Fully-supervised CNN-based approaches for learning local image descriptors have shown remarkable results in a wide range of geometric tasks.
For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component.
This paper presents an infrastructure assisted constrained connected automated vehicles (CAVs) trajectory optimization method on curved roads.
Camera pose estimation in large-scale environments is still an open question and, despite recent promising results, it may still fail in some situations.
In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image.
Image-based camera relocalization is an important problem in computer vision and robotics.
In this paper, instead of in a patch-based manner, we propose to perform the scene coordinate regression in a full-frame manner to make the computation efficient at test time and, more importantly, to add more global context to the regression process to improve the robustness.