Leveraging Local And Global Descriptors In Parallel To Search Correspondences For Visual Localization

Abstract

Visual localization to compute 6DoF camera pose from a given image has wide applications such as in robotics, virtual reality, augmented reality, etc. Two kinds of descriptors are important for the visual localization. One is global descriptors that extract the whole feature from each image. The other is local descriptors that extract the local feature from each image patch usually enclosing a key point. More and more methods of the visual localization have two stages: at first to perform image retrieval by global descriptors and then from the retrieval feedback to make 2D-3D point correspondences by local descriptors. The two stages are in serial for most of the methods. This simple combination has not achieved superiority of fusing local and global descriptors. The 3D points obtained from the retrieval feedback are as the nearest neighbor candidates of the 2D image points only by global descriptors. Each of the 2D image points is also called a query local feature when performing the

Stats

• citations14
• S2 citations—
• github stars0
• HF likes0
• heat score8.82
• arxiv keyzhang2020leveraging

Related papers

• Yes, We CANN: Constrained Approximate Nearest Neighbors For Local Feature-based Visual Localization (2023)14.99
• Leveraging Semantic Cues From Foundation Vision Models For Enhanced Local Feature Correspondence (2024)5.24
• Learning Local Descriptors By Optimizing The Keypoint-correspondence Criterion: Applications To Face Matching, Learning From Unlabeled Videos And 3d-shape Retrieval (2016)11.75
• Sparse-to-dense Hypercolumn Matching For Long-term Visual Localization (2019)12.99
• DH3D: Deep Hierarchical 3D Descriptors For Robust Large-scale 6dof Relocalization (2020)14.76
• Are Local Features All You Need For Cross-domain Visual Place Recognition? (2023)13.80
• Supergf: Unifying Local And Global Features For Visual Localization (2022)0.00
• Air-hloc: Adaptive Retrieved Images Selection For Efficient Visual Localisation (2024)0.00