3d_harmonic_loss_for_object_detection
3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for V2X Orchestration
https://github.com/xjtu-haolin/3d_harmonic_loss_for_object_detection
Science Score: 54.0%
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○DOI references
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Low similarity (7.9%) to scientific vocabulary
Repository
3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for V2X Orchestration
Basic Info
Statistics
- Stars: 9
- Watchers: 1
- Forks: 2
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
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 (IEEE T-VT)
Our implementation is relied on mmdetection3D
Environment
python = 3.7
pytorch = 1.6
CPU: i7-10700K
GPU: RTX-2080Ti
other requriements are same as in mmdetection3D
Dataset Preparation
Please follow the 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 to know how to configurate configs and train models.
Training (OpenPCDet)
will be released soon...
Test
You can follow official document of mmdetection3D to test models.
TODO Lists
- [X] Readme Completion
- [X] Paper Preprinted
- [X] Support KITTI Dataset
- [X] Support Waymo Dataset
- [ ] Support OpenPCDet benchmark
Owner
- Name: Haolin Zhang
- Login: XJTU-Haolin
- Kind: user
- Repositories: 2
- Profile: https://github.com/XJTU-Haolin
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - name: "MMDetection3D Contributors" title: "OpenMMLab's Next-generation Platform for General 3D Object Detection" date-released: 2020-07-23 url: "https://github.com/open-mmlab/mmdetection3d" license: Apache-2.0
GitHub Events
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Dependencies
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- docutils ==0.16.0
- m2r *
- mistune ==0.8.4
- myst-parser *
- sphinx ==4.0.2
- sphinx-copybutton *
- sphinx_markdown_tables *
- mmcv-full >=1.4.8,<=1.6.0
- mmdet >=2.24.0,<=3.0.0
- mmsegmentation >=0.20.0,<=1.0.0
- open3d *
- spconv *
- waymo-open-dataset-tf-2-1-0 ==1.2.0
- mmcv >=1.4.8
- mmdet >=2.24.0
- mmsegmentation >=0.20.1
- torch *
- torchvision *
- lyft_dataset_sdk *
- networkx >=2.2,<2.3
- numba ==0.53.0
- numpy *
- nuscenes-devkit *
- plyfile *
- scikit-image *
- tensorboard *
- trimesh >=2.35.39,<2.35.40
- asynctest * test
- codecov * test
- flake8 * test
- interrogate * test
- isort * test
- kwarray * test
- pytest * test
- pytest-cov * test
- pytest-runner * test
- ubelt * test
- xdoctest >=0.10.0 test
- yapf * test