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.
Render--match--PnP relocalization establishes correspondences between query image pixels and 3D map points for camera pose recovery, but their potential to support dense depth estimation is often overlooked. To exploit this geometric information, we present RIDE, which estimates dense metric depth from a robot's RGB st...
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.
Given a compact semantic scene graph, long-term indoor video relocalization estimates a map-frame trajectory after lighting and furniture changes. Visual methods rely on appearance and become unreliable under these changes; localizing one frame at a time from object classes and geometry instead leaves sparse, ambiguous...
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