DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling

This is a sample demo-code for the following papers:

1. DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling, Gautam Pai, Ronen Talmon, Alex Bronstein and Ron Kimmel, IEEE Winter Conference On Applications Of Computer Vision (WACV) 2019. Paper, Poster, Slides

2. Deep Isometric Maps, Gautam Pai, Alex Bronstein, Ronen Talmon, and Ron Kimmel, Elsevier - Image and Vision Computing (Special Issue on Learning with Manifolds in Computer Vision), 2022. Paper

Main Functions

Start with FPS_Single_Display.py for a basic demo of the method on the S-Curve manifold. Implemented with Pytorch.