To help with making an emergency rescue plan for train accidents, a rapid 3D reconstruction
method of train accident scene based on a monocular image was proposed. Taking two
camera projection models for different application scenarios into consideration, the SIFT algorithm
was introduced to extract and match image feature with the CAD model of an accident train.
Geometric constraints between carriages were provided to transform the 3D reconstruction to
solving a nonlinear least square problem with constraints, by which the position and pose of accident
subjects were reduced at last. To quantitatively and qualitatively verify the calculation performance
of this method, the mimicked train accident scene and real train accident scene were
respectively used to carry out 3D reconstruction. The precise finite camera projection model was
applied in the mimicked train accident scene to carry out offline calibration, and the stable
pin-hole model was adopted in the real train accident scene to carry out auto calibration. Analysis
result shows that through quantitative analysis of mimicked scene the maximal and average relative
error of 8 nodes for measurement in reconstructing two carriages are 4.54% and 1.85% respectively.
Through qualitative analysis of the real scene, the 3D reduction of position and pose
for carriages can also be realized with combining the topographic information correction. The
whole accident environmental panorama can be reduced visually with the help of 3D visualization
engine. This method can also be used in developing emergency rescue electronic sand table for
train accident analysis and safety education
