Comments (1)
Thanks for your email and trying out our codebase. Unfortunately it is not possible for us to share the complete evaluation script at this point but I can help as much as I can to help you reproduce numbers in the paper:
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The pre-trained model we provided in the codebase is only for demo and may be sub-optimal for synthetic scenes. I would highly recommend training your own model using the instructions provided in our repo to get the best checkpoints which you can quantitatively evaluate.
That's correct. ShAPO training from scratch without post-optimization should give numbers close to CenterSnap. -
Your understanding is correct. We use the following to get the predicted sizes. where pcd_dsdf_actual is the pointcloud obtained from the sdf latent codes as here:
pred_size = 2 * np.amax(np.abs(pcd_dsdf_actual), axis=0)
Hope it helps!
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Related Issues (19)
- Problem with boto3 HOT 6
- Creating custom dataset for shapo HOT 7
- Error in distributed_generate_data HOT 9
- Shape and Texture pretraining on the custom dataset to create 'sdf_rgb_pretrained' folder HOT 5
- Use Linemod dataset HOT 9
- I can't find those 4 files: HOT 1
- Dependency and installation, runtime errors in colab HOT 2
- Support for realtime camera image? ROS Integration HOT 5
- how can i get gt rotation matrix, translation vector of each image HOT 1
- Running the evaluation code HOT 4
- Dataset preprocessing HOT 2
- can't find .pickle.zstd to train
- Thank you for your contribution!
- REAL test_data is unavailable HOT 1
- Your display PPT is very amazing, can you share it? HOT 1
- Dataset download HOT 2
- Problems with running code HOT 1
- About pretrained models for inference HOT 1
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from shapo.