Abstract: In the process of bridge structural damage detection, most of the recognition of complex defect morphology relies on two-dimensional images, only considering information such as surface grayscale and texture, resulting in low Mean Average Precision(m_AP) of the recognition results. Therefore, a real-time registration algorithm based on point cloud data was proposed for the recognition of complex defect morphology in bridge structures. Firstly, laser scanners and sensor equipment were applied to collect three-dimensional point cloud data of bridge structures, and density-based clustering algorithms was used for point cloud data clustering and segmentation to achieve point cloud data denoising processing. The Soft Assignment Cost (SAC) registration algorithm and improved Iterative Closest Point (ICP) registration algorithm were used for real-time registration of point cloud data, complex defect area detection was completed by considering three-dimensional attributes such as depth and height. Finally, the structural displacement function separately for the measurement points in the defect area was calculated, and the specific defect morphology was identified based on this. The experimental results showed that the m_AP value obtained from the application of the proposed method for complex defect morphology recognition was higher than 0.92, which basically met the requirements of bridge detection.