Reliable volumetric representation of the nasal cavity is crucial for enabling quantitative assessment in Functional Endoscopic Sinus Surgery (FESS), yet for most patients the evaluation of their anatomy remains largely qualitative and subjective. While computed tomography (CT) scans can provide 3D anatomical information, their routine use is impractical due to radiation exposure concerns and cost constraints, underscoring the need for a non-invasive alternative. Computer vision methods offer a promising solution for reconstructing sinus anatomy from routine endoscopic video. Current methods rely on Structure-from-Motion (SfM), however, this relies on point correspondences that struggle with photometric inconsistencies inherent to endoscopic imaging, reducing robustness and generalizability. Several sinus reconstruction approaches attempt to mitigate this through learning-based and patient-specific approaches, but suffer from error propagation, leading to inaccurate 3D representations. Optimization-based approaches further introduce excessive training times, limiting their practicality. In this work, we revisit simpler techniques for sinus reconstruction and augment them with track-any-point foundation models to develop a training-free, vision-based 3D reconstruction method. Our approach leverages SfM poses and local point-tracks to generate depth information, recovering a globally consistent structure without fine-tuning requirements. We evaluate our method on six pre-operative endoscopic sequences with respect to the ground-truth CT scan. Our results show that this method improves global geometric accuracy by reducing both point-to-point and pose errors from prior work. Our vision-based approach improves spatial consistency and accuracy in sinus 3D reconstruction, enabling non-invasive postoperative monitoring and seamless clinical integration, offering physicians data-driven insights for improved surgical decision-making.