Visual relocalization remains a widely studied problem in the field of 3D vision, where the goal is to estimate the six degrees of freedom (6DOF) camera pose of a query image within a given prior map. The task is particularly crucial in large-scale indoor environments for applications such as augmented reality and robotic navigation. However, rapid changes in scene appearance, caused by camera movement, pose significant challenges to relocalization systems. To address this issue, a method based on virtual view synthesis is proposed, which enriches the query database and enhances pose estimation in such scenarios. In contrast to approaches that rely on rendering realistic images, the proposed method eliminates the need for high-quality 3D models. Instead, global and local features are directly rendered from virtual viewpoints and are then used for image retrieval and feature matching. Experimental results demonstrate that the proposed method significantly enhances relocalization performance in large-scale indoor settings, achieving improvements of 7.1% and 12.2% on the InLoc dataset.