The three dimensional reconstruction of surface defects on complex-shaped components is challenging due to the varying curvature, non-uniform illumination, the absence of regular reference features as well as the low accuracy and slow speed of binocular vision matching. This study proposes a fast stereo matching algorithm named GAF-Census based on Gradient-Aware Fusion (GAF) to achieve the high-precision three dimensional reconstruction of dimensional quantification defective areas. First, an SIFT feature-guided disparity range constraint mechanism is introduced to narrow the search space and improve efficiency at the cost computation stage. Meanwhile, an adaptive Census transform based on key point median filtering is adopted, which replaces the contaminated center pixels and enhance the noise immunity with a dynamic threshold. Additionally a gradient-aware cost fusion mechanism is constructed by strengthening the gradient constraints in the edge regions to accurately locate defect contours, while the Census weight is increased in weak-texture regions to improve matching stability, thereby significantly enhancing the matching accuracy in key areas. Finally, in order to address the difficulty of defect quantification for complex-shaped components, a global fitting method based on a quintic polynomial combined with numerical integration is proposed, enabling the automated and high-precision measurement of defect dimensions. Experimental results show that the proposed GAF-Census algorithm achieves a mismatch rate as low as 5.25% for both standard and custom samples, and the computational efficiency is also improved by 96.7% compared to the conventional AD-Census algorithm. Furthermore the system can detect defects with a minimum width of 0.354 mm, and the average relative errors of defect width and length measurements are only 0.483% and 0.271%, respectively. Last but not least, the algorithm maintains the high reconstruction completeness and measurement stability under the complex lighting and geometric variation conditions, which demonstrates the strong practical applicability and provides a reliable technical solution for the automated high-precision monitoring of surface defects in the complex-shaped components.