vcl3d / deepdepthdenoising Goto Github PK

Hi, first of all, thanks for your sharing this great code.
I'd like to follow the code. But, I couldn't understand the pretrained weight format.
You loaded the 6files,
ddd
DeepDepthDenoising-ddd.tar.gz
DeepDepthDenoising-ddd.zip
ddd_ae
DeepDepthDenoising-ddd_ae.tar.gz
DeepDepthDenoising-ddd_ae.zip
But I don't know the format ddd and ddd_ae.
And Which one is the weight that that I have to torch.load?
Sorry for easy one. But I'd like you to tell me.
Thanks.

Hello,
I am receiving errors when I test (using inference.py) with some custom images of different size and the error is as follows -

RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 1. Got 15 and 90 in dimension 2 at c:\a\w\1\s\tmp_conda_3.7_104535\conda\conda-bld\pytorch_1550400486030\work\aten\src\thc\generic/THCTensorMath.cu:83

This particular moment I had an image with dimensions 216*120 but I have had similar error for different sized images as well which were comparatively bigger.
Will you kindly tell what is causing this error and how can I get rid of it?

Thanks for your code and weight.
When I want to use your ddd weight, bad thing occurs:
hape torch.Size([16, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 64, 3, 3]).
size mismatch for decoder_conv_id_1.0.weight: copying a param with shape torch.Size([16, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 64, 1, 1]).
size mismatch for decoder_deconv1_2.conv.weight: copying a param with shape torch.Size([8, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 32, 3, 3]).
size mismatch for decoder_deconv1_1.conv.weight: copying a param with shape torch.Size([1, 8, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 16, 3, 3]).
Is there any wrong with my operation? Or Do you have provided the code for ddd weight now?
And another thing, I use the ddd_ae model, and it works. However, the ply file resulted seems upgly?

Hey,

Thanks for the code.

I would like to ask if the reported results were evaluated on a test split from within the first 300 interior net scenes or was it tested on other scenes?

Could you also share which scene was Figure 11 in the paper taken from since there are millions of images within the dataset? :)

Thank you!

Hello, thank you very much for the code, is it possible to train the model with only single view and depth map? If it is possible, how to load the dataset and modify the code?

Hey guys, great work you have here and thank you for providing the code. I would just like to ask if you could upload the scripts that you use for adding artificial noise to Interiornet dataset so that other researchers are able to benefit from your work.

I would be very grateful if you could do it as soon as possible!

Thank you!