| 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 |
