This study presents the design and evaluation of a markerless 3D scanning system based on the Helios2 time-of-flight 3D camera, with automatic alignment of multiple scans acquired from different viewpoints. The proposed system integrates data acquisition, raw coordinate conversion, point cloud preprocessing, including voxel downsampling, statistical outlier removal, and surface normal estimation using Open3D and a coarse-to-fine automatic registration strategy combining FPFH descriptor matching with RANSAC initialization and ICP refinement, followed by voxel-based fusion of aligned views. The system was experimentally evaluated on three cardboard filament-box objects representing a realistic robotic handling scenario. The reconstructed models were compared against nominal reference data obtained from a commercial Revopoint Miraco markerless scanner using surface deviation analysis. The evaluation yielded an overall mean surface deviation of 0.04 mm and a standard deviation of 1.34 mm, demonstrating that the proposed innovative Helios2 workflow can produce markerless multi-view reconstructions quantitatively comparable to those of an existing commercial scanning device. The results confirm the practical feasibility of automatic markerless alignment for ToF 3D scanning and indicate the suitability of the developed system for robotic perception applications where moderate geometric accuracy and reduced scene preparation are prioritized over ultra-high metrological precision.
Vodilka et al. (Tue,) studied this question.