Comments (9)
The provided pretrained weights in Github is same with the weights(epinet9x9, epinet5x5) in our paper. But we didn't use a ensemble technique in github, the performance is little different with the performance in the paper. Thanks.
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Thank you very much for your response, have you ever tested your method on old HCI data(stilllife, buddha, butterfly, monasRoom)? I have found that the estimated disparity map has many artifacts for these scenes, so I'm not sure this is due to the model itself or other reasons?
Yours sincerely,
Jinglei
from epinet.
I just tested it, and I think it works well with pretrained weights(9x9).
The ordering(numbering) of old HCI dataset is little different with ours.
So you need to convert them into our data format like below.
f=h5py.File('stillLife/lf.h5','r')
LF=f['LF']
// load LF images (768,768,9,9,3)
LF=np.transpose(LF,(2,3,0,1,4))
// convert to (9,9,768,768,3)
LF=LF[:,:,:,::-1,:]
// reverse order
LF_our_format=LF[np.newaxis,:,:,:,:,:]
// add one dimension
from epinet_fun.func_generate_traindata import generate_traindata512
(val_90d , val_0d, val_45d, val_M45d,_)=generate_traindata512(LF_our_format, np.zeros(LF_our_format.shape[:-1]), Setting02_AngualrViews)
from epinet.
Hi Dr. Shin,
Thank you very much for your advice, I'll retest these scenes. If this doesn't bother you, could you please send me the disparity map of stillLife you have got, which can help me to verify if I give input images order correctly. And could you please give me your model 7x7 ([email protected]), I'm very interested in the performance evolution when increasing stream length. Thank you for your attention.
Yours sincerely,
Jinglei SHI
from epinet.
Diparity result of stillLife -->stillLife_9x9.zip
Sorry, we have the checkpoint file only with 5x5 and 9x9 viewpoints. I don't know where it is, I couldn't find it... Now I'm re-training the model, and I will upload it soon.
from epinet.
Thank you very much!
from epinet.
Sorry Dr.Shin, I have another question about your paper, in your paper, I found that you compare with method 'Neural EPI-volume Networks' of Stefan Heber, where did you find their code source and dataset? I have searched them but didn't find them. Thank you for your attention.
from epinet.
We emailed him to request their code and dataset, and received the link for the dataset.
from epinet.
Hi Dr. Shin,
Have you ever tried to train the model without excluding reflection and refraction regions? and I found that in your paper, you have removed the textureless regions where the MAD between center pixel and other pixels in a patch is less than 0.02. Do the reflection refraction regions and textureless regions significantly influence the final performance or can they make convergence harder ? Thank you for your attention!
Yours sincerely,
Jinglei
from epinet.
Related Issues (18)
- FAQ: LF image ordering
- Cannot reach the performance of pre-trained model using default code setting HOT 1
- A bug in boolmask HOT 1
- random shift augmentation in training data
- repetitive computation in test data generation
- Can you introduce the color scale method HOT 6
- How to evaluate 490x490 output with light field evaluation-toolkit?
- questions about AttributeError: 'threadsafe_iter' object has no attribute 'shape' HOT 5
- hello,can you provide me with the pytorch version of the code?thank you very muchγ
- disparity label setting after random shift augmentation
- Hello Changha Shin, I want to ask some questions about the training code. HOT 1
- Spelling Error in 'EPINET_plusX_9conv22_save.py' Line 167 HOT 1
- How to generate lytro 9*9 data? HOT 2
- Resolution of output disparity map should be 512x512 in case of synthetic LF data HOT 1
- More details in augmentation would be appreciated HOT 2
- Hello, some question about training code HOT 1
- A little question about epinet_fun/func_generate_traindata.py HOT 1
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