Awesome

Track-it-in-3D

This is the official implementation of Towards Generic 3D Tracking in RGBD Videos: Benchmark and Baseline (ECCV2022).

Download the Dataset and Checkpoints

Training set(Code: 162r)

Test set(Code: fsoo)

Checkpoint(Code: 7mad)

Evaluation Protocols

Test the Baseline

# Clone the repository:
git clone https://github.com/yjybuaa/Track-it-in-3D.git

# Install dependencies: 
pip install -r requirements.txt

Citation

We appreciate your support of our work!

@inproceedings{trackitin3d,
  author       = {Jinyu Yang and
                  Zhongqun Zhang and
                  Zhe Li and
                  Hyung Jin Chang and
                  Ales Leonardis and
                  Feng Zheng},
  title        = {Towards Generic 3D Tracking in {RGBD} Videos: Benchmark and Baseline},
  booktitle    = {{ECCV} {(22)}},
  series       = {Lecture Notes in Computer Science},
  volume       = {13682},
  pages        = {112--128},
  publisher    = {Springer},
  year         = {2022}
}