ENFT: Efficient Non-Consecutive Feature Tracking for Robust Structure-from-Motion
This addresses robustness issues in SfM for large-scale scenes, particularly when capturing multiple sequences, but appears incremental as it builds on existing tracking methods.
The paper tackles the problem of disjointed feature tracks in structure-from-motion (SfM) due to occlusions or noise, proposing an efficient non-consecutive feature tracking (ENFT) framework that matches interrupted tracks across subsequences or videos, with experimental validation on challenging video data.
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.