用特征高斯点云实现无需初始位姿的精准相机重定位
From Sparse to Dense: Camera Relocalization with Scene-Specific Detector from Feature Gaussian Splatting
- 从稀疏到稠密的全新定位范式，直接构建场景特征高斯表示
- 在室内外数据集上定位精度与召回率均超越现有最优方法
- 专为特定场景设计检测器，提升初始位姿估计效率与鲁棒性
本文提出一种新型相机重定位方法STDLoc，利用特征高斯点云作为场景表征。该方法构成完整重定位流程，可在无任何位姿先验的情况下实现高精度重定位。不同于传统粗到精的定位方式需先图像检索再特征匹配，我们提出一种新颖的稀疏到稠密定位范式。基于此场景表示，引入面向匹配的高斯采样策略及场景专用检测器，实现高效稳健的初始位姿估计。进一步地，基于初始定位结果，通过稠密特征匹配将查询特征图对齐至高斯特征场，从而实现精确重定位。在室内外数据集上的实验表明，STDLoc在定位精度与召回率方面均优于当前最先进的定位方法。
原文摘要 · Abstract (English)
This paper presents a novel camera relocalization method, STDLoc, which leverages Feature Gaussian as scene representation. STDLoc is a full relocalization pipeline that can achieve accurate relocalization without relying on any pose prior. Unlike previous coarse-to-fine localization methods that require image retrieval first and then feature matching, we propose a novel sparse-to-dense localization paradigm. Based on this scene representation, we introduce a novel matching-oriented Gaussian sampling strategy and a scene-specific detector to achieve efficient and robust initial pose estimation. Furthermore, based on the initial localization results, we align the query feature map to the Gaussian feature field by dense feature matching to enable accurate localization. The experiments on indoor and outdoor datasets show that STDLoc outperforms current state-of-the-art localization methods in terms of localization accuracy and recall.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。