dgcnn.pytorch
Pointnet_Pointnet2_pytorch
dgcnn.pytorch | Pointnet_Pointnet2_pytorch | |
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1 | 3 | |
656 | 3,250 | |
- | - | |
10.0 | 0.0 | |
over 1 year ago | 26 days ago | |
Python | Python | |
MIT License | MIT License |
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dgcnn.pytorch
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Neural Network on Point Clouds
In this project I generate pointclouds from ifc files. Next step is getting dgcnn to run on the dataset to do some segmentation.
Pointnet_Pointnet2_pytorch
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Help me understand how to use this PointNet implementation (pytorch, point cloud classification)
here is a sample from one of these log files
I am trying to use this implementation of PointNet (GitHub repo)
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Are the New M1 Macbooks Any Good for Deep Learning? Let’s Find Out
Here you go: https://github.com/yanx27/Pointnet_Pointnet2_pytorch - no need for any custom cuda code.
Note the cuda kernels in the original repo were added in August 2017. It might have been the case at the time they needed them, but again, if you need to do something like that today, you're probably an outlier. Modern DL library have a pretty vast assortment of ops. There have been a few cases in the last couple of years when I thought I'd need write a custom op in cuda (e.g. np.unpackbits) but every time I found a way to implement it with native Pytorch ops.
If you're doing DL/CV research, can you give an example from your own work where you really need to run custom cuda code today?
What are some alternatives?
pointnet2 - PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.