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I have found better initial points in traditional rigid registration before, but I want to migrate to DiffDRR and find that it is not ideal（I used the astra library to generate DRR (https://github.com/astra-toolbox/astra-toolbox), which I set based on the actual projection conditions of the X-ray image）. So if I don't use my previous better initial points, will it affect the subsequent registration results? This question is bugging me right now, if you could answer it I would really appreciate it！

Hi,
This is a great work and I'd like to try it out on my custom data. However, not able to perform training/inference/visualization of results on custom data. Could anyone please guide me towards using a simple API to train/infer and visualize inferred results?

Thanks in advance,
Amar

• Can you compile the DRR module before running pose optimization?
• Does it make a difference?

See eigenvivek/DiffDRR#183

• Remove diffpose.calibration.RigidTransform and all related conversion functions, use diffdrr.se3.RigidTransform

I have a question about getting the volume

I understood the operation the swapaxes of the volume, but I don’t understand why [::-1] is needed.

New PR fixes numerical stability issues that necessitated developing pytorchse3

Try the latest version of pytorch3d

facebookresearch/pytorch3d#1609

https://arxiv.org/abs/2308.10902

eigenvivek/DiffDRR#179