54 citations · 238 across the 27 of their papers we have counts for
29 papers
Learning Global Camera Poses from Noisy View-Graphs for Structure from Motion
Fadi Khatib, Meirav Galun, Ronen Basri
Camera pose estimation is a key step in 3D reconstruction and view-synthesis pipelines. We present a deep, global Structure-from-Motion framework based on learned view-graph aggreg…
GSVisLoc: Generalizable Visual Localization for Gaussian Splatting Scene Representations
Fadi Khatib, Dror Moran, Guy Trostianetsky +3
We introduce GSVisLoc, a visual localization method designed for 3D Gaussian Splatting (3DGS) scene representations. Given a 3DGS model of a scene and a query image, our goal is to…
Querying Kernel Methods Suffices for Reconstructing their Training Data
Daniel Barzilai, Yuval Margalit, Eitan Gronich +3
Over-parameterized models have raised concerns about their potential to memorize training data, even when achieving strong generalization. The privacy implications of such memoriza…
On the Reconstruction of Training Data from Group Invariant Networks
Ran Elbaz, Gilad Yehudai, Meirav Galun +1
Reconstructing training data from trained neural networks is an active area of research with significant implications for privacy and explainability. Recent advances have demonstra…
Consensus Learning with Deep Sets for Essential Matrix Estimation
Dror Moran, Yuval Margalit, Guy Trostianetsky +3
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…
RESfM: Robust Deep Equivariant Structure from Motion
Fadi Khatib, Yoni Kasten, Dror Moran +2
Multiview Structure from Motion is a fundamental and challenging computer vision problem. A recent deep-based approach utilized matrix equivariant architectures for simultaneous re…