The Third Workshop and Challenge on Deep Learning for Geometric Computing Call for Papers

The Third Workshop and Challenge on Deep Learning for Geometric Computing 
in conjunction with ICCV 2021

Computer vision approaches have made tremendous efforts toward
understanding shape from various data formats, especially since
entering the deep learning era. Although accurate results have been
obtained in detection, recognition, and segmentation, there is less
attention and research on extracting topological and geometric
information from shapes. These geometric representations provide
compact and intuitive abstractions for modeling, synthesis,
compression, matching, and analysis. Extracting such representations
is significantly different from segmentation and recognition tasks, as
they contain both local and global information about the shape. To
attract attention of researchers from computer vision, computational
geometry, computer graphics, and machine learning to this branch of
problems, we organize the third edition of “Deep Learning for
Geometric Computing” workshop at ICCV 2021. The workshop
encapsulates competitions with prizes, proceedings, keynotes, paper
presentations, and a fair and diverse environment for brainstorming
about future research collaborations.

*************** Call for competition participation ***************

We are hosting seven competition tracks in two main domains: The
SkelNetOn Challenge (2D) and The ABC Challenge (3D), implemented as
independent contests available at Codalab.

*** The SkelNetOn Challenge ***

The SkelNetOn Challenge is structured around shape understanding in
four domains. We provide shape datasets and some complementary
resources (e.g, pre/post-processing, sampling, and data augmentation
scripts) and the testing platform.

Submissions to the challenge will perform one of the following tasks:
- Shape pixels to skeleton pixels
- Shape points to skeleton points
- Shape pixels to parametric curves
- Natural image pixels to skeleton pixels 

*** The ABC Challenge ***
The ABC Challenge serves as a testbed for common shape analysis and
geometry processing tasks. We supplement the challenge with additional
software libraries, sets of large-scale standardized benchmarks (data
splits, resolutions, and targets), and implementations of evaluation
metrics. The first ABC challenge will be hosting a three-track contest
on geometry processing, including:
- Estimation of non-oriented normals
- Geometric shape segmentation
- Sharpness fields extraction

*************** Call for paper submissions ***************

We will have an open submission format where i) participants in the
competition will be required to submit a paper, or ii) researchers can
share their novel unpublished research in deep learning for geometric
computing. The top submissions in each category will be invited to
present their work during the workshop and will be published in the
workshop proceedings. The workshop will also honor the best paper and
the best student paper.

Although we encourage all submissions to benchmark their results on
the evaluation platform, there are other relevant research areas that
our datasets do not address. For those areas, the scope of the
submissions may include but is not limited to the following general

    Boundary extraction from 2D/3D shapes
    Geometric deep learning on 3D and higher dimensions
    Generative methods for parametric representations
    Novel shape descriptors and embedding for geometric deep learning
    Deep learning on non-Euclidean geometries
    Transformation invariant shape abstractions
    Shape abstraction in different domains
    Synthetic data generation for data augmentation in geometric deep learning
    Comparison of shape representations for efficient deep learning
    Novel kernels and architectures specifically for 3D generative models
    Eigen-spectra analysis and graph-based approaches for 3D data
    Applications of geometric deep learning in different domains
    Learning-based estimation of shape differential quantities
    Detection of geometric feature lines from 3D data, including 3D point clouds and depth images
    Geometric shape segmentation, including patch decomposition and sharp lines detection

The CMT site for paper submissions is . Each submitted paper
must be 4-8 pages excluding references. Please refer to the ICCV
author submission guidelines for instructions at The review
process will be double-blind but the papers will be linked to any
associated challenge submissions. Selected papers will be published in
IEEE ICCVW proceedings, visible in IEEE Xplore and on the CVF Website.

*************** Awards ***************

The winning submission in each seven track will receive a prize
(either cash or equipment) provided by the workshop sponsors. The top
submissions in each category with accepted papers in the workshop will
be chosen as finalists and will be invited to present their research
in the spotlight session.

*************** Important dates ***************

    Challenges Launch for Submissions: May, 07, 2021
    Second Phase for Submissions: July, 22, 2021
    Challenges Close for Submissions: August 1, 2021
    Abstract Submission Deadline: July 26, 2021
    Paper Submission Deadline: August 1, 2021
    Acceptance Notification: August 11, 2021
    Camera Ready Due: August 17, 2021
    Workshop (full day): October 11, 2021


    Ilke Demir, Sr. Staff Research Scientist, Intel Corporation
    Alexey Artemov, Research Scientist, Skolkovo Institute of Science and Technology
    Dena Bazazian, Senior Research Associate, University of Bristol
    Bernhard Egger, Postdoctoral Researcher, MIT
    Géraldine Morin, Professor, University of Toulouse
    Kathryn Leonard, Professor of Computer Science, Occidental College
    Evgeny Burnaev, Associate Professor, Skolkovo Institute of Science and Technology
    Adarsh Krishnamurthy, Associate Professor, Iowa State University
    Daniele Panozzo, Assistant Professor, Courant Institute of Mathematical Sciences, New York University
    Albert Matveev, Ph.D. student, Skolkovo Institute of Science and Technology
    Denis Zorin, Professor of Computer Science and Mathematics, Chair of Computer Science Department Courant Institute of Mathematical Sciences, New York University
    Rana Hanocka, Ph.D. student, Tel Aviv University
    Sebastian Koch, Ph.D. student, Technische Universität Berlin