ranahanocka/MeshCNN
A PyTorch-based deep neural network framework for 3D triangular mesh analysis with custom convolution, pooling, and unpooling operations.
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MeshCNN provides specialized neural network layers that operate directly on mesh edges for 3D shape classification and segmentation. The framework implements convolution, pooling, and unpooling operations tailored for triangular mesh data, enabling end-to-end learning on 3D geometric shapes without converting to intermediate representations.
Frequently asked
- What is ranahanocka/MeshCNN?
- A PyTorch-based deep neural network framework for 3D triangular mesh analysis with custom convolution, pooling, and unpooling operations.
- Is MeshCNN open source?
- Yes — ranahanocka/MeshCNN is open source, released under the MIT license.
- What language is MeshCNN written in?
- ranahanocka/MeshCNN is primarily written in Python.
- How popular is MeshCNN?
- ranahanocka/MeshCNN has 1.7k stars on GitHub.
- Where can I find MeshCNN?
- ranahanocka/MeshCNN is on GitHub at https://github.com/ranahanocka/MeshCNN.