The two real water environments of the Bohai Sea and the South China Sea are simulated in this study by numerically setting the two parameters of salinity and turbidity, and experiments are conducted using this as the medium to obtain the original point cloud data, using the methods of threshold segmentation, refraction correction, and point cloud filtering for processing and reconstruction. In the follow-up error analysis, the k-d tree algorithm is used to calculate the error between the reconstructed point cloud and the standard point cloud, and the cause and impact are analyzed. The results show that: 1) the detection of underwater targets is unaffected by changes in salinity. The average error of the target changes within 1 mm in water environments of 30 PSU and 35 PSU. 2) Changes in water turbidity primarily affect light backscattering in the underwater transmission process, resulting in more noise points in the target point cloud and lowering imaging quality. 3) When the target’s detection distance is fixedly increased, the detection effect gradually deteriorates due to the large attenuation of the water body, and the target point contour of the cloud becomes blurred, The mean error effect increases as the number of effective point clouds decreases. In conclusion, the turbidity influence is dominant in point cloud reconstruction under different water environments, the regularity is not obvious, and the detection distance influence is controllable.