3D measurement point clouds have become a widely used medium for the digital archive of cultural heritage. However, for 2.5D data such as reliefs, scanning is typically performed from a frontal viewpoint, which often re-sults in missing regions, especially for areas with considerable thickness. In large-scale measurement tasks, detecting such incomplete areas is both chal-lenging and prone to low accuracy. In this paper, we propose a method based on the entropy of local linearity to effectively emphasize and visual-ize the boundaries of these missing regions within 3D point clouds. The approach demonstrates strong potential as an auxiliary tool for scanning quality assessment and for enhancing the geometric reliability of heritage point cloud datasets.