This study establishes an experimental pilot for urban digitalization through manual in-situ acquisition with consumer-grade LiDAR sensors (iPhone 15 Pro Max, iPad Pro M4). PointNet++ is implemented as comparative baseline, evidencing geometric difficulties that hierarchical architectures face with portable device noise. Contrasting against RandLA-Net, it is demonstrated that local feature learning capacity is determinant for overcoming such limitations. The evaluation on 759 objects from Vitoria-Gasteiz, Spain (124:1 class imbalance), reveals RandLA-Net achieves 90.4% overall accuracy and 61.5% Intersection over Union (IoU) mean (+12.2% over baseline), with strong per-class performance on majority classes (89.5% Benches, 83.3% Bins, 92.9% Sewer Caps). Results confirm technical superiority and operational viability of this low-cost solution, identifying critical thresholds (<50 instances) requiring specialized strategies.