Multi-View Omnidirectional Vision/Structured Light for High-Precision Mapping (www.mdpi.com)

The paper presents a combined multi-view omnidirectional vision and structured-light system for high-precision 3D mapping and reconstruction. The authors build a virtual simulation framework to design and test a reconstruction pipeline that fuses omnidirectional imagery (wide field-of-view, reduced occlusions) with active structured-light patterns to recover metric depth. They report systematic experiments—simulation and real-world—evaluating reconstruction quality across different object shapes and distances, distance-measurement accuracy, and robustness to viewing geometry, showing that the hybrid approach yields notably higher precision than passive omnidirectional multi-view alone, especially on texture-less or reflective surfaces. Key technical points: the method integrates calibration and multi-view geometry for catadioptric/fisheye-like sensors with structured-light depth cues to resolve scale and local ambiguities. The virtual simulator enables controlled evaluation and calibration refinement before deployment. Fusion of ring/line structured light with omnidirectional captures increases effective baseline and coverage, reducing required viewpoints and improving fidelity in challenging scenes. Implications for the AI/ML community include more reliable training data for 3D perception, better metric supervision for learning-based reconstruction, and practical mapping for robotics, inspection, and AR/VR where compact, mobile platforms benefit from omnidirectional coverage plus active depth sensing.