Compared with grid-based 2D images, processing 3D point clouds is more challenging due to their irregular distribution and intricate spatial information. Most prior works introduce delicate designs on either local feature aggregators or global geometric architecture, but few combine two scales effectively. Therefore, to better incorporate the advantages of both local and global processing, we propose DS-Point, a dual-scale 3D framework for point cloud understanding. DS-Point firstly disentangles 3D features from channel dimension for concurrent dual-scale modeling, i.e., point-wise convolution for local fine-grained geometry parsing, and voxel-wise attention for global long-range spatial exploration. Upon that, an HF-fusion module is proposed to enhance the cross-modal interaction and thoroughly blend the dual-scale features. Then, with task-specific heads for different downstream tasks, DS-Point serves as an effective 3D framework for feature extraction. By the dual-scale paradigm, DS-Point achieves superior performance on multiple downstream tasks, e.g., 93.8% for shape classification on ModelNet40, 84.9% on ScanObjectNN, and 84.3% on ShapeNetPart.