Comments (1)
It should be fairly easy actually. I don't normalize the depth maps at the input since it results in worse performance (which makes sense actually). The preprocessing is quite limited. The most important thing is to make sure that there is no mismatch between the lidar and rgb frames. I ran the code a few months ago, and got the same numbers of the paper, as did a few other people.
Hope this 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
- num_samples should be a positive integer value, but got num_samples=0 HOT 4
- 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
- About the loss function
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from sparse-depth-completion.