Scholarlink

<p>Absolute Pose Regression (APR) encompasses a spectrum of visual localization methods that directly regress the 6-DoF camera pose from input images. Previous APR methods typically rely on 3DGS rendered features that smooth out structural details, leading to ambiguous scene descriptors. To address this limitation, we introduce surface curvature as explicit multi-view epipolar geometric cues that capture stable, detailed variations across viewpoints and provide structurally reliable cues for pose estimation. Specifically, we adopt a 3D Gaussian Splatting (3DGS) representation equipped with surface curvature for the scene, and introduce a novel refinement framework termed CurvLoc. Within this framework, a Surface Curvature Extractor is designed to capture curvature information from rendered features along epipolar line directions. Additionally, we propose a Curvature-aware Sampling Strategy that prioritizes regions exhibiting the largest curvature, effectively exploiting multi-view information. This approach significantly enhances geometric detail awareness and delineates clear boundaries in complex regions, facilitating precise visual localization. Extensive experiments on indoor and outdoor visual localization benchmarks demonstrate that the proposed CurvLoc framework surpasses existing state-of-the-art methods in accuracy and robustness.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

CurvLoc: Surface Curvature Prompted Gaussian Splatting for Visual Localization

  • 期刊论文

  • 发表日期：

    2026年2月21日

查看全文
  • Hang Li,
  • Jiawei Zhang,
  • Jiahe Li,
  • Botao Jiang,
  • Zihang Wang,
  • Xiaohan Yu,
  • Jin Zheng,
  • Xiao Bai,
  • Haonan Luo

摘要

Absolute Pose Regression (APR) encompasses a spectrum of visual localization methods that directly regress the 6-DoF camera pose from input images. Previous APR methods typically rely on 3DGS rendered features that smooth out structural details, leading to ambiguous scene descriptors. To address this limitation, we introduce surface curvature as explicit multi-view epipolar geometric cues that capture stable, detailed variations across viewpoints and provide structurally reliable cues for pose estimation. Specifically, we adopt a 3D Gaussian Splatting (3DGS) representation equipped with surface curvature for the scene, and introduce a novel refinement framework termed CurvLoc. Within this framework, a Surface Curvature Extractor is designed to capture curvature information from rendered features along epipolar line directions. Additionally, we propose a Curvature-aware Sampling Strategy that prioritizes regions exhibiting the largest curvature, effectively exploiting multi-view information. This approach significantly enhances geometric detail awareness and delineates clear boundaries in complex regions, facilitating precise visual localization. Extensive experiments on indoor and outdoor visual localization benchmarks demonstrate that the proposed CurvLoc framework surpasses existing state-of-the-art methods in accuracy and robustness.