Comments (4)
Can you please share the links for pretrained models of your experiments in the paper?
The pretrained model has been uploaded. You can get it from 'https://github.com/gpcv-liujin/REDNet/blob/master/MODEL_FOLDER/MODEL_FOLDER.zip'
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Thanks. I didn't notice that earlier.
I want to ask one more question, in your paper you showed the colmap results. Did you use ground truth extrinsic/instrinsic parameters to generate point clouds for WHU data?
And also if you can tell how are you visualizing the depth maps from COLMAP.
Many thanks.
from rednet.
Thanks. I didn't notice that earlier.
I want to ask one more question, in your paper you showed the colmap results. Did you use ground truth extrinsic/instrinsic parameters to generate point clouds for WHU data?
And also if you can tell how are you visualizing the depth maps from COLMAP.
Many thanks.
Sorry I just noticed your reply.
I used ground truth camera parameters as input, so that the depth results obtained by colmap can be compared with the provided ground truth.
The output depth maps are stored in the directory of "dense/stereo/depth_maps/" in the colmap project. The depth map results are stored in "bin" format, so I reloaded the "bin" file and visualized it using python.
from rednet.
thanks for your response.
- I ran the pretrained model on test set. For a particular image the predicted depth map from REDNet and GT depth map looks similar. But the range of predicted Depth map is in (493.5945, 526.7905) and GT map is in (123,131). So do I need to do any post-processing to compute the L1 distance between these two maps?
- In Colmaps's "dense/stereo/depth_maps/" folder two depth maps get stored one is photometric and one is geometric. Can you please tell which one is you are using?
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Related Issues (16)
- Link to paper HOT 1
- Can't access WHU MVS dataset HOT 4
- 数据集制作 HOT 2
- WHU数据集的评价指标 HOT 1
- 关于viewpair.txt HOT 1
- 请问深度值是如何获取的?
- How to make camera files?
- The graph couldn't be sorted in topological order. HOT 2
- visualization HOT 1
- Cannot find the pre-trained REDNet model HOT 2
- 数据集中的模型 HOT 1
- Unable to access the data HOT 1
- Unable to reproduce the results HOT 2
- python ./predict.py has error HOT 2
- 无法重现结果 HOT 3
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