The paper addresses geometric and attribute distortions in point clouds, which can arise at various stages of data acquisition and processing. The authors present the main sources of distortions—such as sensor limitations, platform movement, and environmental influences—and their impact on point density, data distribution, and attribute reliability. They detail statistical methods for quantitative evaluation of distortions (e.g., Getis–Ord Gi*, Moran’s I, Gini coefficient), which allow systematic measurement and correction of errors. The study emphasizes that analyzing and mitigating distortions is essential for creating more accurate spatial models and producing reliable geoinformatics analyses.