A teaser version of the code for the paper Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection.
It contains a cpp file used for kitti offline evaluation, compile it with
g++ -o evaluate_object_3d_offline evaluate_object_3d_offline.cpp -lboost_system -lboost_filesystem
and use it same as the original kitti evaluation
./evaluate_object_3d_offline ~/Kitti/object/training/label_2/ [your-path-to-predictions-in-KITTI-format]
It will generate files hack files at the result folder
Use the python file write_JIOU.py to print results. Change the paths in the file before usage.
There are several implicit parameters:
- If "waymo" exists in the prediction data path, it will read the data in a format different from KITTI.
- If "unc" exists in the prediction data path, it will try to read the probabilistic prediction in the format of the paper Hujie20
- If "uncertainty" exists in the prediction data path, it will try to read the probabilistic prediction in the format of ProbPIXOR proposed in paper Di19
The IO of probabilistic prediction result is a disaster. The data format are not unified and vary from paper to paper. We have two kinds of
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