Comments (2)
Hi @ARITRA2296,
Yes, everything is possible, you just need to find the way to implement it. Therefore the question is more: how difficult it would be, and how good are you at python :)
First of all, I would say you will have a much easier time coding this in the more recent PyTorch implementation of KPConv.
If you are ok with it, could you open an issue on the PyTorch GitHub and I will try to give you directions for this implementation? Just so you know before you get into this, it could be quite hard to implement, there might be some tricks that we could find to make it simpler, but overall you will have to be quite good at python and even C++ for the neighborhoods
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Hi @HuguesTHOMAS
Thank you so much for reverting back.
I shall open this issue in the PyTorch implementation. If can guide me on how and where to change the code that would be really great!
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Related Issues (20)
- For custom point cloud data, classification fails during training.
- Instance Segmentation HOT 2
- Planar occlusion explanation HOT 1
- Problemes with testing pretrained NPM3D HOT 4
- About 6-fold CV HOT 2
- about test results
- about making my own datasets
- ModelNet40 implementation - (\x00\x00\x00\x00\x00)
- about segmentation fault (core demped) HOT 2
- about the training time
- Do you have the pretrained model for the Semantic3D dataset? HOT 2
- Thesis not available anymore
- GAN for point cloud
- More features in KPCONV HOT 1
- IndexError:too many indices for array
- Why are there no oa and macc in the test results for S3DIS
- Not compiling
- Why S3DIS is only tested on validation set? HOT 1
- visualize in SemanticKITTI dataset HOT 1
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