Indoor visual localization, i.e., 6 Degree-of-Freedom camera pose estimation for a query image with respect to a known scene, is gaining increased attention driven by rapid progress of applications such as robotics and augmented reality. However, drastic visual discrepancies between an onsite query image and prerecorded indoor images cast a significant challenge for visual localization. In this paper, based on the key observation of the constant existence of planar surfaces such as floors or walls in indoor scenes, we propose a novel system incorporating geometric information to address issues using only pixelated images. Through the system implementation, we contribute a hierarchical structure consisting of pre-scanned images and point cloud, as well as a distilled representation of the planar-element layout extracted from the original dataset. A view synthesis procedure is designed to generate synthetic images as complementary to that of a sparsely sampled dataset. Moreover, a global image descriptor based on the image statistic modality, called block mean, variance, and color (BMVC), was employed to speed up the candidate pose identification incorporated with a traditional convolutional neural network (CNN) descriptor. Experimental results on a popular benchmark demonstrate that the proposed method outperforms the state-of-the-art approaches in terms of visual localization validity and accuracy.