martianxiu / msecnet Goto Github PK

Hello! Thanks for your great work!
When I try to test the accuracy of the inference, I found that line 416 in test_sparse_full_PCPNet.py which require file of the format ".pidx.npy".
I ran the download_pclouds.py and I did not get the file matched that format, only ".pidx" existed.
I wonderer whether I should convert the format of ".pidx" file into ".pidx.npy" manually or the format changed itself when I ran the code.
Best.

Hey authors, great work! Thank you for open-sourcing the code!

I reproduced the tests with your pretrained model for PCPNet and was wondering if you have deeper insights of "how to orient the Normans" in post-processing.

Your Output results of the Liberty Statue show oriented normals, which doesn't match the predicted output of the model and the test script.
I can reproduce a kind of similar result if I use third party libraries, e.g. Open3D to orient normals along local tangent planes. But this is highly inefficient for larger point clouds.

Can you recommend a more efficient way to orient the normals global, that fits better in the pipeline with your approach, to reproduce the presented results?