Hi!

1. In the paper, you mentioned 'Detailed network structure of neural projector and the training settings are provided in the supplementary file.', but I can not fild the supplementary file. Would you like to tell me where is the supplementary file?

2. What's more, I tried to train my own dataset with your code for comparison, but I don't know the original file structure so I got some problems. Could you tell me the structure of the original file and the format of the images in the folder?

Thank you very much!

hi ,

i saw your code in data.py have ['depth'] term, but not in distribution_representation.py pkl's save para. Is the 'depth' used in training? and how can i get the 'depth' information.

thanks!

Hi fnzhan: Could you please tell me which illumination map parameters you used as input during the training of the Neural Projector of EMlight, the parameters predicted by Regression network or the parameters of Ground Truth ？

Hi,

congrats for the great work!

I'm trying to run the training code and I'm getting the following error:

(base) root@bd14969643f5:~/codes/Illumination-Estimation/RegressionNetwork# CUDA_VISIBLE_DEVICES=2 python train.py
  + Number of params: 9.50M
0 optim: 0.001
Traceback (most recent call last):
  File "train.py", line 68, in <module>
    for i, para in enumerate(dataloader):
  File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
    data = self._next_data()
  File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/root/codes/Illumination-Estimation/RegressionNetwork/data.py", line 68, in __getitem__
    training_pair['depth'] = torch.from_numpy(gt['depth']).float()
KeyError: 'depth'

I generated the dataset using distribution_representation.py, but I could not find anywhere the depth being added to the list.

Thanks for the help!

Dear authors,

Thanks for opening source your code, and it is really helpful. I have a question about the train/test split of the Laval HDR dataset when I read the paper. It says that 19,556 training pairs are generated from 2,100 HDR panoramas, and 200 images are randomly selected as the test set. I am not sure if the 200 images are selected from the 2,100 HDR panoramas, or just from the 19,556 generated images. If they are from the 19,556 images, how to make sure the corresponding HDR panoramas of them are not in the training set or not seen during the training process?

Best regards, Hao XU

Forgive me, my level is not enough, the input of the first stage of the model is a local picture, then its output is a local light, but I see from the code it should be global, there is no position information, how to achieve this

Hi, fnZhang. Do you reproduce the paper “Fast Spatially-Varying Indoor Lighting Estimation“ as reference, or in any other ways to get the predictions.

I have been trying to get the code to work on generate a rough HDRI from an image input but have had no luck so far, I have downloaded the pretrained model but I am not entirely sure where to start. Can anyone help me?

hi, I have some question about the evaluations matrix. The matrix in the paper was to caculating the loss of render the three different material sphere with predicted light. how to render the three different material sphere with the hdr lighting??

Hello!

I found that you use a TonemapHDR(..) function to tone your images in '\crop' folder. Is it mean that images in '\crop' are just cut from hdr, without any tone process ?

Dear zhan: I am training the regression ,now I happend to the probelms that the intensity output of '*.pth' model is positive number, however convered the onnx model output is negative number. would you like help me sovle the problem?

hi, i have some question about evaluation.

The paper mention that the scenes used in evaluations consist of three spheres with different materials including diffuse gray, matte silver and mirror silver.

Was it bulided with blender?

if it was builded with blender , can you provide the parameters of three different materials ball?

I tried both train and test code on Laval Indoor dataset, and from the test code, I get one result as the image shown above. It seems like the Guassian Map that mentioned in the paper, and I wonder how to reconstruct the environment map from this image.

Thank you so much for your excellent work and I look forward to any reply.

HI~I have some question about REMS and si-REMS. I know the matrix, but i don't know which parameter will be calculated. is the illumination map? I also want to ask that how to calculate the Angular Error.

thanks!

Thanks for your wonderful work. I wonder why you delete the sigmoid in the output layers in DenseNet.py in the latter version? And I guess it will be reasonable if self.fc_dist(out) is followed by softmax , since the sum of gt_distribution is one .(https://github.com/fnzhan/Illumination-Estimation/blob/master/RegressionNetwork/DenseNet.py)

Traceback (most recent call last): File "distribution_representation.py", line 145, in para, map = extractor.compute(hdr) File "distribution_representation.py", line 94, in compute hdr = self.steradian * hdr ValueError: operands could not be broadcast together with shapes (128,256,1) (1024,2048,3)

When I run distribution_representation.py, I encounter the above error, how can I solve it, thank you！