Comments (5)
Hi @zazgf,
It depends on the sparsifier argument passed to the dataset construction (the name itself is a bit confusing). So if the sparsifier is "radar", then the inputs contain the concatenation of images and raw radar depth maps. If it's "radar_filtered", then the inputs are composed by images and filtered radar depth maps instead.
Therefore, as you can see in README.md, the sparsifier is set to "radar".
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With reading the program, I learned a lot, Thank you very much for releasing the code.
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Hi @zazgf ,
The part you mentioned is actually in the data loader and that part was used to generate different sparse patterns for the experiments in Table 2 from our paper. So you can generate (1) normal radar measurements, (2) radar points filtered by the ground truth LiDAR scans, or (3) radar points filtered by the trained classifyer, etc. In the main experiment, we use (1), so there should be no concern related to "using lidar data to do the filtering".
from radar_depth.
@brade31919 Thanks for your reply.
Does this mean that I can achieve the project function without lidar?
Thanks again.
from radar_depth.
@brade31919 Get your point, I didn't realize this before.I need pay more attention on code reading. Thanks.
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Related Issues (19)
- Awesome! HOT 1
- Evaluation error args = checkpoint['args'] HOT 7
- Visualization HOT 1
- About the pretrained argument HOT 2
- Can not evaluate provided pretrained models HOT 1
- Question about ref_chan in radar multisweep HOT 1
- max_depth or max-depth?
- can you release your dataset? HOT 1
- Evaluation of Pretrained Models
- Detailed documentation for the processed dataset.
- Release date HOT 13
- How to obtain ground truth depth image? HOT 3
- About ver2 in code HOT 2
- Questions about hyperparameters and processed dataset HOT 7
- Question on the RGB - Radar alignment issue HOT 5
- Bidirectional radar sweeps HOT 2
- About train / val scene numbers HOT 2
- Own dataset make method HOT 2
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