Comments (2)
you can control the size of superpoint with --reg_strength and the minimal size of superpoints with --cutoff, but you can't control the exact number of sp or their exact size.
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Hi!
We are releasing a new version of SuperPoint Graph called SuperPoint Transformer (SPT).
https://github.com/drprojects/superpoint_transformer
It is better in any way:
✨ SPT in numbers ✨ |
---|
📊 SOTA results: 76.0 mIoU S3DIS 6-Fold, 63.5 mIoU on KITTI-360 Val, 79.6 mIoU on DALES |
🦋 212k parameters only! |
⚡ Trains on S3DIS in 3h on 1 GPU |
⚡ Preprocessing is x7 faster than SPG! |
🚀 Easy install (no more boost!) |
If you are interested in lightweight, high-performance 3D deep learning, you should check it out. In the meantime, we will finally retire SPG and stop maintaining this repo.
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Related Issues (20)
- CUDA error when training for Semantic3D HOT 1
- the version of metrics HOT 2
- When making ply_c, fatal error: numpy/ndarrayobject.h: No such file or directory HOT 3
- How to control the number of superpoints in a room? HOT 6
- Segmentation fault (core dumped) HOT 2
- Running on Stanford3dDataset_v1.2_Aligned_Version, the error occurs. HOT 6
- CMake error HOT 1
- Which version of Pytorch is needed for this code? HOT 1
- ModuleNotFoundError: No module named 'torchnet' HOT 3
- RuntimeError: scan failed to synchronize: an illegal memory access was encountered HOT 2
- L0-cut pursuit partition algorithm HOT 3
- cupy_backends.cuda.api.driver.CUDADriverError: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered HOT 3
- Overfitting soon after around 30 epochs HOT 13
- How to visualize SSP HOT 1
- ValueError: need at least one array to concatenate HOT 1
- Pretrained weight link
- How to visualize SSP results? HOT 1
- What parts of the code should be changed in the custom dataset when using this network? HOT 2
- How to set the parameters in Geometric Partition of SPG? HOT 2
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