pointnet2
Pointnet_Pointnet2_pytorch
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| pointnet2 | Pointnet_Pointnet2_pytorch | |
|---|---|---|
| 2 | 3 | |
| 2,875 | 3,187 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | 5 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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pointnet2
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Why does trained model fail on new data?
Is this how you trained the segmentation? https://github.com/charlesq34/pointnet2
- Are the New M1 Macbooks Any Good for Deep Learning? Let’s Find Out
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?
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
dgcnn.pytorch - A PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
ttach - Image Test Time Augmentation with PyTorch!
Pix2Vox - The official implementation of "Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images". (Xie et al., ICCV 2019)
albumentations - Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
RAFT
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
SimpleView - Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"