  This article describes novel approaches to quickly estimate planar surfaces
from RGBD sensor data. The approach manipulates the standard algebraic fitting
equations into a form that allows many of the needed regression variables to be
computed directly from the camera calibration information. As such, much of the
computational burden required by a standard algebraic surface fit can be
pre-computed. This provides a significant time and resource savings, especially
when many surface fits are being performed which is often the case when RGBD
point-cloud data is being analyzed for normal estimation, curvature estimation,
polygonization or 3D segmentation applications. Using an integral image
implementation, the proposed approaches show a significant increase in
performance compared to the standard algebraic fitting approaches.
