posted by organizer: daerduomkch || 1298 views || tracked by 1 users: [display]

StruCo3D 2021 : Structural and Compositional Learning on 3D Data (ICCV 2021 Workshop)

FacebookTwitterLinkedInGoogle

Link: https://geometry.stanford.edu/struco3d
 
When Oct 11, 2021 - Oct 17, 2021
Where Virtual
Submission Deadline Jul 26, 2021
Notification Due Aug 9, 2021
Final Version Due Aug 16, 2021
Categories    computer vision   computer graphics   robotics   structure learning
 

Call For Papers

3D structure and compositionality lie at the core of many methods for different tasks in computer vision, graphics and robotics, including but not limited to recognition, reconstruction, generation, planning, manipulation, mapping and embodied perception. Unlike traditional connectionist approaches in deep learning, structural and compositional learning includes components that lean more towards the symbolic end of the spectrum, where data or functions are represented by a sparse set of separate and more clearly defined concepts. For example, in 3D objects, this could be a decomposition of an object into spatially localized parts and a sparse set of relationships between them, or in scenes, it could be a scene graph, where rich inter-object relationships are described. Similarly, a navigation or interaction task in robotics can also be decomposed into separate parts of concepts or submodules that are related by spatial, causal, or semantic relationships.

People from different fields or backgrounds use different structural and compositional representations of their 3D data for different applications. We bring them together in this workshop to have an explicit discussion of the advantages and disadvantages of different representations and approaches, as well as to share, discuss and debate the diverse opinions regarding the following questions:

- Which types of structure should we use for different tasks and applications in graphics, vision and robotics?
- How should we factorize a given problem into sparse concepts that make up the structure?
- How should we factorize different types of 3D data into sparse sets of components, relationships, or operators?
- Which algorithms are best suited for a given type of structure?
- How should we mix structural and non-structural approaches?
- Which parts of a problem are suited for structural approaches, and which ones are better handled without structure?

We accept both archival full paper (up to 8 pages) and non-archival short paper (up to 4 pages) submissions. Every accepted paper will have the opportunity to give a 10-min spotlight presentation and host two 30-min poster sessions (12-hours separated).

Please refer to the workshop website and CFP page for more details: https://geometry.stanford.edu/struco3d

Contact: struco3d@googlegroups.com or kaichun@cs.stanford.edu

Related Resources

Federated Learning in IOT Cybersecurity 2021   PeerJ Computer Science - Federated Learning for Cybersecurity in Internet of Things
CMCA 2022   11th International Conference on Control, Modelling, Computing and Applications
IEEE COINS 2022   IEEE COINS 2022: Hybrid (3 days on-site | 2 days virtual)
RiE 2022   13th International Conference on Robotics in Education
ICPR 2022   26th International Conference on Pattern Recognition
IEEE COINS 2022   Internet of Things IoT | Artificial Intelligence | Machine Learning | Big Data | Blockchain | Edge & Cloud Computing | Security | Embedded Systems |
EI Compendex, Scopus-CDIVP 2022   2022 2nd International Conference on Digital Image and Video Processing (CDIVP 2022)
IEEE SSCI 2023   2023 IEEE Symposium Series on Computational Intelligence
ABDAI 2022   2022 4th International Conference on Applications of Big Data and Artificial Intelligence(ABDAI 2022)
WSCG 2022   30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision