• • Centimeter-level reconstruction precision achieved on natural field scenes, enabling passive 3D imaging where active illumination is impractical or prohibited, such as covert surveillance or heritage site documentation.
• • The iterative optimization framework resolves the π ambiguity and target discontinuity without neural network training, reducing computational overhead and eliminating the need for large labeled datasets that plague deep learning approaches.
• • Scale normalization strategy globally aligns multi-frame point clouds, enabling dynamic video reconstruction with consistent metric scale—critical for applications requiring temporal depth tracking, such as vehicle navigation or fluid surface monitoring.
• • The method operates passively under natural illumination, removing dependence on active light sources and scanning mechanisms, which lowers power consumption and hardware complexity for long-duration outdoor deployment.