Accurate semantic segmentation of 3D point clouds is essential for applications in photogrammetry, robotics, and large-scale scene understanding. While recent point-based architectures such as PointNeXt achieve strong performance through hierarchical feature learning, they still struggle near semantic boundaries, where points from different classes share local neighborhoods and feature aggregation leads to oversmoothing and ambiguous predictions. To address this limitation, we propose a lightweight boundary-aware learning framework that explicitly models boundary regions during training. The method introduces an auxiliary boundary prediction head that learns boundary cues from local semantic disagreement and integrates them into the segmentation process through a simple late-stage feature fusion mechanism. This design enhances feature discrimination near class transitions without modifying the backbone architecture or increasing inference complexity. Experiments on the S3DIS benchmark with the standard 6-fold cross-validation protocol show consistent improvements over the PointNeXt baseline, achieving gains of 3.22% in mean Intersection over Union (mIoU) and 2.85% in mean class accuracy (mACC) (relative), with notably improved predictions along object boundaries. These results show that incorporating boundary-aware supervision provides an effective and efficient approach to improving segmentation quality in challenging regions.