# ENFT: Efficient Non-Consecutive Feature Tracking for Robust Structure-from-Motion

arXiv: https://arxiv.org/abs/1510.08012 · HTML: https://mlsanity.com/paper/1510.08012/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2015.

## Built on by (most cited first, 6 of 6)

- [PATS: Patch Area Transportation with Subdivision for Local Feature Matching](https://mlsanity.com/paper/2303.07700/) (2023)
- [StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation](https://mlsanity.com/paper/2501.05763/) (2025)
- [Parallel Structure from Motion for UAV Images via Weighted Connected Dominating Set](https://mlsanity.com/paper/2206.11499/) (2022)
- [EC-SfM: Efficient Covisibility-based Structure-from-Motion for Both Sequential and Unordered Images](https://mlsanity.com/paper/2302.10544/) (2023)
- [TC-SfM: Robust Track-Community-Based Structure-from-Motion](https://mlsanity.com/paper/2206.05866/) (2022)
- [Hierarchical Segment-based Optimization for SLAM](https://mlsanity.com/paper/2111.04101/) (2021)

## Abstract (arXiv)

Structure-from-motion (SfM) largely relies on feature tracking. In image sequences, if disjointed tracks caused by objects moving in and out of the field of view, occasional occlusion, or image noise, are not handled well, corresponding SfM could be affected. This problem becomes severer for large-scale scenes, which typically requires to capture multiple sequences to cover the whole scene. In this paper, we propose an efficient non-consecutive feature tracking (ENFT) framework to match interrupted tracks distributed in different subsequences or even in different videos. Our framework consists of steps of solving the feature `dropout' problem when indistinctive structures, noise or large image distortion exists, and of rapidly recognizing and joining common features located in different subsequences. In addition, we contribute an effective segment-based coarse-to-fine SfM algorithm for robustly handling large datasets. Experimental results on challenging video data demonstrate the effectiveness of the proposed system.