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Detection

Requirements

  1. Clone the repository
git clone https://github.com/kaist-avelab/K-Radar.git
cd K-Radar
  1. Create a conda environment
conda create -n kradar python=3.8.13 -y
conda activate kradar

  1. Install PyTorch (We recommend pytorch 1.11.0.)

  2. Install the dependencies

pip install -r requirements.txt
  1. Build packages for Rotated IoU
cd utils/Rotated_IoU/cuda_op
python setup.py install
  1. Modify the code in packages
Add line 11: 'from .nms import rboxes' for __init__.py of nms module.
Add line 39: 'rrect = tuple(rrect)' and comment line 41: 'print(r)' in nms.py of nms module.
  1. Build packages for OpenPCDet operations
cd ../../../ops
python setup.py develop
  1. Unzip 'kradar_revised_label_v2_0.zip' in the 'tools/revise_label' directory (For the updated labeling format, please refer to the dataset documentation.)

We use the operations from OpenPCDet repository and acknowledge that all code in ops directory is sourced from there. To align with our project requirements, we have made several modifications to the original code and have uploaded the revised versions to our repository. We extend our gratitude to MMLab for their great work.

Train & Evaluation

  • To train the model, prepare the total dataset and run
python main_train_0.py
  • To evaluate the model, modify the path and run
python main_val_0.py (for evaluation)
python main_cond_0.py (for conditional evaluation)
  • To visualize the inference result, modify the path and run
python main_test_0.py (with code)
python main_vis.py (with GUI)

Model Zoo

The reported values are ${AP_{3D}}$ for Sedan class. (based on the label v1.0)

Name Total Normal Overcast Fog Rain Sleet LightSnow HeavySnow Pretrained Logs
RTNH 47.4 49.9 56.7 52.8 42.0 41.5 50.6 44.5 Link Link
RTN 40.1 45.6 48.8 46.9 32.9 22.6 36.8 36.8 Link Link
PILLARS 45.4 52.3 61.0 42.2 44.5 22.7 40.6 29.7 Link Link

Based on our findings presented, we elucidate the capability of the 4D Radar in terms of 3D object detection and its resilience in the face of adverse weather conditions. This is evident from its relatively stable performance under conditions of sleet and heavy snow, especially when compared to the performance metrics of LiDAR neural networks. We do not claim that 4D Radar is always superior to LiDAR in 3D object detection. In other words, we note that the performance of LiDAR neural networks could surpass that of the 4D Radar neural network with improvements in its structure or hyper-parameters.

Performance of RTNH on wider areas and multiple classes (Sedan & Bus or Truck)

The performance metrics presented below have three main discrepancies compared to the results reported earlier (found in Appendix H and Section 4 of the K-Radar paper, respectively):

  1. The evaluation now considers a broader area (with x, y, z each ranging from 0 to 72, -16 to 16, and -2 to 7.6 meters), unlike the narrower range (x, y, z each from 0 to 72, -6.4 to 6.4, and -2 to 6 meters) used previously.
  2. The classes have been expanded to include not only the Sedan class but also the Bus or Truck class, representing compact and large vehicles, respectively.
  3. The performance analysis has been updated to incorporate the version 2.0 labels. For changes relative to the version 1.0 labels, please refer to the Revising K-Radar label section.

For a more detailed performance review (e.g., performance for each road environment), please refer to the logs at this link: available at log_test_RTNH_wide_11epoch.zip.

For the model below, please check the configuration in configs/cfg_RTNH_wide.yml and the pretrained model in the Google Drive. (We have updated the RTNH head code within the same anchor head format provided by OpenPCDet to enhance its stability and compatibility. In addition, we denote the performance of the Bus or Truck class with '-' in both Rain and Sleet conditions, as they do not exist in these conditions.

(1) ${AP_{3D}}$

Class Total Normal Overcast Fog Rain Sleet LightSnow HeavySnow
Sedan 48.2 45.5 58.8 79.3 40.3 48.1 65.6 52.6
Bus or Truck 34.4 25.3 31.1 - - 28.5 78.2 46.3

(2) ${AP_{BEV}}$

Class Total Normal Overcast Fog Rain Sleet LightSnow HeavySnow
Sedan 56.7 53.8 68.3 89.6 49.3 55.6 69.4 60.3
Bus or Truck 45.3 31.8 32.0 - - 34.4 89.3 78.0