After a few long days onsite, you've recorded every detail of your client's newly constructed building using a laser scanner. All of the data that you've captured is ideal for measuring the existing conditions and will form the basis of a precise and accurate virtual model. But when you get back to the office to process the data, you're overwhelmed by a dense fog of millions of data points. How can you sift through the data to determine which points are integral to your model, and which represent environmental noise that will skew the final deliverable? Traditional methods require painstaking manual effort, and even small inaccuracies can affect the final deliverable. What if there were a way to streamline this process that eliminates the complexity and saves hours of work?
Cleaning point clouds removes environmental “noise” that may come from the scanning process, caused by reflections, landscaping elements, differences in surface texture, scanner movement, or uncalibrated equipment. It also reduces the size of the point cloud, which allows you to handle the file more efficiently in your preferred design tool. It also produces a much more accurate, true-to-life representation of the scanned environment, ensuring that the output can be processed further.
Point cloud cleaning involves several manual steps to remove inaccuracies, including zooming in to delete inaccurate points, using segmentation tools to trim data, and more. You may run into processing delays due to the computational demands of large point cloud files. In addition, identifying and removing unwanted points can be complicated, especially when dealing with intricate details and complex environments. How can you easily distinguish between what is random noise and small, genuine details? How can you quickly remove points from objects that are partially obscured by other objects? Even though it's a critical step to improving the quality of the data for downstream tasks, point cloud cleaning is a time-consuming and labor-intensive process.
As data from 3D laser scans becomes more valuable in different types of applications, several rapidly evolving improvements are addressing these issues and shaping the way that point cloud files can be cleaned. Some current trends include:
Our new, cloud-based RoboClean simplifies the point cloud cleaning process. With RoboClean, users can retain their local computing power while taking advantage of a powerful, automated solution that analyzes and cleans point clouds in a matter of minutes, instead of hours. It also comes with a direct integration into RoboFlat for concrete floor flatness / levelness analysis. Now you can clean and test the point cloud in one location. Start a free trial of RoboClean today!