Adenoid hypertrophy (AH) is a key contributor to pediatric obstructive sleep apnea syndrome (OSAS). Physicians rely on nasopharyngeal endoscopy to identify AH and the obstruction of adenoid to the airway. However, due to the limitations of 2D endoscope images, physicians have to subjectively infer the 3D structure of the adenoid region, which heavily relies on their expertise and the angle at which the adenoids are observed. The adenoid surface is composed of mucosal tissue covered by nasal secretions, and thus strongly reflective, smooth, and lack features. Furthermore, the endoscope image of adenoid is relatively blurred. Based on these unique characteristics of the adenoids, this paper introduces a multi-view stereo algorithm based on endoscopic image sequences of the adenoid nasopharyngeal cavity. The algorithm employs multi view stereo matching to first estimate the depth maps corresponding to the images. Subsequently, it utilizes mesh surfaces to fit the rough depth information in the depth space, and thereby generates the smooth and refined depth maps. Eventually, fusing the obtained depth maps leads to a dense and precise reconstruction of the adenoid region. Both synthetic and real experimental results demonstrate that the algorithm can achieve accurate, dense, and smooth reconstruction of the adenoid area, surpassing the existing reconstruction algorithms significantly.