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[ICRA 2021] FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection

License: MIT License

Python 99.34% Shell 0.66%
3d-object-detection deep-learning geometric weakly-supervised-object-detection

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fgr's Issues

Line Numbers to edit in PCDet code before training


pcdet.datasets.kitti.kitti_dataset:

  1. line between 142 and 143, add: "if len(obj_list) == 0: return None"
  2. line after 191, delete "return list(infos)", and add:

final_result = list(infos)
while None in final_result:
final_result.remove(None)

return final_result


The above line numbers seem incorrect. Can you update the README file?

About the object's orientation

Hi @weiyithu , thank you for your great work. Your algorithm works very well in KITTI dataset.

However, in your paper, you didn't mention how to estimate the object's orientation (heading-specific). As far as I understand, if we can determine the rotation angle 'phi' of the bounding box in bird-eye-view, the final object's orientation can be 'phi' or 'phi + pi' because we do not know about the heading of the object. How can you resolve this scenario? Thank you.

regarding the evaluation by mAP

Hi Weiyi, thanks for sharing your great work.

I was wondering if I overlooked some codes for calculating the metric of mAP. Could you give me more information on where I could find the mAP code in this repo. Thank you for your time, any response will be appreciated.

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