Reconstructing complete 3D geometry from monocular clinical endoscopic videos is challenging due to weak texture, repetitive tissue patterns, and severe illumination artifacts. Although emerging dense matching methods exhibit improved resilience to textureless regions, they often produce abundant spurious correspondences across non-overlapping views, which corrupts the correspondence graph and cause structure-from-motion (SfM) pipelines to fail. In this work, we propose a framework for the construction of dense correspondence graph that leverages explicit temporal locality, parallax-driven geometric constraints, and loop- closure revisiting to enable reliable SfM for monocular endoscopic videos. Instead of exhaustively connecting all frame pairs, the approach effectively suppresses invalid inter- frame correspondences while preserving essential long-range geometric relations critical for stable reconstruction. Combined with illumination-aware masking and SfM initialization adapted to endoscopy, the proposed framework achieves substantial improvements in registration robustness and reconstruction completeness for both phantom and real clinical datasets.