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.
@article{arxiv.2103.06644,
title = {Real-Time Surface Fitting to RGBD Sensor Data},
author = {John Papadakis and Andrew R. Willis},
journal= {arXiv preprint arXiv:2103.06644},
year = {2021}
}