CVPR 2021 Workshop on 3D Vision and Robotics

Website: https://sites.google.com/view/cvpr2021-3d-vision-robotics

Overview
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In this workshop, we aim to bring together experts spanning visual computing, machine learning, and robotics to discuss challenges in 3D vision and how it can help with perception, control, and planning in robotics. One of the key challenges of 3D vision is what type of representation is appropriate and how do we design machine learning algorithms for these different representations. In contrast to 2D images that have a standard representation as regular pixel grids, 3D data can come as irregular 3D point clouds (e.g., acquired with LIDAR sensors), meshes of varying topology, or as volumetric data. Intelligent agents also need to take temporal sequences of sensory observations and make decisions on how to act. Will accumulating sensory observations into a 3D representation of the world lead to better models? This workshop will provide a venue for people interested in 3D vision and robotics to come together to discuss the various challenges and problems in this area.

Topics of Interest
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This workshop will focus on related discussion topics such as the ones below:
- Is 3D useful for robotics? What kind of 3D representations are useful for robotics?
- How can a robot learn a 3D representation of its environment and relevant objects from raw sensory input under noisy sensors and actuators? What kind of machine learning algorithms are needed?
- What is the right interface between 3D perception and planning & control?
- What are the underexplored areas of 3D perception for robotics (e.g. instance recognition, few-shot learning)?
- What is the role of 3D simulation for robotics?
- Both robotics and 3D data are fields that are research areas with high barriers to entry. How can we enable researchers from other fields such as ML to more easily work in these areas?

Call for Papers
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Submission page: https://cmt3.research.microsoft.com/3DVR2021
We solicit 2-4 page extended abstracts conforming to the official CVPR style guidelines. A paper template is available in LaTeX and Word. References will not count towards the page limit. The review process is double-blind.
Submissions can include: late-breaking results, under review material, archived, or previously accepted work (please make a note of this in the submission).

Important Dates
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Submission Deadline: April 21, 2021 (11:59 pm PST)
Papers Assigned to Reviewers: April 24, 2021 (11:59 pm PST)
Reviews Due: May 8, 2021 (11:59 pm PST)
Acceptance Decision: May 15, 2021 (11:59 pm PST)
Camera-Ready Version: May 29, 2021 (11:59 pm PST)
Please note the accepted contributions will be presented as spotlight talks in the workshop and will be posted on the workshop website upon author approval.

Organizers
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Angel X. Chang (Simon Fraser University)
Katerina Fragkiadaki (Carnegie Mellon University)
Qixing Huang (The University of Texas at Austin)
Li Erran Li (Alexa AI at Amazon)
Charles Ruizhongtai Qi (Waymo LLC)
Yu Xiang (NVIDIA Research)
Yuke Zhu (The University of Texas at Austin)