Real-Time Surface Fitting to RGBD Sensor Data
This work addresses the need for efficient surface fitting in applications like normal estimation and 3D segmentation for robotics or computer vision, though it is incremental as it builds on existing algebraic fitting approaches.
The paper tackles the problem of real-time planar surface estimation from RGBD sensor data by reformulating algebraic fitting equations to pre-compute variables from camera calibration, resulting in significant time and resource savings, with an integral image implementation showing a notable performance increase over standard methods.
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.