Learning Global Camera Poses from Noisy View-Graphs for Structure from Motion
This work presents a deep, global Structure-from-Motion framework based on learned view-graph aggregation that employs a permutation-equivariant, edge-conditioned graph neural network that takes noisy pairwise relative poses as input and outputs globally consistent camera extrinsics.
Fadi Khatib, M. Galun, R. Basri
· 0 citations