To solve the problems of insufficient estimation ability for the center of gravity of a vehicle and difficulty in vehicle reidentification in large bridge vehicle moving load monitoring, a fast fusion method of color image and point cloud based on point cloud grayscale image is proposed to improve the spatial positioning ability for vehicles and recognition ability for targets. The stereo calibration target was used to calibrate the position and attitude of cameras and point cloud collection devices with different viewing angles to obtain their relative positions and attitudes. Then, the calibration results to splice the point clouds collected from different perspectives were used to obtain a complete vehicle point cloud. The complete point cloud was converted to the color camera coordinate system and projected, extracting the point cloud grayscale image and realizing the registration of the color image and point cloud grayscale image. We adjusted the attitude to be consistent with the position and attitude of the vehicle entity in the color camera coordinate system. The mapping relationship between color pixels and 3D point cloud was established. The color information was associated with the point cloud to fuse the color images and point clouds. Using the fused color point cloud and camera imaging model, the virtual image of the vehicle in the color camera coordinate system can be obtained, providing a basis for vehicle recognition. The results demonstrate that compared with the sampling consistency algorithm, the proposed registration algorithm requires approximately 74.1% less time. Experiments reveal that the color point cloud generated by the proposed algorithm after data fusion has a high degree of restoration, proving the feasibility of the proposed algorithm. The proposed algorithm provides new ideas and methods for solving similar problems.