Point-cloud upsampling aims to generate dense point sets from sparse or incomplete 3D data while preserving geometric fidelity. Most existing works follow point-to-point (P2P) framework to produce denser point sets through iterative, fixed-scale upsampling, which can limit flexibility in handling various levels of detail in 3D models. Alternatively, voxel-based methods can dynamically upsample point density in voxel space but often struggle to preserve precise point locations due to quantization effects.In this work, we introduce Hybrid-Voxel Point-cloud Upsampling Network (HVPUNet), an efficient framework for dynamic point-cloud upsampling to address the limitations of both point-based and voxel-based methods. HVPUNet integrates two key modules: (1) a Shape Completion Module to restore missing geometry by filling empty voxels, and (2) a Super-Resolution Module to enhance spatial resolution and capture finer surface details. Moreover, we adopt progressive refinement, operational voxel expansion, and implicit learning to improve efficiency in 3D reconstruction. Experimental results demonstrate that HVPUNet effectively upscales large scenes and reconstructs intricate geometry at significantly lower computational cost, providing a scalable and versatile solution for 3D reconstruction, super-resolution, and high-fidelity surface generation.