ZixunH/DTTD2-IPhone

DTTD-Mobile

🤔 Are current 3D object tracking methods truely robust enough for low-fidelity depth sensors like the iPhone LiDAR?

This repository contains an example dataset.py and simulated file structure within root. It is the dataset for the 2025 CVPRW paper Robust 6DoF Pose Estimation Against Depth Noise and a Comprehensive Evaluation on a Mobile Dataset.

🚩 Project page: https://openark-berkeley.github.io/DTTDNet/

🦾 Extension to Robotic Tasks: https://huggingface.co/datasets/ZixunH/DTTD3_Impedance

🏆 Leaderboard Embedded: https://paperswithcode.com/dataset/dttd2

🧸 You can also download our 6-DoF pose estimation network and the ckpt from: https://github.com/augcog/DTTD2

📁 Folder Structure:

Robust-Digital-Twin-Tracking
├── checkpoints
│   ├── m8p4.pth
│   ├── m8p4_filter.pth
│   └── ...
|   ...
└── dataset
    └── dttd_iphone
        ├── dataset_config
        ├── dataset.py
        └── DTTD_IPhone_Dataset
            └── root
                ├── cameras
                │   ├── az_camera1 (if you want to train our algorithm with DTTD v1)
                │   ├── iphone14pro_camera1
                │   └── ZED2 (to be released...)
                ├── data
                │   ├── scene1
                │   │   └── data
                │   │   │   ├── 00001_color.jpg
                │   │   │   ├── 00001_depth.png
                │   │   │   └── ...
                |   │   └── scene_meta.yaml
                │   ├── scene2
                │   │   └── data
                |   │   └── scene_meta.yaml
                │   ...
                └── objects
                    ├── apple
                    │   ├── apple.mtl
                    │   ├── apple.obj
                    │   ├── front.xyz
                    │   ├── points.xyz
                    │   ├── ...
                    │   └── textured.obj.mtl
                    ├── black_expo_marker
                    └── ...

If our work is useful or relevant to your research, please kindly recognize our contributions by citing our papers:

@inproceedings{huang2025robust,
  title={Robust 6DoF Pose Estimation Against Depth Noise and a Comprehensive Evaluation on a Mobile Dataset},
  author={Huang, Zixun and Yao, Keling and Zhao, Zhihao and Pan, Chuanyu and Yang, Allen},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={1848--1857},
  year={2025}
}