Information-Aware Reduction of Construction LiDAR Point Clouds: Scene-Level Fidelity, Minority-Class and Registration Trade-offs

Dense construction point clouds are often reduced before storage, transfer or analysis, but global distortion metrics can conceal loss of sparsely represented classes and instances. This study evaluates information-aware point-cloud reduction using 51 terrestrial laser-scan scenes from two Rohbau3D construction sites. Every result is conditional on a class-aware 150,000-point reference sample per scene (7.65 million sampled points in total), rather than the native full-resolution scans. We compare a naïve NPZ/Deflate storage baseline, voxel grids of 0.05, 0.10 and 0.20 m, and five archived fixed-rate selection policies at 25%, 10% and 5% retention. Scene-level bootstrap confidence intervals and repeated-measures tests replace point-pooled inference. The naïve storage baseline reduced sampled payload size by 10.13%. A 0.05 m voxel grid removed 69.83% of sampled points while producing 1.470 cm mean nearest-neighbour error and 99.32% nearest-neighbour class agreement. All voxel outcomes changed monotonically across settings. In controlled ICP, 0.05 m voxelisation did not significantly change the archived control-RMSE measure relative to the sampled-reference condition, but fitness within 10 cm fell by 13.62 percentage points ( $$p<.001$$ p < . 001 ). At 0.20 m, 83.77% of rare class–scene records retained less than 10% of their sampled points, although no evaluated class disappeared completely. Balanced information-aware selection improved small-instance presence over class-aware selection by 2.10, 7.21 and 15.47 percentage points at 25%, 10% and 5% retention, respectively, while sharing identical class quotas and therefore identical rare-class retention. These measures indicate how much information is retained after point-cloud reduction, on top of segmentation or object-detection performance. The study therefore provides a construction-specific, scene-level benchmark and a transparent basis for selecting appropriate reduction settings.