Three-dimensional (3D) models of environments are a promising technique for serious gaming and professional engineering applications. In this paper, we introduce a fast and memory-efficient system for the reconstruction of large-scale environments based on point clouds. Our main contribution is the emphasis on the data processing of large planes, for which two algorithms have been designed to improve the overall performance of the 3D reconstruction. First, a flatness-based segmentation algorithm is presented for plane detection in point clouds. Second, a quadtree-based algorithm is proposed for decimating the point cloud involved with the segmented plane and consequently improving the efficiency of triangulation. Our experimental results have shown that the proposed system and algorithms have a high efficiency in speed and memory for environment reconstruction. Depending on the amount of planes in the scene, the obtained efficiency gain varies between 20% and 50%.