Comments (6)
Hi Max Shi,
Thanks for your interest in our work. The reason for the different result on the SemanticKITTI validation sequence 08 is that we used the SuMa poses without loop closing for validation. The poses with loop closing lead to a lot of false positives for some scans due to a drift induced by the loop closing in sequence 08.
You can download the poses without loop closing here. Just put them in the same folder as the poses.txt
file and provide the filename when running the inference script by passing the -poses
flag:
python scripts/predict_confidences.py -w path/to/model.ckpt -poses name_pose_file.txt -seq 8
Please let me know if anything is unclear.
Best regards
Benedikt
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Hi Max Shi,
Thanks for your interest in our work. The reason for the different result on the SemanticKITTI validation sequence 08 is that we used the SuMa poses without loop closing for validation. The poses with loop closing lead to a lot of false positives for some scans due to a drift induced by the loop closing in sequence 08.
You can download the poses without loop closing here. Just put them in the same folder as the
poses.txt
file and provide the filename when running the inference script by passing the-poses
flag:
python scripts/predict_confidences.py -w path/to/model.ckpt -poses name_pose_file.txt -seq 8
Please let me know if anything is unclear.
Best regards Benedikt
it work! thank you so much
from 4dmos.
Hi,benemer
I saw your score in the CodaLab, You are the champion and 0.8 is pretty good result. Congratulation!
I guess you may have used SuMa poses without loop in sequence11-21,If you use SuMa poses without loop, can you give me a copy?I want to make little attempt and see the effect.
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Hi @MaxShi007,
Thank you! We achieved this result by training our model using 10 scans with additional training data. The data is taken from the KITTI Road sequences and the labels can be found here. We added all labeled sequences to the training set.
If you want to try the SuMa poses without loop closing, you can download them here.
Best regards
Benedikt
from 4dmos.
Thank you for providing this information about KITTI Road , I will try it.
Here is only have the SuMa poses without loop closing of the trainset(sequence 00-07,09-10) and the validset(sequence 08). Do you have the SuMa poses without loop closing of the testset(sequence 11-21)?
from 4dmos.
Ah sorry, you are right. Here are the SuMa poses on the test set without loop closing:
If I remember correctly, there were a lot of false positives on sequence 20 due to some inconsistent poses (even without loop closing).
Best,
Benedikt
from 4dmos.
Related Issues (20)
- Custom visualization - mismatch data shape HOT 1
- Does using closed loop poses have a significant impact on recognition results? HOT 4
- Inference problem HOT 2
- evaluate problem HOT 14
- predict confidences when using pose
- Evaluation on 16 channel LiDAR pointcloud HOT 3
- Model adaption for Movable object segmentation HOT 1
- About the gap between testing result and validation result on SemanticKITTI HOT 5
- Docker build issue HOT 2
- Deployment HOT 7
- Does it have scan_cleaner utils like LM-Net? HOT 6
- Deploying 4DMOS on Robots HOT 6
- Results regarding Apollo HOT 2
- Performane on trained weights HOT 13
- 4DMOS in Nuscenes HOT 2
- some questions about MinkowskiEngine HOT 1
- UserWarning: GPUs can't support floating point data HOT 3
- Was able to run everything. All of a sudden, I got this error. TypeError: expected str, bytes or os.PathLike object, not NoneType" HOT 2
- Which sequence is used as the validation sequence for the Apollo dataset HOT 1
- Apollo dataset fine tune loss nan HOT 1
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