Search Results for author:
Found 7 papers, 5 papers with code
The NeRFect Match: Exploring NeRF Features for Visual Localization
Significantly, we introduce NeRFMatch, an advanced 2D-3D matching function that capitalizes on the internal knowledge of NeRF learned via view synthesis.
Text2Pos: Text-to-Point-Cloud Cross-Modal Localization
Natural language-based communication with mobile devices and home appliances is becoming increasingly popular and has the potential to become natural for communicating with mobile robots in the future.
Is Geometry Enough for Matching in Visual Localization?
In this paper, we propose to go beyond the well-established approach to vision-based localization that relies on visual descriptor matching between a query image and a 3D point cloud.
Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization
The goal of cross-view image based geo-localization is to determine the location of a given street view image by matching it against a collection of geo-tagged satellite images.
Patch2Pix: Epipolar-Guided Pixel-Level Correspondences
In this work, we propose a new perspective to estimate correspondences in a detect-to-refine manner, where we first predict patch-level match proposals and then refine them.
To Learn or Not to Learn: Visual Localization from Essential Matrices
Using a classical feature-based approach within this framework, we show state-of-the-art performance.
Understanding the Limitations of CNN-based Absolute Camera Pose Regression
We furthermore use our model to show that pose regression is more closely related to pose approximation via image retrieval than to accurate pose estimation via 3D structure.
