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jeanfeydy avatar jeanfeydy commented on July 17, 2024

Hi @coldgemini ,

All the examples in the gallery work fine in 3D: the tutorials are written the way they are because it is easier for me to show what is happening with 2D toy examples, but there really isn't any technical limitation as far as GeomLoss is concerned. See for instance the tutorial on color transfer, which is a simple 3D point cloud registration in the unit RGB cube.
Going further, I will implement higher-level loss functions over the summer (such as varifold kernel and OT losses for 3D meshes), and write self-contained rigid or spline registration examples. Before working on it, I just have to release KeOps v1.1 first!

Note, however, that unlike KeOps, GeomLoss will not support raw numpy input: the native support of automatic differentiation by PyTorch is a key feature that allowed me to considerably trim down the GeomLoss interface. It really should be favored over numpy for any kind of intensive application, and is on the verge of becoming a new standard for computational anatomy - my main motivation.

Best regards,
Jean

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coldgemini avatar coldgemini commented on July 17, 2024

from geomloss.

jeanfeydy avatar jeanfeydy commented on July 17, 2024

You're welcome! Feel free to create an other issue if you have any other question.

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coldgemini avatar coldgemini commented on July 17, 2024

did you integrate geomloss into your lddmm registration project?

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