DeepAI AI Chat
Log In Sign Up

Leveraging Local and Global Descriptors in Parallel to Search Correspondences for Visual Localization

by   Pengju Zhang, et al.

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 2D-3D point correspondences. In this paper, we propose a novel parallel search framework, which leverages advantages of both local and global descriptors to get nearest neighbor candidates of a query local feature. Specifically, besides using deep learning based global descriptors, we also utilize local descriptors to construct random tree structures for obtaining nearest neighbor candidates of the query local feature. We propose a new probabilistic model and a new deep learning based local descriptor when constructing the random trees. A weighted Hamming regularization term to keep discriminativeness after binarization is given in the loss function for the proposed local descriptor. The loss function co-trains both real and binary descriptors of which the results are integrated into the random trees.


page 4

page 13


SuperGF: Unifying Local and Global Features for Visual Localization

Advanced visual localization techniques encompass image retrieval challe...

Local Descriptors Optimized for Average Precision

Extraction of local feature descriptors is a vital stage in the solution...

Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization

Many robotics applications require precise pose estimates despite operat...

LandmarkBoost: Efficient Visual Context Classifiers for Robust Localization

The growing popularity of autonomous systems creates a need for reliable...

MTLDesc: Looking Wider to Describe Better

Limited by the locality of convolutional neural networks, most existing ...

Late Fusion of Local Indexing and Deep Feature Scores for Fast Image-to-Video Search on Large-Scale Databases

Low cost visual representation and fast query-by-example content search ...

Cluster-Wise Ratio Tests for Fast Camera Localization

Feature point matching for camera localization suffers from scalability ...