Team 13 repository for ML3D course project @KAIST
This is our project "Geometric Deep Learning for 3D Shape Correspondence". It explores several recent methods on 3D Shape correspondence, including:
- CNN methods with local operators e.g. Geodesic CNN [1] and MoNet[2].
- methods evaluating functional maps on geometry e.g. Geometric Functional Maps [3] and
- our main focus (baseline method), DiffusionNet[4].
The README in each folder explains the prerequisite development environment, code and data needed.
Our code is evaluated on the FAUST Humans dataset[5], a standard realistic benchmark for 3D Shape Correspondence methods.
[1] Masci, J. et al. 2015. Geodesic Convolutional Neural Networks on Riemannian Manifolds. 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) (Dec. 2015)
[2] Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, & Michael M. Bronstein. (2016). Geometric deep learning on graphs and manifolds using mixture model CNNs.
[3] Nicolas Donati, Abhishek Sharma, & Maks Ovsjanikov. (2020). Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence.
[4] Sharp, N. et al. 2022. DiffusionNet: Discretization Agnostic Learning on Surfaces. arXiv.
[5] Bogo, F. et al. 2014. FAUST: Dataset and Evaluation for 3D Mesh Registration. 2014 IEEE Conference on Computer Vision and Pattern Recognition (Columbus, OH, USA, Jun. 2014)