Abstract
This paper addresses the generation and visualization of noisy 3D point clouds. The goal is to extract 3D information from image sequences in real-time and to transform the resulting noisy point clouds into a representation with dense surfaces and an intuitive character. Therefore a bridge is built from image sequence analysis to computer graphics. A point cloud is calculated from a single camera by using state of the art algorithms for egomotion estimation and dense 3D reconstruction. Due to inaccuracies in the measurement process and hence of the resulting depth maps, a degree of uncertainty of the 3D data is defined. This additional information can be used to filter the point cloud in space and time. We present an approach to filter and polygonize weighted point clouds and visualize it by mapping image texture on the resulting surface.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
