DTTD: Digital Twin Tracking Dataset Official Repository

Overview

Please refer to the updated project repo for latest updates!

This repository is the implementation code of the paper "Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications" (arXiv, paper, video). Our work is accepted to CVPR 2023 Workshop on Vision Datasets Understanding.

In this work, we create a RGB-D dataset, called Digital-Twin Track-ing Dataset (DTTD), to enable further research of the problem to extend potential solutions to longer-range in a meter scale. We select Microsoft Azure Kinect as the state-of-the-art time-of-flight (ToF) camera. In total, 103 scenes of 10 common off-the-shelf objects with rich textures are recorded, with each frame annotated with a per-pixel semantic segmentation and ground-truth object poses provided by a commercial motion capturing system. We also provide source code in this repository as references to data generation and annotation pipeline in our paper.

Recent Update

• 10/10/2023: We introduce a continuous work of DTTD, "Robust Digital-Twin Localization via An RGBD-based Transformer Network and A Comprehensive Evaluation on a Mobile Dataset" (arXiv, project page), which proposes a novel depth-robust pose estimator as well as an iPhone Dataset (DTTDv2).
• 06/28/2023: DTTDv1 (Azure Kinect) & DTTDv2 (iPhone) released at here.

Dataset File Structure

DTTD_Dataset
├── train_data_list.txt
├── test_data_list.txt
├── classes.txt
├── cameras
│   ├── az_camera1
│   └── iphone12pro_camera1 (to be released...)
├── data
│   ├── az_new_night_1
│   │   └── data
│   │   │   ├── 00001_color.jpg
│   │   │   ├── 00001_depth.png
│   │   │   ├── 00001_label_debug.png
│   │   │   ├── 00001_label.png
│   │   │   ├── 00001_meta.json
│   │   │   └── ...
|   |   └── scene_meta.yaml
│   ├── az_new_night_2
│   │   └── data
|   |   └── scene_meta.yaml
|   ...
|
└── objects
    ├── apple
    │   ├── apple.mtl
    │   ├── apple.obj
    │   ├── front.xyz
    │   ├── points.xyz
    │   ├── textured_0_etZloZLC.jpg
    │   ├── textured_0_norm_etZloZLC.jpg
    │   ├── textured_0_occl_etZloZLC.jpg
    │   ├── textured_0_roughness_etZloZLC.jpg
    │   └── textured.obj.mtl
    ├── black_expo_marker
    ├── blue_expo_marker
    ├── cereal_box_modified
    ├── cheezit_box_modified
    ├── chicken_can_modified
    ├── clam_can_modified
    ├── hammer_modified
    ├── itoen_green_tea
    ├── mac_cheese_modified
    ├── mustard_modified
    ├── pear
    ├── pink_expo_marker
    ├── pocky_pink_modified
    ├── pocky_red_modified
    ├── pocky_white_modified
    ├── pop_tarts_modified
    ├── spam_modified
    ├── tomato_can_modified
    └── tuna_can_modified

Requirements

Before running our data generation and annotation pipeline, you can activate a conda environment where Python Version >= 3.7:

conda create --name [YOUR ENVIR NAME] python = [PYTHON VERSION]
conda activate [YOUR ENVIR NAME]

then install all necessary packages:

pip install -r requirements.txt

Code Structure

• calculate_extrinsic: extrinsic information
• cameras: camera information
• data_capturing: helper package for data capturing
• data_processing: helper package for data processing
• demos: demo videos
• doc: demo images
• extrinsics_scenes: folder to save all extrinsic scenes
• iphone_app: iPhone app development for capturing RGBD images for iPhone 12 Pro camera
• manual_pose_annotation: helper package for pose annotation
• models: baseline deep learning 6D pose estimation algorithms
• objects: object models that we use in DTTD (with corresponding scale and texture)
• pose_refinement: helper package for pose refinement
• quality_control: helper package for reviewing manual annotations
• scene_labeling_generation: helper package for generating labels
• scenes: folder to save all recorded RGBD data
• synthetic_data_generation: helper package for generating synthetic data
• testing: package to test aruco marker's appearance, extrinsic's validity, etc.
• toolbox: package to generate data for model training
• tools: commands for running the pipelines. Details in tools/README.md.
• utils: utils package

What you Need to Collect your own Data

1. OptiTrack Motion Capture system with Motive tracking software
   • This doesn't have to be running on the same computer as the other sensors. We will export the tracked poses to a CSV file.
   • Create a rigid body to track a camera's OptiTrack markers, give the rigid body the same name that is passed into tools/capture_data.py
2. Microsoft Azure Kinect
   • We interface with the camera using Microsoft's K4A SDK: https://github.com/microsoft/Azure-Kinect-Sensor-SDK
3. iPhone 12 Pro / iPhone 13 (to be released...)
   • Please build the project in iphone_app/ in XCode and install on the mobile device.

