Robust3DOD
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Eaphan
A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks [IJCV2023]
In Section 5.3 in paragraph _Robustness Measurement_, you defined the mAP ratio as " the ratio of IoU on adversarial examples to that on clean point cloud over the whole...
Hello, thank you for sharing. The appendix at the end of the paper visualizes a bird's-eye view of the point cloud and its 3D detection box and ground-truth bounding box...
I am trying to run your code and after following all the guides i reached a point where i am getting an error in your attack.py script ------------------------------------------------- Traceback (most...
Dear authors, Thank you for sharing the official implementation of your work. I'm particularly interested in reproducing the adversarial training results using your Balanced Adversarial Focal Training (BAFT) approach. I've...
Thank you for your work,sir. After confirming that the dataset and .pth file , I cannot run attack.py directly using the environment code you provided. However, I can run attack.py...
Thank you for your work and open source. I want to know how I can test it on the Target Model after I generate adversarial samples on the Source Model...
Thank you for your work and open source code. I would like to know how the mAP ratio of KITTI is calculated in the article? Is it just the relative...