Comments (4)
fangchangma
commented on August 23, 2024
Hi Shreyas,
The KITTI depth completion result is from a follow-up paper, and the code will be released later in another repo. For clarity, the "KITTI" dataset used in this repo is actually the KITTI odometry dataset, not the KITTI depth dataset (which had not been released when we started out this project).
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ShreyasSkandan
commented on August 23, 2024
Ahh, I see. However, can I assume that to get the results mentioned in https://arxiv.org/pdf/1709.07492.pdf I can use the parameters mentioned in the paper / default parameters in this repo?
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fangchangma
commented on August 23, 2024
Short answer: yes.
Longer answer: The results in the paper were generated using the Torch implementation, and I haven't done a detailed benchmarking for this pytorch code yet. However, I believe they should be pretty well in line with each other (maybe with some minor differences in accuracy).
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ShreyasSkandan
commented on August 23, 2024
Great, thanks for the information. I appreciate the quick responses!
Good luck on publishing your follow up work.
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Related Issues (20)
- An issue with "resume" mode HOT 2
- [NYU] Different Scaling in Training and Validation HOT 4
- Implementing SLAM
- How is the loss calculated for KITTI dataset ?
- Is there an easy way to run inference on a different dataset HOT 2
- Apply the pretrained model to other datasets HOT 2
- No rgb image normalization during pre-process HOT 1
- Different sparse input when each sample input is loaded HOT 3
- The low download speed in NYU and KITTI
- License for repo
- pose information for processed data
- Benchmark on KITTI vs NYU Depth v2
- Request for pretrained model with depth-only modality
- Failed to reproduce the RGB based problem, whereas the RGBd problem works fine for me.
- How can I use this Git from Windows OS HOT 1
- Using another model
- Scaling factor cancels out for depth values
- The principle of implementing a simple Visual Odometry (VO) algorithm
- Output for custom image
- replace the method of "misc.imresize(img, self.size, self.interpolation, 'F')" HOT 2
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