Evaluation of Plane Detection with RANSAC According to Density of 3D Point Clouds
This work addresses the trade-off between scanning speed and point density in 3D scanning, but it is incremental as it focuses on evaluating an existing method rather than introducing new techniques.
The paper tackled the problem of evaluating plane detection using RANSAC on 3D point clouds by conducting experiments with varying parameters and point densities, finding that the number of detected planes differed between high and low density data under the same parameters.
We have implemented a method that detects planar regions from 3D scan data using Random Sample Consensus (RANSAC) algorithm to address the issue of a trade-off between the scanning speed and the point density of 3D scanning. However, the limitation of the implemented method has not been clear yet. In this paper, we conducted an additional experiment to evaluate the implemented method by changing its parameter and environments in both high and low point density data. As a result, the number of detected planes in high point density data was different from that in low point density data with the same parameter value.