Comments (1)
Dear @Lelucermaire111,
Thanks for your interest in our work!
It is an interesting question. I think you can use the learned sampling concept but add hierarchy. For example, use a SampleNet over local regions of the point cloud to sample them. Then, if you want to lower the number of points further, unify the points from the local regions and run another SampleNet on that set.
Let me know if you have any further questions or thoughts.
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Related Issues (20)
- NameError: name 'farthest_point_sample' is not defined HOT 3
- Whether the SampleNet is suitable for large scene data sets like semantic3d or KITTI? HOT 2
- Compare with your first paper HOT 3
- What does "Classification accuracy" mean in the paper? HOT 1
- Can SampleNet obtain better performance on Semantic Segmentation task? HOT 4
- problem with knn_cuda torch HOT 1
- Some problems with converting Tensorflow framework to PyTorch framework HOT 5
- Difference test set accuracy of train_samplenet.py and evaluate_samplenet.py in the classification task HOT 3
- Visualization HOT 7
- Have you applied this work to PointNet++? HOT 1
- Reconstruction Use other data HOT 6
- Question about reflectivity HOT 1
- How to process my point cloud file with the network HOT 5
- problem with dataloader HOT 1
- Can't run compile_ops.sh in classification folder and GPU usage HOT 7
- registration result visualization HOT 1
- classification HOT 1
- Docker problem in registration HOT 1
- Classification Accuracy HOT 9
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