3D Feature Matching
10 papers with code • 1 benchmarks • 4 datasets
Image: Choy et al
Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown.
Visual information extraction (VIE) has attracted considerable attention recently owing to its various advanced applications such as document understanding, automatic marking and intelligent education.
While the Transformer architecture has become ubiquitous in the machine learning field, its adaptation to 3D shape recognition is non-trivial.
We apply GAM to a new hierarchical visual localization pipeline and show that GAM can effectively improve the robustness and accuracy of localization.
Visual localization is the task of estimating a 6-DoF camera pose of a query image within a provided 3D reference map.