Airhole is the most common apparent quality problem in reinforced concrete structures. Limited by the complex site environment and the computing power of equipment, existing evaluation methods have challenges such as poor accuracy and slow operation efficiency. In this paper, a quantitative assessment method of airhole defects based on hierarchical fusion architecture of point clouds is proposed, to realize efficient quantitative assessment of the apparent quality of concrete from end to end. Firstly, the set, shape, and depth feature information of the point cloud of the target scene were extracted, and a new hierarchical fusion architecture of multi-dimensional information of point cloud was given. Secondly, a planar linear search method based on depth line distortion is proposed to effectively overcome the influence of environment and noise in defect detection. Then, in order to reduce the influence of shooting angle and other interference information, the maximum heavy plane defect volume quantification model was established. In addition, to address the issue of key defect point loss during oblique scanning, a compensation strategy is proposed to improve the evaluation accuracy of different shooting angles. Finally, the accuracy and robustness of the proposed method are verified by the comprehensive evaluation index of defects and field experiments. The results show that the proposed method has a good evaluation effect on stomatal defects under various conditions, with a front scan error of less than 6. 0% and a compensation error for oblique shooting of less than 19. 8% . This method can provide an effective reference for on-site construction quality assessment.