authors: July 22, 2021 August 15, 2021
    Workshop Date: September 28, 2021

    Use the following CMT submission link



Submission and Reviews

We invite submissions of 3 page extended abstracts on research ideas and publications you would like to present as part of the workshop. The 3 pages exclude references and represent an upper limit as long as you communicate your presentation topic clearly in your abstract.

The presented research is not required to be novel and we allow presenting previously published work. This workshop should not necessarily be considered as a venue for publication, but rather as a place to gather the community interested in scene understanding in unstructured environments.

The submissions will be processed via CMT, follow this link to the CMT submission page.



Submission Format

Extended abstracts submitted to the workshop should conform with the Springer LCNS format. The template is also available on Overleaf.

The extended abstract length is limited to 3 pages excluding references. All papers have to be submitted as a single PDF file.

We allow supplementary material to be submitted as well. The material can contain documents, images, scripts, and videos. The material should be submitted as a ZIP file and should not exceed 50 MB.

The SUUE workshop reviewing is single blind and managed over Microsoft CMT.



Topics

The goal of this workshop is to bring together the part of the vision community interested in scene understanding in unstructured environments. The following topics are of interest for submission at this workshop:


Scene Understanding in Unstructured Environments

    • Novel datasets for unstructured environments
    • Scene understanding from 2D data
    • Scene understanding from 3D data
    • Semi-supervised learning
    • Self-supervised learning
    • Landmark detection in unstructured environments
    • Part-based approaches in unstructured environments

Multimodal Learning

    • Multimodal segmentation in unstructured environments
    • Sensor fusion in unstructured environments
    • Using geographical priors for scene understanding
    • Using shape priors for scene understanding

Transfer Learning

    • Transfer learning from urban to outdoor environments
    • Domain adaptation
    • Unsupervised domain adaptation
    • Synthetic data generation for unstructured environments
    • Multi-domain scene understanding

Applications in Field Robotics

    • Precision agriculture
    • Autonomous driving in unstructured outdoor environments
    • Semantic mapping of unstructured environments
    • Decontamination of hazardous waste sites
    • Outdoor environment monitoring
    • Maritime robotics
    • Forestry robotics