3D_Harmonic_Loss_for_Object_Detection

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3D_Harmonic_Loss_for_Object_Detection

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3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for V2X Orchestration

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# 3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for V2X Orchestration
  
## Notification

Our proposed **3D Harmonic Loss** can be applied to many lidar-based 3D object detection methods for solving inconsistency problem as shown below.

Paper in IEEE: [PDF](https://ieeexplore.ieee.org/document/10173488/) (IEEE T-VT)

Our implementation is relied on [mmdetection3D](https://github.com/open-mmlab/mmdetection3d)

## Environment    
python = 3.7  
pytorch = 1.6    
CPU: i7-10700K  
GPU: RTX-2080Ti  
other requriements are same as in [mmdetection3D](https://github.com/open-mmlab/mmdetection3d)

## Dataset Preparation  
Please follow the  [mmdetection3D](https://github.com/open-mmlab/mmdetection3d) to convert KITTI Dataset and Waymo Dataset

## Training (MMdetection3D)
Note that our 3D harmonic loss optimization can be implemented to train almost all anchor-based 3D detectors without inference time cost.
Please get familar with mmdetection3D-format config in advance, and then you can check `configs/_base_/models/hv_second_secfpn_kitti_harmonic_loss.py` as an example to customize other model configs, and use our revised anchor-head with harmonic loss for 3D detectors. You can follow official document of [mmdetection3D](https://github.com/open-mmlab/mmdetection3d) to know how to configurate configs and train models.

## Training (OpenPCDet)   
will be released soon...

## Test
You can follow official document of [mmdetection3D](https://github.com/open-mmlab/mmdetection3d) to test models.

## TODO Lists
- [X] Readme Completion
- [X] Paper Preprinted
- [X] Support KITTI Dataset
- [X] Support Waymo Dataset
- [ ] Support OpenPCDet benchmark

3D_Harmonic_Loss_for_Object_Detection

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