<p>High-precision three-dimensional geometric feature documentation is crucial for monitoring the structural health of the Great Wall, yet existing methods struggle to balance efficiency with geometric fidelity when processing massive, complexly point clouds. To address this, we propose a bioinspired stereoscopic feature extraction framework that mimics raptor visual perception. Validated on the Ming Great Wall, including the Jiayuguan Pass in Gansu Province and the Guangwu section in Shanxi Province, China. This method condenses complex morphologies into sparse yet recognizable feature sets, significantly enhancing representation efficiency while minimizing storage and rendering costs. These high-fidelity features facilitate downstream tasks such as contour extraction and solid modeling and offer diagnostic insights into heritage pathology by capturing geometric anomalies associated with surface erosion and structural instability. By harmonizing large-scale processing with microscale detail recognition, this work provides a scalable, automated solution for refined surveying and digital preservation of extensive linear cultural heritage sites.</p>