Our workshop will be hybrid and take place on June 20th. Stay tuned for information on online and in-person attendance.
Challenge submission deadline (training phase): March 20th, 2022
Challenge submission deadline (validation phase): March 27th, 2022
Paper submission deadline: March 27th, 2022
Acceptance notification: April 10th, 2022
Camera-ready paper due: April 18th, 2022
Workshop (full day): June 20th, 2022
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 CVPR 2022. 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