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Train and Test #8
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First of all, sorry for the late response. The reason for using the same dataset for training and testing is that NeRF requires each network for each dataset. The reason why a datapath is not required in the compression process is that our compressing process does not require any tuning during the compression process. |
Hello, I have another question for the paper. |
The reason for using idwt is that we view planes as wavelet coefficients. |
Can I understand that by adding an inverse wavelet transform, the coefficients obtained when decomposing the three-dimensional mesh into planes can be more sparse (closer to the coefficients obtained with the wavelet transform) |
If wavelet coefficients were to be used, they must be inverse transformed before rendering. That is why we used iDWT, so they are not close to wavelet coefficients, but they are the wavelet coefficients. |
OkI think i know your means. |
Could you please check if you used the latest version by any chance? |
I solved this problem by commenting out the code using inverse in compress. Thank you for your reply and your great work! |
Hello, I try to train and test your work with what you say in readme.md.
But i have a question that why train and test are the same dataset(chari.txt)
And Why there is no dataset_path in compress progress.
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