The workshop has started. You can join our live YouTube stream now.
The workshop program is published.
Paper submissions are now open!
All challenges are now online and accepting submissions!
Challenges are open: May, 07, 2021
Challenge second phase: July, 22, 2021
Challenges close: August 1, 2021
Abstract submission deadline: July 26, 2021, 11:59pm pacific time August 1, 2021, 11:59pm pacific time
Paper submission deadline: August 1, 2021, 11:59pm pacific time (no extension possible)
Acceptance notification: August 11, 2021
Camera ready due: August 17, 2021
Workshop (full day): October 16, 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 advance the state of the art in topological and geometric shape analysis using deep learning, we aim to gather researchers from computer vision, computational geometry, computer graphics, and machine learning in this 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.
Boundary extraction from 2D/3D shapes
Geometric deep learning on 3D and higher dimensions
Generative methods for parametric representations
Novel shape descriptors and embeddings 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