We present an enhanced 3D reconstruction algorithm designed to support an autonomously navigated unmanned aerial
system (UAS). The algorithm presented focuses on the 3D reconstruction of a scene using only a single moving camera.
In this way, the system can be used to construct a point cloud model of its unknown surroundings. The original
reconstruction process, resulting with a point cloud was computed based on feature matching and depth triangulation
analysis. Although dense, this original model was hindered due to its low disparity resolution. As feature points were
matched from frame to frame, the resolution of the input images and the discrete nature of disparities limited the depth
computations within a scene. With the recent addition of the preprocessing steps of nonlinear super resolution, the
accuracy of the point cloud which relies on precise disparity measurement has significantly increased. Using a pixel by
pixel approach, the super resolution technique computes the phase congruency of each pixel's neighborhood and
produces nonlinearly interpolated high resolution input frames. Thus, a feature point travels a more precise discrete
disparity. Also, the quantity of points within the 3D point cloud model is significantly increased since the number of
features is directly proportional to the resolution and high frequencies of the input image. The contribution of the newly
added preprocessing steps is measured by evaluating the density and accuracy of the reconstructed point cloud for
autonomous navigation and mapping tasks within unknown environments.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yakov Diskin and Vijayan K. Asari
"Dense point-cloud creation using superresolution for a monocular 3D reconstruction system", Proc. SPIE 8399, Visual Information Processing XXI, 83990N (May 1, 2012); doi:10.1117/12.919620; http://dx.doi.org/10.1117/12.919620