Comments (7)
@gfiameni First, you need to convert RGBD to point clouds format. You may check here as reference.
from softgroup.
Yes. As long as your data has semantic_id
and instance_id
you can use this repo to train your data. To do that you need to create new data directory in SoftGroup/dataset/
then preprocess to generate .pth
file for input and .txt
file for ground truth (example here and here).
After that you can create a new dataloader in SoftGroup/data
, follow scannetv2_inst.py.
from softgroup.
Thanks a lot for your response. Do you have a specific tool for generating instance segmentation labels of the dataset? Or should I keep using Cloud Compare by adding two scalar fields to every point: semantic_id
and instance_id
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I just used standard public dataset. I think you can use that tool to label your data.
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Thanks a lot for your reponse. Do you think that this format is valid for training. This is a sample of the txt file
-------------------------------------------------------------------------------------------------
X Y Z RGB semantic_id instance_id
-------------------------------------------------------------------------------------------------
-87.30151367 48.11581421 -32.99898148 0 0 0 1.000000 0.000000
-83.23958588 53.01259232 -31.98804665 0 0 0 1.000000 0.000000
-91.00957489 52.23581314 -25.49759102 0 0 0 1.000000 0.000000
-91.01223755 52.74370193 -25.20007706 0 0 0 1.000000 0.000000
-82.53973389 53.23898315 -32.66012573 0 0 0 1.000000 0.000000
-82.68397522 51.79740906 -34.88851547 0 0 0 1.000000 0.000000
-82.80941010 55.73773575 -32.12082672 0 0 0 1.000000 0.000000
-83.50354767 54.06523132 -31.11934280 0 0 0 1.000000 0.000000
-83.09005737 53.63531876 -31.71231270 0 0 0 1.000000 0.000000
-83.52418518 56.01091003 -30.02872658 0 0 0 1.000000 0.000000
-92.61465454 47.71725464 -30.11337662 0 0 0 1.000000 0.000000
-85.50755310 50.83419800 -31.79884911 0 0 0 1.000000 0.000000
-85.05304718 49.18477631 -34.39563751 0 0 0 1.000000 0.000000
-87.02261353 47.79521561 -33.57074738 0 0 0 1.000000 0.000000
-87.79747009 48.99874496 -31.63723564 0 0 0 1.000000 0.000000
-88.17048645 48.41600037 -32.07061386 0 0 0 1.000000 0.000000
-83.23866272 51.24077225 -34.98325729 0 0 0 1.000000 0.000000
-88.18229675 47.79760361 -32.80801010 0 0 0 1.000000 0.000000
-91.42159271 48.30610657 -30.03435135 0 0 0 1.000000 0.000000
-87.46870422 47.66637421 -33.31636047 0 0 0 1.000000 0.000000
from softgroup.
Yes. This is expected format.
from softgroup.
On the same topic, I have an annotated 2D (RGB-D) COCO datasets, is there any way I can leverage SoftGroup for 3D segmentation? Tnx
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Related Issues (20)
- TypeError: forward() takes 6 positional arguments but 9 were given(maybe something wrong with the source codes? HOT 1
- How to obtain voxel labels? HOT 2
- Request code to evaluate mPrec mRec mCov mWcov HOT 1
- Transform_train for custom dataset HOT 1
- Train SoftGroup on different Scenarios HOT 1
- Custom dataset visualization HOT 3
- Why do you need "force_fp32"? HOT 2
- Training batch is empty HOT 2
- Inference without label
- How do I run this network with my own data?
- Volume of instances
- Model reproducibility
- BatchNorm not training
- Understanding the how the visualization process works on instance segmentation
- import error can not import ops from module softgroupe.ops HOT 1
- cannot find the pretrained model:softgroup_s3dis_spconv2.pth HOT 1
- i dont know how to kill a hanging progress HOT 2
- about late devoxelization HOT 1
- AssertionError: Empty dataset HOT 1
- The problem encountered during training on my own dataset HOT 2
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