On multi view texture mapping of indoor environments using Kinect depth sensors
At present, research on reconstruction and coloring of 3D models is growing rapidly due to increasing availability of low cost 3D sensing systems. In this paper, we explore coloring of triangular mesh models with multiple color images by employing a multi view texture mapping approach. The fusion of depth and color vision data is complicated by 3D modeling and multi viewpoint registration inaccuracies. In addition, the large amount of camera viewpoints in our scenes requires techniques that process the depth and color vision data efficiently. Considering these difficulties, our primary objective is to generate high quality textels that can also be rendered on a standard hardware setup using texture mapping. For this work, we have made three contributions. Our first contribution involves the application of a visibility map to efficiently identify visible faces. The second contribution is a technique to reduce ghosting artifacts based on a confidence map. The third contribution yields high detail textels by adding the mean color and color histogram information to the sigma outlier detector. The experimental results show that our multi view texture mapping approach efficiently generates high quality textels for colored 3D models, while being robust to registration errors.
2014 International Conference on Computer Vision Theory and Applications (VISAPP)