Mohsen Yavartanoo

Thank you very much for your question. ```self.Validation_Gt``` and ```self.Validation_Noisy``` are the list of GT clean and input noisy images, respectively. Therefore, you can simply load your list of images...

You can edit lines 41 and 42 of the [CVF-SID_PyTorch/src/data_loader/data.py ](https://github.com/Reyhanehne/CVF-SID_PyTorch/blob/c89242a4ef5cc98481404192392a6f76e0fb0ec9/src/data_loader/data.py#L41) to read your images as a list of .png files or a .mat file.

Hi, thank you very much for your interest. Since the signal-dependent noise map is multiplied by the zero clean image, it dose not have any effect but still there can...

Thank you for your question. To reduce the padding issue, we first add a padding to an input image and then crop the generated images to have the same size...

Hi, Thanks for your interest. The links are updated now so please check them again.

Google drive has an option that makes a zip file from the entire folder then you can download the zip file.

Thank you very much for your question. In `_gen_polycoeff_center` we translate the center of each primitive to the learned `origins` and simplify the algebraic surfaces to their standard forms to...

Same as the above example each x, y, and z variable will be translated by o1, o2, and o3. Therefore, the new variables will be x-o1, y-o2, and z-o3. If...

Sorry for the ambiguity, we do not use the logits. The code is updated now. We follow [Occupancy Networks](https://github.com/autonomousvision/occupancy_networks) to generate watertight meshes. It generates three files `train.lst`, `test.lst`, and...