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Code for "Cross-modal Learning for Image-Guided Point Cloud Shape Completion" (NeurIPS 2022)

License: MIT License

Python 69.78% C++ 9.09% Cuda 18.66% C 1.02% Makefile 1.12% CMake 0.17% Shell 0.14%
deep-neural-networks differentiable-rendering multimodal-deep-learning point-cloud point-cloud-completion transformer weakly-supervised-learning

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xmfnet's Issues

About the evaluation results

Hi, thanks for your work.

I wonder what is the test_list.txt in eval.py file this line. Is it the same with the test_list2.txt?

Besides, I want to know the what the data split you used to obtain the evaluation results in Table 1 and Table 2 in your paper?

Can I run this model on my own data?

Thanks for your work! I have .pcd file and images. But I don't have ground truth. Can I use your model to do point cloud completion on my own data? Can you give me some advice>

I think your experiments may have some problems

I think your experiments may be wrong. You used L1 chamfer distance loss to train the model, but L1 is not derivable. And you use L2 CD loss for evaluation. We usually use L1 for evaluation. In other words, in the code, you have reversed L1 and L2. In fact, we can't achieve such accuracy in your paper by using the trained model provided by you. Moreover, if you input 2048 points, your model only generate 1024 more points (8 branches, generate 128 for each branch). We don't usually generate so few papers. Your paper setting is different from other papers, so it is not comparable with them.

Something wrong about the datasets ShapeNet-ViPC

When I run the command cat ShapeNetViPC-Dataset.tar.gz* | tar zx , I only get the corresponding images (ShapeNetViPC-View). So, what can I do to get the corresponding partial and complete point clouds?

About the Experiment

As for the impact of auxiliary image input on completion performance in the paper, I reproduced it with the pre-trained model you provided. Different auxiliary images seem to have very little impact on performance. Why is that?

about the dataset.

Hello, when we use shapenet-vipc data sets of some categories, there will be data loss. How do you solve it?

Can the code work with CUDA11.1

Thanks for your great work.
I tested the code with cuda11.1, pytorch 1.10.1, then I got "segmentation fault". Can the code work with cuda11.1?

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