6D Pose Estimation via Keypoint Heatmap Regression with RGB-D Residual Neural Networks

This repository provides the official implementation of our Machine Learning & Deep Learning project focused on 6D object pose estimation.

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📂 Structure

.
├── data/                                                                        # images, depth maps, labels, and keypoints
├── docs/                                                                        # project report and related documents
│   ├── s337769_s337006_s344174_s342583_ALJOSEVIC_ALMASI_PAROVIC_SHAFIEI.pdf     # project report
│   └── instructions.pdf                                                         # instructions of project structure and execution
├── models/                                                                      # trained model checkpoints
│   ├── resnet/                                                                  # ResNet-based models
│   ├── yolov10m/                                                                # YOLO model artifacts
│   └── yolov10m.pt                                                              # pretrained YOLO weights
├── notebooks/                                                                   # Jupyter notebooks for pipeline phases
│   └── end_to_end/                                                              # complete pipeline notebook and modules
├── requirements.txt                                                             # Python dependencies
└── README.md                                                                    # project overview

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🚀 Getting Started

Step 1: Clone the Repository

git clone https://github.com/emirmasood/HeatNet.git
cd HeatNet

Step 2: Install Dependencies

pip install -r requirements.txt

Step 3: Download Data and Models

Due to GitHub's file size restrictions, download large files separately:

• Dataset: Google Drive Data Folder
• ResNet Checkpoints: ResNet Checkpoints
• YOLOv10m pretrained weights: YOLOv10m Checkpoint

Place downloaded files into their respective folders as indicated in the folder structure above.

Step 4: Run the Full Pipeline

jupyter lab notebooks/end_to_end/ph5_01_end_to_end.ipynb

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👥 Authors

This project was created by:

Ismail Aljosevic (ismail.aljosevic@studenti.polito.it)

Amir Masoud Almasi (amirmasoud.almasi@studenti.polito.it)

Ana Parovic (ana.parovic@studenti.polito.it)

Ashkan Shafiei (ashkan.shafiei@studenti.polito.it)