# Sparse Optical Flow-Based Line Feature Tracking

arXiv: https://arxiv.org/abs/2204.03331 · HTML: https://mlsanity.com/paper/2204.03331/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments](https://mlsanity.com/paper/1705.09479/) (2017)
- [Learning Attraction Field Representation for Robust Line Segment Detection](https://mlsanity.com/paper/1812.02122/) (2018)
- [PPGNet: Learning Point-Pair Graph for Line Segment Detection](https://mlsanity.com/paper/1905.03415/) (2019)
- [TP-LSD: Tri-Points Based Line Segment Detector](https://mlsanity.com/paper/2009.05505/) (2020)
- [CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction](https://mlsanity.com/paper/1810.01011/) (2018)
- [PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features](https://mlsanity.com/paper/2009.07462/) (2020)
- [StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments](https://mlsanity.com/paper/1810.06796/) (2018)
- [Observability Analysis of Aided INS with Heterogeneous Features of Points, Lines and Planes](https://mlsanity.com/paper/1805.05876/) (2018)
- [Fast ORB-SLAM without Keypoint Descriptors](https://mlsanity.com/paper/2008.09870/) (2020)
- [Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry using Points and Lines](https://mlsanity.com/paper/1803.02403/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges](https://mlsanity.com/paper/2305.00264/) (2023)

## Abstract (arXiv)

In this paper we propose a novel sparse optical flow (SOF)-based line feature tracking method for the camera pose estimation problem. This method is inspired by the point-based SOF algorithm and developed based on an observation that two adjacent images in time-varying image sequences satisfy brightness invariant. Based on this observation, we re-define the goal of line feature tracking: track two endpoints of a line feature instead of the entire line based on gray value matching instead of descriptor matching. To achieve this goal, an efficient two endpoint tracking (TET) method is presented: first, describe a given line feature with its two endpoints; next, track the two endpoints based on SOF to obtain two new tracked endpoints by minimizing a pixel-level grayscale residual function; finally, connect the two tracked endpoints to generate a new line feature. The correspondence is established between the given and the new line feature. Compared with current descriptor-based methods, our TET method needs not to compute descriptors and detect line features repeatedly. Naturally, it has an obvious advantage over computation. Experiments in several public benchmark datasets show our method yields highly competitive accuracy with an obvious advantage over speed.