Estimating camera motion from monocular video is a core challenge in computer vision, underpinning applications like SLAM, visual odometry, and structure-from-motion. Numerous methods have been proposed to determine the camera’s heading when its rotation is known either from an IMU or an optimization algorithm. While these approaches perform well in low-noise, low-outlier conditions, they often fail or become computationally expensive as noise and outlier levels increase. To address these limitations, we introduce a novel approach that employs a generalized Hough transform on the unit sphere, $\mathcal{S}^2$, to estimate the camera translation directions. We start by extracting correspondences between two frames and generating a great circle of directions compatible with each pair of correspondences. By discretizing the unit sphere using a Fibonacci Lattice as bin centers, each great circle casts votes for a range of directions, ensuring that features unaffected by noise or dynamic objects vote consistently for the correct motion direction. Experimental results on three datasets demonstrate that our approach exceeds baseline methods in both accuracy and speed.

@misc{dirnfeld2026flight,
      title={FLIGHT: Fibonacci Lattice-based Inference for Geometric Heading in real-Time},
      author={David Dirnfeld and Fabien Delattre and Pedro Miraldo and Erik Learned-Miller},
      year={2026},
      eprint={2602.23115},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2602.23115},
}