Comments (1)
Hi @mzy97,
It would surprise that normalization makes a huge difference for the end result after training (on so many labels). Networks nowadays are more than capable enough to learn this constant. In short, don't spend too much time on it :). Hope it helps.
Best,
Wouter
from sparse-depth-completion.
Related Issues (20)
- Testing with other KITTI sequences or other data HOT 8
- How did you render the results shown in video? HOT 2
- Size of image from an imported folder HOT 7
- Can't load the model HOT 4
- Incorrect depth estimation when there is low LiDAR density HOT 6
- Training conditions HOT 2
- Trying to create tensor with negative dimension -2: [-2, 34, 3, 3] HOT 1
- try to run /Download/download_raw_files.sh HOT 3
- Evaluating your pretrained model HOT 17
- Can't run the code HOT 3
- Confusions about dataset HOT 4
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- Dataset Prepare HOT 2
- The problem about RGB dataset HOT 1
- why the localNet not use global depth prediction as input HOT 4
- Script for only prediction
- Code stuck at testing
- About pretrained model on only trained with sparse data
- About data preprocessing
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from sparse-depth-completion.