Deep Learning
for Geometric Computing

News

• 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!

Important dates

• 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

Introduction

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.

Topics covered

• 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