Comments (4)
Hi,
Yes, we extract 1 feature (the bottleneck of the AE) per point-cloud.
We don't apply any test-augmentations. I will try to release the notebook/code soon. Thanks a lot for your interest.
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Hi,
Yes, we extract 1 feature (the bottleneck of the AE) per point-cloud.
Hi optas!
When you talk about "1 feature per point-cloud", you mean 1 feature vector per point-cloud?
(e.g. 100 chairs point-clouds with 128 feature per point-cloud, so 100x128 features)
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Hi @optas ,
It is might be too late to ask since this project is about 4-5 years ago. But if it would be possible for your to release the part of code for SVM. I totally understand your decision.
Best regards.
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Related Issues (20)
- 'approx_match' is not defined? HOT 2
- No module named latend_3d_points.src.ae_templates HOT 4
- Training my own data
- Training my own data
- The original shapenetcore dataset HOT 3
- Info on clock time per epoch (approximately)
- Where can I get modelnet10 dataset in xyz format
- tf_nndistance_so.so: undefined symbol: __cudaRegisterFunction HOT 1
- NameError: global name 'nn_distance' is not defined HOT 1
- How can I download your dataset?
- How to use D-FAUST database?
- train_single_class_ae.py : IndexError: list index out of range HOT 2
- EMD code
- Cannot compile the EMD/Chamfer losses HOT 1
- [AD] Yet another EMD implementation for dense point clouds HOT 1
- Cannot compile EMD makefile on Google Colab HOT 1
- Running on GPU HOT 1
- Any pytorch implementation available
- NameError: name 'raw_input' is not defined HOT 1
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