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Leveraging Scale- and Orientation-Covariant Features for Planar Motion Estimation

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Computer Vision – ECCV 2024 (ECCV 2024)

Abstract

In this paper, we derive a linear constraint for planar motion leveraging scale- and orientation-covariant features, e.g., SIFT, which is used to create a novel minimal solver for planar motion requiring only a single covariant feature. We compare the proposed method to traditional point-based solvers and solvers relying on affine correspondences in controlled synthetic environments and well-established datasets for autonomous driving. The proposed solver is integrated into a modern robust estimation framework, where it is shown to accelerate the complete estimation pipeline more than 25\(\times \), compared to state-of-the-art affine-based minimal solvers, with negligible loss in precision (Code available here: https://github.com/EricssonResearch/eccv-2024).

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Notes

  1. 1.

    https://github.com/danini/graph-cut-ransac.

  2. 2.

    https://github.com/jizhaox/relative_pose_from_affine.

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Correspondence to Marcus Valtonen Örnhag .

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Örnhag, M.V., Jaenal, A. (2025). Leveraging Scale- and Orientation-Covariant Features for Planar Motion Estimation. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15112. Springer, Cham. https://doi.org/10.1007/978-3-031-72949-2_24

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