Comments (1)
We randomly sample the positive pairs (e.g. <3m) and negative pairs (e.g. >20m) by a ratio (e.g. 1:100) for training. And for evaluation, we remove the easy positive pairs (e.g. <3m & timestamps < 30s). You can refer to the old version code or this repo.
Note that the ratio really matters when compared with other methods (Fig.6 in IROS'21 paper). Please keep all methods compared strictly on the same data distribution.
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Related Issues (19)
- Missing code HOT 3
- about the classification part HOT 2
- How to visualize the Fig.7 HOT 1
- about the testing and training HOT 2
- about the pre-processing HOT 2
- pair_list issue HOT 1
- Ask questions about data usage HOT 3
- about how to get pre-processing data format HOT 1
- How can I use multiple GPUs for training HOT 2
- About Comparative Experiment HOT 1
- Codes wanted HOT 2
- Time for training HOT 1
- Dalao, how to get graphs_rn and graphs_sk with rangenet++ and Kitti dataset ? HOT 3
- dataset HOT 4
- 2 bugs about gpu_id HOT 1
- distance error: HOT 4
- A question about the code in sg_net.py HOT 4
- How should I modify the training params to achieve the accuracy in your paper? HOT 5
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