This paper investigates automatic digitization with complete coverage of large and complex environments using a TLS or a mobile scanner. We propose an adaptive multi-objective view-planner that can operate in an unknown environment to provide guidance for the human operator and ease the scanning task or by a mobile robot for an automatic exploration of the environment. The proposed view-planner is adapted to environments where the sensor is operating on a flat surface such as office spaces, urban areas, open fields or in some cultural heritage applications. First, we propose an adaptive gap-based method to extract occluded areas in a point cloud, which is completely automated and does not require extensive computations in a large environment such as ray-tracing or level-set methods. Then, we introduce a novel exploration strategy that uses specific regions of the environment called "Conservative-Cells" to drastically reduce the number of sensing positions to achieve complete digitization of the environment. Both methods were validated with simulated and real point clouds. The proposed approach has been applied to a scanner carried by a mobile robot, then to data acquired by a TLS used by a human operator in a large, complex environment. Experimental results on both TLS and mobile robot show that our view-planning approach is effective in finding a sequence of positions that leads to a complete reconstruction of the environment. Moreover, the proposed approach shows efficient performance in terms of coverage rate and computational time compared to others view-planning approaches as well as the results of an experienced human operator in a large, complex environment.