Individual tree segmentation from LiDAR point clouds is critical for forest inventory and ecological monitoring. However, accurate delineation remains challenging in complex forest environments with dense crown overlap, occlusion, and multilayer vertical structures. To address these challenges, we propose ForestSeg3D, a semantically guided framework that improves individual tree segmentation through hierarchical semantic supervision and bidirectional cross-task distillation. Specifically, hierarchical semantic supervision (HSS) introduces a coarse-to-fine semantic learning scheme with explicit cross-level consistency, where coarse supervision distinguishes Tree from Non-Tree and fine-grained supervision further decomposes scenes into Ground, Wood, and Leaf, providing structured semantic priors for instance learning and enabling more discriminative tree representations. Building on this representation, bidirectional cross-task distillation (BCTD) explicitly couples semantic prediction and instance partition, reducing conflicts between semantic labels and instance boundaries and improving delineation in crowded forest scenes. To support large-scale inference, ForestSeg3D further incorporates semantic-aware region merging (SRM), a scene-level consolidation strategy that alleviates cross-region conflicts caused by overlapping crowns and boundary-crossing trees. Extensive experiments on the FOR-instanceV2 dataset show that ForestSeg3D achieves the best overall performance in detection completeness, false-positive suppression, and F1-score, while maintaining competitive mAP. The method also maintains strong performance on the ForestSemantic dataset under three-fold leave-one-plot-out cross-validation. Improved segmentation further enables more reliable estimation of tree-level structural attributes, including tree height, crown diameter, and crown volume. These results demonstrate the effectiveness and practical value of ForestSeg3D for individual tree segmentation and downstream forest structural assessment from LiDAR point clouds.