Inferring Spatial Uncertainty in Object Detection

A teaser version of the code for the paper Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection.

Usage

C++ evaluation first

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

Python visualization after evaluation

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

Hints

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

LICENSE

BSD License – Berkeley

Copyright (c) 2020 Zining Wang

All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

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• Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

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