Three-Dimensional Image Reconstruction via Mixed Optical Flow Methods

• Main Authors: Tai-WeiLi, 李岱維
• Other Authors: Ta-Chung Wang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/gtjm46

碩士 === 國立成功大學 === 航空太空工程學系 === 107 === Three-dimensional (3D) image reconstruction is the process of building a 3D model from images. It is widely used in architecture, art, video game development, and healthcare. For example, a patient's eyes can be scanned and the resulting data can be used to 3D print an artificial cornea. This would greatly reduce the cost and waiting time for corneal transplantation. The depth information of an object is derived from the geometric relationship between two images. To ensure that the point on the object is the same in two different images, feature point matching is typically used. Although it has good robustness, the calculation takes a long time. Optical flow, which refers to the motion of an object in the image, can reduce calculation time. This thesis uses two optical flow methods, namely FarneBack and FlowNet (using a convolutional neural network). These two methods can obtain the detailed features and general appearance of an object, respectively. The experimental results are visualized as a 3D surface plot. The 3D image reconstruction of a small object is performed.