TriDE: Triangle-Consistent Translation Directions for Global Camera Pose Estimation

TL;DR

TriDE introduces a triangle-consistency approach for more accurate global camera pose estimation by refining pairwise directions through message passing, outperforming traditional methods.

Contribution

It proposes TriDE, a novel method that exploits camera-triangle consistency to improve pairwise direction estimation without costly global optimization.

Findings

TriDE significantly improves direction accuracy on real image graphs.

It yields better downstream camera location estimates.

The method establishes a strong phase-transition bound for exact recovery.

Abstract

Pairwise translation directions are a key input to camera location estimation in global structure-from-motion. Existing estimators usually process each image pair independently, producing directions that may be locally plausible but inconsistent with the other relative directions in the viewing graph. To jointly estimate the direction, we propose TriDE, which exploits camera-triangle consistency as an efficient higher-order verification signal. Instead of solving a costly global nonlinear optimization problem that is sensitive to initialization, TriDE refines unreliable pairwise directions through message passing between directions and their incident weighted triangles. This information propagation strategy enables us to establish a strong phase-transition bound for exact recovery under a realistic random corruption model. Experiments on real image graphs show that TriDE improves direction accuracy by a large margin and yields better downstream camera locations, providing a practical link between local pairwise estimation and global camera pose geometry.