Search Results for author:
Found 8 papers, 4 papers with code
Hierarchical Scene Coordinate Classification and Regression for Visual Localization
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.
Continual Learning for Image-Based Camera Localization
For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component.
Digging Into Self-Supervised Learning of Feature Descriptors
Fully-supervised CNN-based approaches for learning local image descriptors have shown remarkable results in a wide range of geometric tasks.
Visual Localization via Few-Shot Scene Region Classification
Visual (re)localization addresses the problem of estimating the 6-DoF (Degree of Freedom) camera pose of a query image captured in a known scene, which is a key building block of many computer vision and robotics applications.
Guiding Local Feature Matching with Surface Curvature
We propose a new method, named curvature similarity extractor (CSE), for improving local feature matching across images.
HSCNet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer
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.
DGC-GNN: Leveraging Geometry and Color Cues for Visual Descriptor-Free 2D-3D Matching
Matching 2D keypoints in an image to a sparse 3D point cloud of the scene without requiring visual descriptors has garnered increased interest due to its low memory requirements, inherent privacy preservation, and reduced need for expensive 3D model maintenance compared to visual descriptor-based methods.
DUSt3R: Geometric 3D Vision Made Easy
Our formulation directly provides a 3D model of the scene as well as depth information, but interestingly, we can seamlessly recover from it, pixel matches, relative and absolute camera.
