Hi. First of all thank you for your amazing work. Is there any way you could get a metric distance between the camera and the object from the depth map outputted by this work? I get how you could get the distance when you have a disparity map, but I'm not quite sure if the same concept applies here. Thank you

Hi there! Thanks for sharing this fantastic work :).

Due to I have a small dataset, the available encoder networks are too large for my purpose. Therefore, I would like to use a smaller ResNet-18 instead. However, I am struggling with the code. This is the code for ResNet-101:

https://github.com/tjqansthd/LapDepth-release/blob/2ea5f29d2e117e765e9da97984bbc5b6de0eddcb/model.py#L338-L383 What would it be like for a ResNet-18?

Cheers!

Hi, very great work! but I met a problem when training. The output gradient remains 0 when I train on the kitti dataset(I did not change any code), have you met this problem before?

Hello,

when I run "aria2c -x 16 -i ./datasets/kitti_archives_to_download.txt", its appear "No files to download." I have checked that the path is correct. I can't seem to get the raw dataset...

Hi, I would like to ask about the feasibility of training a model from the code provided using generated dataset. I have a set of RGB images obtained from several open-source data and I have passed the dataset into the model provided in https://github.com/compphoto/BoostingMonocularDepth to produce ground truth. If I were to use this "generated" dataset, what should be the depth scale and maximum depth I should input? I notice that there is a fixed depth scale and maximum depth for NYU and KITTI datasets in the code such as self.depth_scale = 256.0 in line 24 of datasets_list.py

Thanks for your great work. I'd like to konw the difference between gt and dense gt. I get the data by carla simulator， so I get the gt with float32. Can I set the same ground truth with gt and dense gt?

hello, when I run train.py, the code reaches line 54 “args.save_path.makedirs_p()”，Such an error occurred：OSError: [Errno 22] Invalid argument: Path('checkpoints/sync/04-13-10:14') I have been unable to solve this problem......

Hello,I don't understand how to get the real distance from the "out" in demo.py .What model I used is trained from KITTI.Could you please tell me how to get it ?

Hello,

I just run the train.py script and I am getting the following error:

File "/home/raai/sparse-to-dense/train_main.py", line 356, in main()

File "/home/raai/sparse-to-dense/train_main.py", line 269, in main model = LDRN()

File "/home/raai/sparse-to-dense/model_lapdepth.py", line 912, in init self.decoder = Lap_decoder_lv6(self.encoder.dimList)

File "/home/raai/sparse-to-dense/model_lapdepth.py", line 740, in init self.ASPP = Dilated_bottleNeck_lv6(norm, act, dimList[4])

IndexError: list index out of range

Thank you very much for your outstanding work. I am very interested in your work, but I have a doubt about how to get the real depth map mentioned in your paper. The real depth map of Kitti dataset is sparse。

Hi, thank you for sharing your excellent work. Please how to evaluate the depth image for any real-world images. I can try the command line "python eval.py --model_dir ./pretrained/LDRN_KITTI_ResNext101_pretrained_data.pkl --evaluate --batch_size 1 --dataset KITTI --data_path ./datasets/KITTI --gpu_num 0 --img_save". However, it cannot work. If you have some good suggestions, please let me know. I appreciate your help.

I am currently trying the network trained on the NYU dataset on a separate dataset (TUM RGBD desk see: https://vision.in.tum.de/data/datasets/rgbd-dataset/download#freiburg2_desk)

I would have expected to get a similar performance but the RMSE is significantly higher (TUM: 0.73 vs NYU: 0.38)

Both datasets were recorded using Microsoft Kinect Sensors and are comprised of indoor scenery.

I am trying to figure out why the model cannot generalize well to the TUM dataset.

I build my own data loading logic with follows the same steps as the one defined for the NYU dataset:

 def predict(img_path):
  img = Image.open(img_path)
  img = img.crop((40,42,616,474))
  img = np.asarray(img, dtype = np.float32) / 255
  img = img.transpose((2, 0, 1))
  img = torch.from_numpy(img)
  img = normalize(img)

  img.cuda()
  _, org_h, org_w = img.shape
  img = img.unsqueeze(0)
  img_flip = torch.flip(img,[3])

  with torch.no_grad():
      _, out = Model(img)
      _, out_flip = Model(img_flip)
      out_flip = torch.flip(out_flip,[3])
      out = 0.5*(out + out_flip)

  pred = out[0,:,:]
  return pred

Do you have any idea why the performance varies so drastically while using the same sensors and similar scenery?

Also I would appreciate if you could help me understand the different image crops in the evaluation: Prediction: pred_uncropped[42:474, 40:616] = pred

Mask for groundtruth and prediction: crop_mask[45:471, 41:601] = 1

Thank you for any help & guidance!