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Hi, I am currently endeavoring to train the joint SDF-VAE to acquire SDF modulations. Successful reconstruction is observed when processing data in the object normalization space (as exemplified by ShapeNet). However, when I rotate and translate the object for training, but also normalize the object in the unit cube space. Despite a seemingly well-performing loss, the model struggles to faithfully reconstruct the object. Presented below are the reconstructed results; could this discrepancy be indicative of a limitation in the model?
The text was updated successfully, but these errors were encountered:
Hi, I am currently endeavoring to train the joint SDF-VAE to acquire SDF modulations. Successful reconstruction is observed when processing data in the object normalization space (as exemplified by ShapeNet). However, when I rotate and translate the object for training, but also normalize the object in the unit cube space. Despite a seemingly well-performing loss, the model struggles to faithfully reconstruct the object. Presented below are the reconstructed results; could this discrepancy be indicative of a limitation in the model?
![image](https://private-user-images.githubusercontent.com/162939597/311575320-45ad64f0-a702-4a13-9506-143f1b1d9360.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjU5NzI3NzcsIm5iZiI6MTcyNTk3MjQ3NywicGF0aCI6Ii8xNjI5Mzk1OTcvMzExNTc1MzIwLTQ1YWQ2NGYwLWE3MDItNGExMy05NTA2LTE0M2YxYjFkOTM2MC5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwOTEwJTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDkxMFQxMjQ3NTdaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT0yNjQ3NzM1MDRjNzZmYWU2ZmNiM2FjYTEzMjZjNGY2OTQ3MDYwMGZmNmZiYjM4NzFhNDhiMTRjOGVmZTY5MjdjJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.L71t8JL5z3B3PaSRm4e-98IkmqZ7t1MfumR7aj0mutE)
The text was updated successfully, but these errors were encountered: