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06-17-2025 (V1.3)

Maintainer: Jack Shilton

  • Refactored codebase to simplify setup and execution on custom data.
  • Improved training feedback and logging: enhanced console printouts with multiline updates, terminal clearing between iterations, detailed elapsed/ETA times, and integrated TensorBoard logging (tensorboard --logdir data/output/logs) in 3.deep_model.
  • Added multithreaded file processing support with progress indicators for parallel tasks in folder 2.point_cloud_generation.
  • Updated README with QuickStart guide and usage instructions.

08-08-2021 (V1.2)

  • Release DataCaptureDemo_1843new.lua in folder 1.mmWave_data_capture.
  • Add a simple tutorial to explain how to enable real-time data steaming.
  • The structure of the added folder is as following:
├── 1.mmWave_data_capture
│   ├── DataCaptureDemo_1843new.lua

08-08-2021 (V1.1)

  • Release code in folder 2.point_cloud_generation for the point cloud generation from binary file of mmWave radar (no packet head)
  • The structure of the added folder is as following:
├── 2.point_cloud_generation
│   ├── configuration.py
│   └── pc_generation.py
  • Change the folder name from 2.deep_model to 3.deep_model

06-30-2021 (V1.0)

  • Release the repository as following:
├── 0.preliminary
│   └── extract_SMPL_model.py
├── 1.mmWave_data_capture
│   ├── capture.py
│   └── steaming.py
├── 2.deep_model
│   ├── data.py
│   ├── infer_model.py
│   ├── network.py
│   ├── smpl_utils_extend.py
│   └── train_model.py
├── HISTORY.md
├── LICENSE
├── README.md
└── .gitignore
  • Release code in folder 0.preliminary to generate the SMPL model in the training of mmMesh model.
  • Release code in folder 1.mmWave_data_capture for real-time mmWave radar steaming.
  • Release code in folder 2.deep_model for the training and inference of the deep model in mmMesh.