Consensus Learning with Deep Sets for Essential Matrix Estimation

Bibliographic Details
Title: Consensus Learning with Deep Sets for Essential Matrix Estimation
Authors: Moran, Dror, Margalit, Yuval, Trostianetsky, Guy, Khatib, Fadi, Galun, Meirav, Basri, Ronen
Publication Year: 2024
Collection: Computer Science
Subject Terms: Computer Science - Computer Vision and Pattern Recognition
More Details: 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.
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2406.17414
Accession Number: edsarx.2406.17414
Database: arXiv
More Details
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