In image-guided surgery, endoscope tracking and surgical scene reconstruction are critical, yet equally challenging tasks. We present a hybrid visual odometry and reconstruction framework for stereo endoscopy that leverages unsupervised learning-based and traditional optical flow methods to enable concurrent endoscope tracking and dense scene reconstruction. More specifically, to reconstruct texture-less tissue surfaces, we use an unsupervised learning-based optical flow method to estimate dense depth maps from stereo images. Robust 3D landmarks are selected from the dense depth maps and tracked via the Kanade-Lucas-Tomasi tracking algorithm. The hybrid visual odometry also benefits from traditional visual odometry modules, such as keyframe insertion and local bundle adjustment. We evaluate the proposed framework on endoscopic video sequences openly available via the SCARED dataset against both ground truth data, as well as two other state-of-the-art methods - ORB-SLAM2 and Endo-depth. Our proposed method achieved comparable results in terms of both RMS Absolute Trajectory Error and Cloud-to-Mesh RMS Error, suggesting its potential to enable accurate endoscope tracking and scene reconstruction.