AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
LoFG is presented, a localization-oriented Feature Gaussian representation that unifies both stages within a single Gaussian scene and improves the robustness of sparse initialization and the accuracy of dense refinement, demonstrating potential for localization applications in AR, robotics, and visual navigation systems.
Visual localization is a key technology in many vision-based measurement applications, aiming to estimate the camera pose of a query image in a known environment. However, most existing methods rely on heavy scene-specific representations, such as explicit 3-D map construction or per-scene training. Constructing and ma...
GS-CPE (Gaussian Splatting based Camera Pose Estimation), a coarse-to-fine framework for 6-DoF camera pose estimation that unifies geometry-based coarse pose estimation with robust 3D Gaussian Splatting based pose refinement, is introduced.
This work enhances the existing iterative object-basesd visual localization approach with an additional semantic feature derived from a pretrained semantic segmentation model and conducts a systematic baseline study of contemporary feature matching techniques on such cross-domain query-reference image pairs.
Experiments show improved depth accuracy and temporal consistency over scale-only calibration, demonstrating how localization geometry can support both pose recovery and dense robot perception.
This paper proposes a novel iterative refinement framework based on a video diffusion model to improve the completeness and consistency of dynamic 4D scenes, and substantially outperforms existing baselines.
This work introduces VideoReloc, whose adaptive clips use odometry to gather spatial evidence until object and motion criteria are met, adapting query length to the observed scene, and reframes sparse-map relocalization as verification of spatially extended video queries.
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