Consensus Learning with Deep Sets for Essential Matrix Estimation

• URL: http://arxiv.org/abs/2406.17414v2
• Date: Sat, 02 Nov 2024 11:05:02 GMT
• Title: Consensus Learning with Deep Sets for Essential Matrix Estimation
• Authors: Dror Moran, Yuval Margalit, Guy Trostianetsky, Fadi Khatib, Meirav Galun, Ronen Basri,
• Abstract summary: We propose a simpler network architecture based on Deep Sets. Our method identifies outlier point matches and models the displacement noise in inlier matches. A weighted DLT module uses these predictions to regress the essential matrix.
• Score: 12.363338401943887
• License:
• Abstract: Robust estimation of the essential matrix, which encodes the relative position and orientation of two cameras, is a fundamental step in structure from motion pipelines. Recent deep-based methods achieved accurate estimation by using complex network architectures that involve graphs, attention layers, and hard pruning steps. Here, we propose a simpler network architecture based on Deep Sets. Given a collection of point matches extracted from two images, our method identifies outlier point matches and models the displacement noise in inlier matches. A weighted DLT module uses these predictions to regress the essential matrix. Our network achieves accurate recovery that is superior to existing networks with significantly more complex architectures.

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