Motivation 💡
The ability to perceive, understand and interact with arbitrary 3D environments is a long-standing goal in
both academia and industry with applications in AR/VR as well as robotics.
Current 3D scene understanding models are largely limited to recognizing a closed set of pre-defined object
classes.
Recently, large visual-language models, such as CLIP, have demonstrated impressive capabilities trained
solely on internet-scale image-language pairs.
Some initial works have shown that these models have the potential to extend 3D scene understanding not only
to open set recognition, but also offer additional applications such as affordances, materials, activities,
and properties of unseen environments.
The goal of this workshop is to bundle these initial siloed efforts and to discuss and establish clear task
definitions, evaluation metrics, and benchmark datasets.
Invited Speakers 🧑🏫
Professor Shuran Song
Columbia Artificial Intelligence and Robotics (CAIR) Lab
Shuran Song is an assistant professor at the Department of Computer Science at Columbia University,
where she directs the Columbia Artificial Intelligence and Robotics (CAIR) Lab.
Her research focuses on computer vision and robotics.
She is interested in developing algorithms that enable intelligent systems to learn from their
interactions with the physical world, and autonomously acquire the perception and manipulation skills
necessary to execute complex tasks and assist people.
Professor Angela Dai
Technical University of Munich
Angela Dai is an assistant professor at the Technical University of Munich (TUM) where she leads the
3D AI Lab.
Her research focuses on understanding how the 3D world around us can be modeled and semantically
understood.
Prof. Dai is the creator of the seminal ScanNet benchmark that sparked the development of numerous 3D
scene understanding works.
Professor Manolis Savva
Simon Fraser University
Manolis Savva is an assistant professor in the School of Computing Science at Simon Fraser University,
and a Canada Research Chair in Computer Graphics.
His research focuses on analysis, organization and generation of 3D content.
The methods that he works on are stepping stones towards holistic 3D scene understanding revolving
around people,
with applications in computer graphics, computer vision, and robotics.
Prof. Savva contributed highly influential works towards embodied AI including
Matterport and Habitat.
Professor Thomas Funkhouser
Princeton University
Thomas Funkhouser is a full professor at Princeton University and a senior research scientist at
Google.
His research focuses on computer graphics, computer vision, and in particular 3D machine perception.
In recent years, Professor Funkhouser has greatly impacted the field of 3D scene understanding.
Professor Jen Jen Chung
University of Queensland
Jen Jen Chung is an associate professor in Mechatronics within the School of Information Technology
and Electrical Engineering at the University of Queensland.
Her current research interests include perception, planning and learning for robotic mobile
manipulation, algorithms for robot navigation through human crowds, informative path planning and
adaptive sampling.
Important Dates 🗓️
- Paper Track: We accept novel full 8-page papers for publication in the proceedings, and shorter 4-page extended abstracts of either novel or previously published work that will not be included in the proceedings. All submissions shall follow the ICCV 2023 author guidelines.
- Submission Portal: CMT
- Paper Submission Deadline: July 20, 2023
- Notification to Authors: August 2, 2023
- Camera-ready submission: August 11, 2023
- Challenge Track: TBD
