The assembly of large aerospace structures is a critical step in the manufacturing process, with hole-shaft connections being a common configuration, and the accuracy of their pose measurement directly affects assembly quality. Traditional visual measurement methods are limited by image resolution, especially under long-distance working conditions, making it difficult to meet high accuracy requirements. Furthermore, existing super-resolution technologies are rarely used in industrial measurement and lack optimization methods specifically for the characteristics of hole-shaft images. This article proposes a pose measurement method for aerospace hole-shaft structures based on geometry-aware and pixel-accurate adjustable super-resolution. First, an image degradation strategy is optimized to address the characteristics of hole-shaft images, and a dedicated super-resolution dataset is constructed. Second, a geometry-aware and pixel-accurate adjustable super-resolution network is designed, achieving a balance between pixel-level fidelity and geometric structure quality by introducing low-rank adapters (LoRA) and a loss function based on context loss and edge enhancement gradient variance loss. For pose calculation, a spline feature extraction algorithm is proposed, and a pose optimization model based on forward projection geometric distance and hybrid product is established to address geometric deviations caused by manufacturing errors. Experimental results show that this method improves the position measurement accuracy from 0.008 mm to 0.003 mm and the attitude measurement accuracy from 0.14° to 0.07°, while significantly enhancing measurement stability. In practical applications, this method has been successfully used in helicopter lift system assembly, achieving an attitude adjustment accuracy better than 0.05 mm and an assembly time of less than 30 minutes, verifying its effectiveness and practicality.