Integration of Panorama Image and Laser Scanning to Build Large-Scale Virtual Reality Models

• Main Authors: Lun-Cheng Chu, 朱倫成
• Other Authors: Ta-Te Lin
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/79743967712142371211

碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 97 === This research proposes a method to reconstruct real-world objects in large-scale virtual reality by integrating the information from panoramic images and data acquired with a laser range sensor. Compared with most virtual reality applications, the proposed method is capable of building large-scale virtual reality models such as a campus environment including many buildings, and the reconstructed building''s appearance is photorealistic. The procedure for building the virtual reality models mainly consists of three phases: the acquisition of the space information, reconstruction of 3D model, and integration of multiple scenes to form a large-scale virtual reality model. In the first phase of data acquisition, the complexity of the model reconstruction is reduced by properly arranging the acquired point cloud data. Therefore, the reconstruction of the 3D model is nearly real time. The time required to acquire the 3D point cloud data for a single scene was about 12 minutes. The reconstruction of 3D virtual reality model utilizes the well organized collected spatial data for meshing which also applies Delaunay triangulation for a data reduction to 60%. The texture mapping process is also simplified based on the correspondence of boundary conditions in the panorama image and the 3D model. Moreover, a global registration method was developed. It initially coverts the 3D spatial data into 2D information and then applies spatial propagation and shape analysis filters for enhancing the matching probability. When applying this method on a reconstruction of a map 454.9 meters in length, the error was 0.1%. The matching probability is affected by the complexity of environment to be reconstructed. When applying the method on an environment with regular features, such as the NTU gymnasium with mostly regular wall surfaces and with an average scanning distance of 46.8m and standard deviation of 8.8m, the matching probability was 100%. In a separate experiment for a more complex environment such as the NTU Royal Palm Blvd. with many trees and irregular structures, the matching probability decreases to 72%. The method was also successfully applied for the reconstruction of an indoor environment which is ten meters in width and height. Comparing with the iterative closest point (ICP) method, our approach has the advantage that it does not require initial guess for the registration of multiple scenes. The registration process can be accomplished in 5 minutes. Moreover, it can be completed in 10 seconds if the initial guess is given. The developed method can be used in various applications such as model reconstruction, tour guiding, and the city planning.