In this article, we present a multiagent framework for real-time large-scale 3-D reconstruction applications. In SLAM, researchers usually build and update a 3-D map after applying nonlinear pose graph optimization techniques. Moreover, many multiagent systems are prevalently using odometry information from additional sensors. These methods generally involve extensive computer vision algorithms and are tightly coupled with various sensors. We develop a generic method for the key challenging scenarios in multiagent 3-D mapping based on different camera systems. The proposed framework performs actively in terms of localizing each agent after the first loop closure between them. It is shown that the proposed system only uses monocular cameras to yield real-time multiagent large-scale localization and 3-D global mapping. Based on the initial matching, our system can calculate the optimal scale difference between multiple 3-D maps and then estimate an accurate relative pose transformation for large-scale global mapping.