# Segmentation-Guided Homography Estimation for Long-Term Planar Tracking

arXiv: https://arxiv.org/abs/2602.19624 · HTML: https://mlsanity.com/paper/2602.19624/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [A Distractor-Aware Memory for Visual Object Tracking with SAM2](https://mlsanity.com/paper/2411.17576/) (2024)
- [GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs](https://mlsanity.com/paper/1911.05932/) (2019)
- [Online Invariance Selection for Local Feature Descriptors](https://mlsanity.com/paper/2007.08988/) (2020)
- [Planar Object Tracking in the Wild: A Benchmark](https://mlsanity.com/paper/1703.07938/) (2017)
- [Homography Decomposition Networks for Planar Object Tracking](https://mlsanity.com/paper/2112.07909/) (2021)
- [HVC-Net: Unifying Homography, Visibility, and Confidence Learning for Planar Object Tracking](https://mlsanity.com/paper/2209.08924/) (2022)
- [Planar Object Tracking via Weighted Optical Flow](https://mlsanity.com/paper/2301.10057/) (2023)
- [PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking](https://mlsanity.com/paper/2303.07625/) (2023)
- [PlanarTrack: A high-quality and challenging benchmark for large-scale planar object tracking](https://mlsanity.com/paper/2510.23368/) (2025)

## Abstract (arXiv)

Recent state-of-the-art visual trackers produce high quality and long-term-stable segmentation masks. We propose to leverage these strengths for planar object tracking, in which the goal is to estimate a precise 8-degrees-of-freedom homography pose, a geometric representation not estimated by segmentation trackers. We present SAM-H - a planar object tracker that estimates homographies from segmentation mask contours via a training-free pipeline. When SAM-H is applied to masks from SAM 2, it sets a new state-of-the-art performance on the challenging PlanarTrack benchmark by a large margin, +18.4pp on the p@5 metric. We further show that segmentation-based and correspondence-based homography estimation are complementary, and propose WOFTSAM, which out-performs all prior methods on both PlanarTrack and POT-210. We also provide precise re-annotations of PlanarTrack initial poses, enabling more accurate benchmarking in the high-precision p@5 metric. The code and the re-annotations are available at https://github.com/serycjon/WOFTSAM