This invention discloses a pure vision-based 3D reconstruction and adaptive completion method in a collapse environment, belonging to the field of
computer vision and
emergency rescue 3D reconstruction technology. The method completes initial 3D reconstruction by acquiring data through
binocular vision, constructs an anisotropic
point cloud density distribution field to achieve accurate detection of low-density blind spots, establishes safe and reachable spatial constraints based on an implicit distance field, and optimizes the completion viewpoint by fusing an
observability model. Map iteration is completed through local reconstruction and
point cloud updates, and a closed-loop completion mechanism is formed using
point cloud density as feedback until the reconstructed model meets the integrity requirements. This invention solves the problems of permanent voids in reconstruction caused by viewpoint blind spots, poor consistency between completion results and the real scene, and insufficient safety in the reconstruction process in existing technologies in collapse environments. It can achieve complete and high-precision autonomous reconstruction of 3D models of collapse disaster sites, providing reliable spatial data support for
emergency rescue decision-making.