Data Collection Pipeline (for Azure Kinect)

Link to tutorial video: https://youtu.be/ioKmeriW650.

Configuration & Setup

1. Place ARUCO marker somewhere visible
2. Place markers on the corners of the aruco marker, we use this to compute the (aruco -> opti) transform
3. Place marker positions into calculate_extrinsic/aruco_corners.yaml, labeled under keys: quad1, quad2, quad3, and quad4.

Record Data (tools/capture_data.py)

1. Data collection
   • If extrinsic scene, data collection phase should be spent observing ARUCO marker, run tools/capture_data.py --extrinsic
2. Example data collection scene (not extrinsic): python tools/capture_data.py --scene_name test az_camera1

Data Recording Process

1. Start the OptiTrack recording
2. Synchronization Phase
   1. Press c to begin recording data
   2. Observe the ARUCO marker in the scene and move the camera in different trajectories to build synchronization data
   3. Press p when finished
3. Data Capturing Phase
   1. Press d to begin recording data
   2. If extrinsic scene, observe the ARUCO marker.
   3. If data collection scene, observe objects to track
   4. Press q when finished
4. Stop OptiTrack recording
5. Export OptiTrack recording to a CSV file with 60Hz report rate.
6. Move tracking CSV file to <scene name>/camera_poses/camera_pose.csv

Process Extrinsic Data to Calculate Extrinsic (If extrinsic scene)

1. Clean raw opti poses (tools/process_data.py --extrinsic)
2. Sync opti poses with frames (tools/process_data.py --extrinsic)
3. Calculate camera extrinsic (tools/calculate_camera_extrinsic.py)
4. Output will be placed in cameras/<camera name>/extrinsic.txt

Process Data (If data scene)

1. Clean raw opti poses (tools/process_data.py)
   Example: python tools/process_data.py --scene_name [SCENE_NAME]
2. Sync opti poses with frames (tools/process_data.py)
   Example: python tools/process_data.py --scene_name [SCENE_NAME]
3. Manually annotate first frame object poses (tools/manual_annotate_poses.py) 1. Modify ([SCENE_NAME]/scene_meta.yml) by adding (objects) field to the file according to objects and their corresponding ids.
   Example: python tools/manual_annotate_poses.py test
4. Recover all frame object poses and verify correctness (tools/generate_scene_labeling.py)
   Example: python tools/generate_scene_labeling.py --fast [SCENE_NAME]
   1. Generate semantic labeling (tools/generate_scene_labeling.py)
      Example: python /tools/generate_scene_labeling.py [SCENE_NAME]
   2. Generate per frame object poses (tools/generate_scene_labeling.py)
      Example: python tools/generate_scene_labeling.py [SCENE_NAME]

Citation

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

@InProceedings{DTTDv1,
    author    = {Feng, Weiyu and Zhao, Seth Z. and Pan, Chuanyu and Chang, Adam and Chen, Yichen and Wang, Zekun and Yang, Allen Y.},
    title     = {Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2023},
    pages     = {3288-3297}
}

@misc{huang2024robust6dofposeestimation,
      title={Robust 6DoF Pose Estimation Against Depth Noise and a Comprehensive Evaluation on a Mobile Dataset}, 
      author={Zixun Huang and Keling Yao and Seth Z. Zhao and Chuanyu Pan and Chenfeng Xu and Kathy Zhuang and Tianjian Xu and Weiyu Feng and Allen Y. Yang},
      year={2024},
      eprint={2309.13570},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2309.13570}, 
}

Minutia

• Extrinsic scenes have their color images inside of data stored as png. This is to maximize performance. Data scenes have their color images inside of data stored as jpg. This is necessary so the dataset remains usable.
• iPhone spits out jpg raw color images, while Azure Kinect skips out png raw color images.

Best Scene Collection Practices

• Good synchronization phase by observing ARUCO marker, for Azure Kinect keep in mind interference from OptiTrack system.
• Don't have objects that are in our datasets in the background. Make sure they are out of view!
• Minimize number of extraneous ARUCO markers/APRIL tags that appear in the scene.
• Stay in the yellow area for best OptiTrack tracking.
• Move other cameras out of area when collecting data to avoid OptiTrack confusion.
• Run manual_annotate_poses.py on all scenes after collection in order to archive extrinsic.
• We want to keep the data anonymized. Avoid school logos and members of the lab appearing in frame.
• Perform 90-180 revolution around objects, one way. Try to minimize stand-still time.