# Point Tracking in Surgery--The 2024 Surgical Tattoos in Infrared (STIR) Challenge

arXiv: https://arxiv.org/abs/2503.24306 · HTML: https://mlsanity.com/paper/2503.24306/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 26 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 20 of 20)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [RoMa: Robust Dense Feature Matching](https://mlsanity.com/paper/2305.15404/) (2023)
- [TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement](https://mlsanity.com/paper/2306.08637/) (2023)
- [TAP-Vid: A Benchmark for Tracking Any Point in a Video](https://mlsanity.com/paper/2211.03726/) (2022)
- [SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow](https://mlsanity.com/paper/2405.14793/) (2024)
- [Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation](https://mlsanity.com/paper/2003.13045/) (2020)
- [BootsTAP: Bootstrapped Training for Tracking-Any-Point](https://mlsanity.com/paper/2402.00847/) (2024)
- [MFT: Long-Term Tracking of Every Pixel](https://mlsanity.com/paper/2305.12998/) (2023)
- [Learning How To Robustly Estimate Camera Pose in Endoscopic Videos](https://mlsanity.com/paper/2304.08023/) (2023)
- [TAPTR: Tracking Any Point with Transformers as Detection](https://mlsanity.com/paper/2403.13042/) (2024)
- [Tracking and Mapping in Medical Computer Vision: A Review](https://mlsanity.com/paper/2310.11475/) (2023)
- [Surgical Tattoos in Infrared: A Dataset for Quantifying Tissue Tracking and Mapping](https://mlsanity.com/paper/2309.16782/) (2023)
- [SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery](https://mlsanity.com/paper/2302.03022/) (2023)
- [SENDD: Sparse Efficient Neural Depth and Deformation for Tissue Tracking](https://mlsanity.com/paper/2305.06477/) (2023)
- [Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistency](https://mlsanity.com/paper/2403.06683/) (2024)
- [Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation](https://mlsanity.com/paper/2005.05050/) (2020)
- [MFTIQ: Multi-Flow Tracker with Independent Matching Quality Estimation](https://mlsanity.com/paper/2411.09551/) (2024)
- [A-MFST: Adaptive Multi-Flow Sparse Tracker for Real-Time Tissue Tracking Under Occlusion](https://mlsanity.com/paper/2410.19996/) (2024)

## Built on by (most cited first, 7 of 7)

- [LiteTracker: Leveraging Temporal Causality for Accurate Low-latency Tissue Tracking](https://mlsanity.com/paper/2504.09904/) (2025)
- [SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures](https://mlsanity.com/paper/2507.00209/) (2025)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [Surg-InvNeRF: Invertible NeRF for 3D tracking and reconstruction in surgical vision](https://mlsanity.com/paper/2508.09681/) (2025)
- [Track2Map: Online Deformable SLAM with Motion-Aware Pose Optimization in Robotic Surgery](https://mlsanity.com/paper/2607.08408/) (2026)
- [Point Tracking in Surgery--The 2025 Surgical Tattoos in Infrared Challenge (STIRC2025)](https://mlsanity.com/paper/2607.12939/) (2026)
- [S3-Tracker: Self-Supervised Surgical Tissue Tracking With Contrastive Random Walks](https://mlsanity.com/paper/2609.14313/) (2026)

## Abstract (arXiv)

Understanding tissue motion in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning, and subtask autonomy. Labeled data are essential to enabling algorithms in these downstream tasks since they allow us to quantify and train algorithms. This paper introduces a point tracking challenge to address this, wherein participants can submit their algorithms for quantification. The submitted algorithms are evaluated using a dataset named surgical tattoos in infrared (STIR), with the challenge aptly named the STIR Challenge 2024. The STIR Challenge 2024 comprises two quantitative components: accuracy and efficiency. The accuracy component tests the accuracy of algorithms on in vivo and ex vivo sequences. The efficiency component tests the latency of algorithm inference. The challenge was conducted as a part of MICCAI EndoVis 2024. In this challenge, we had 8 total teams, with 4 teams submitting before and 4 submitting after challenge day. This paper details the STIR Challenge 2024, which serves to move the field towards more accurate and efficient algorithms for spatial understanding in surgery. In this paper we summarize the design, submissions, and results from the challenge. The challenge dataset is available here: https://zenodo.org/records/14803158 , and the code for baseline models and metric calculation is available here: https://github.com/athaddius/STIRMetrics