Comments (4)
The downsampling process is non-trivial. We kept a subset of the laser scans, which were then projected onto the image plane. However, since some lidar measurements were missing in the raw file (due to being out of sensor range), it is a bit tricky to separate the scans in a clean way.
We might release the downsampled dataset / the code later
from self-supervised-depth-completion.
ok,thanks
from self-supervised-depth-completion.
The downsampling process is non-trivial. We kept a subset of the laser scans, which were then projected onto the image plane. However, since some lidar measurements were missing in the raw file (due to being out of sensor range), it is a bit tricky to separate the scans in a clean way.
We might release the downsampled dataset / the code later
Is the downsampled dataset / the code available now?
Thanks!
from self-supervised-depth-completion.
Expecting the release of downsampling code.
Thanks!
from self-supervised-depth-completion.
Related Issues (20)
- Error while loading "calib_cam_to_cam.txt" - can not reshape the array.
- question about depth-estimation results HOT 2
- What is the network used for single d?
- Why I can't get the result when using the trained model you provided?
- How can I get the result in your paper?
- About extracting trained model HOT 2
- Clip output in model.py
- inference HOT 2
- colorize the depth map HOT 1
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- Use your pretrained model: GPU run out of memory. 8.95 gb already allocated
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- dataset extracting
- Training doesn't converge HOT 4
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- Running Error in train mode sparse+photo HOT 1
- To much warning. HOT 2
- Use Stereo Pair Instead of Temporal Pair for Self-Supervised Training?
- The result cannot be reproduced
- Some questions about the details of the code
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from self-supervised-depth-completion.