First please download the SemanticPOSS Dataset from the official website.
Then download our predictions from Google Drive.
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Visualize GT:
python visualize.py -d /dataset -s sequences:00-06
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Visualize Our Predictions:
python visualize.py -d /dataset -p /predictions -s 02/03
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In the visualization:
To navigate: b: back (previous scan) n: next (next scan) q: quit (exit program)
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Eval Seq 02:
python evaluate_iou.py -d /dataset -p /predictions -s valid02
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Eval seq 03:
python evaluate_iou.py -d /dataset -p /predictions -s valid03
The code is partly based on LiDAR-Bonnetal and SalsaNext. Thanks for their open source work.
Currently, please consider citing:
@inproceedings{pan2020semanticposs,
title={Semanticposs: A point cloud dataset with large quantity of dynamic instances},
author={Pan, Yancheng and Gao, Biao and Mei, Jilin and Geng, Sibo and Li, Chengkun and Zhao, Huijing},
booktitle={2020 IEEE Intelligent Vehicles Symposium (IV)},
pages={687--693},
year={2020},
organization={IEEE}
}