Abstract. Affordable 360° cameras combined with cloud- or desktop-based photogrammetry have made image-based documentation of complex interiors widely accessible. However, the metric reliability of point clouds reconstructed from 360° video remains inconsistently reported, particularly when compared to survey-grade terrestrial laser scanning (TLS). This study evaluates point-cloud generation from 360° videos captured along guided walking paths using consumer cameras, with high-overlap frame extraction and Structurefrom- Motion processing using the CupixVista solution. Comparisons are performed using cloud-to-cloud (C2C) distance analysis, control-point analysis (CPA), and visual inspection of noise, surface roughness, and edge definition, with efficiency also considered. Point clouds are registered to the TLS reference using a two-step rigid alignment (manual coarse registration followed by ICP (Iterative Closest Point)). C2C distances, point density, and CPA errors are computed in CloudCompare after spatial subsampling. Control points are defined as intersections of locally fitted planes to improve precision. Several indoor test scenes with varying geometry, scale, and visual characteristics are analyzed. Results show average deviations around 50 mm for C2C and 35 mm for CPA, with visual inspection confirming higher noise levels and deformation of sharp features in 360°-derived point clouds compared to TLS.
Honti et al. (Thu,) studied this question.