The Next Best View (NBV) problem is a pivotal challenge in 3D robotic scanning, with the potential to significantly improve the efficiency of object capture and reconstruction. Existing methods for determining the NBV often overlook view overlap, assume a fixed virtual origin for the camera, and rely on voxel-based representations of 3D data. To address these limitations and enhance the practicality of scanning unknown objects, we propose an NBV policy in which the next view explores the boundary of the scanned point cloud, with overlap intrinsically considered. The scanning or working distance of the camera is user-defined and remains flexible throughout the process. To this end, we first introduce a model-based approach in which candidate views are iteratively proposed based on a reference model. Scores are computed using a carefully designed strategy that accounts for both view overlap and convergence. In addition, we propose a learning-based method, the Boundary Exploration NBV Network (BENBV-Net), which predicts the NBV directly from the scanned data without requiring a reference model. BENBV-Net estimates scores for candidate boundaries, selecting the one with the highest score as the target for the next best view. It offers a significant improvement in NBV generation speed while maintaining the performance level of the model-based approach. We evaluate both methods on the ShapeNet, ModelNet, and 3D Repository datasets. Experimental results demonstrate that our approach outperforms existing methods in terms of scanning efficiency, final coverage, and overlap stability, all of which are critical for practical 3D scanning applications. The related code is available at github.com/leihui6/BENBV.