Real-Time Surface Fitting to RGBD Sensor Data

TL;DR

This paper introduces a fast, algebraic method for real-time planar surface estimation from RGBD sensor data, significantly improving computational efficiency for 3D data analysis tasks.

Contribution

It presents a novel algebraic fitting approach that leverages camera calibration to pre-compute regression variables, enabling faster surface fitting from RGBD data.

Findings

Significant reduction in computation time for surface fitting.

Improved performance over standard algebraic fitting methods.

Effective in applications like normal estimation and 3D segmentation.

Abstract

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.