Point cloud data has become an important tool for professionals in industries such as surveying, construction, mining, and infrastructure management. It provides a wealth of information that can be used to make informed decisions. However, processing this data can be a daunting task, especially when working with large amounts, sometimes from multiple sources. That's where FJD Trion Model comes in. This software solution is designed to seamlessly process point cloud data from a variety of hardware, reducing processing time, and improving data quality. In this blog post, we will do a deep dive of the features that make FJD Trion Model the ultimate point cloud processing solution.
Multiple data sources supported
With the increasing prevalence of LiDAR technology, capturing point cloud data can be done in various ways. Whether you opt for a drone-mounted LiDAR, handheld scanner, or terrestrial scanner, leveraging point cloud data effectively requires appropriate post-processing in the office. Trion Model offers compatibility with commonly used point cloud data formats such as .las, .ply, .pts, and .e57. This allows you to efficiently process any field LiDAR data and obtain the desired outcomes quickly.
Next, we'll take a closer look at key features of the Trion Model software.
Data Processing Overview
Point Cloud Rectification
Accuracy is key when it comes to point cloud data. However, sometimes the data collected can be tilted, leading to inaccuracies in measurements. Rectify the point cloud data automatically when you import data into Trion Model, correcting any tilt in the Z-axis direction. This improves the quality of the data and enhances accuracy, as the data can be correctly aligned to the intended reference system.
Point Cloud Registration
Multiple scans of a location can sometimes result in data that are in different coordinate systems. With FJD Trion Model's point cloud registration feature, data from multiple scans of the same location can be matched accurately into the same coordinate system. Trion Model uses Iterative closest point (ICP) algorithms to calculate precise points and speed up calculations. This not only reduces errors in manual point selection but also improves the accuracy of dataset stitching.
Quickly evaluate point cloud data quality by taking a slice of the 3D point cloud data can be converted into a 2D view for further analysis. Such as point cloud layering, thickness and other quality assessment; object surface abnormality detection; object shape, size, and feature extraction.
In addition to basic functions, Trion Model also comes with modules designed for Construction, Mining, and Forestry.
Automatically classify indoor and outdoor point cloud data into different categories such as ground, trees, ceilings, walls, floors, etc., to extract key features and information in the scene for targeted analysis.
Calculate volume with a few simple clicks. Generate reports for various scenarios such as filling and excavation, stockpiles, mine tunnels, mine roads, and karst caves.
2D Graphics Drawing
Automatically extract the plane contour lines in one click, or manually draw the vector line based on the section to complete the reconstruction of the 2D drawing. This is especially helpful in building facade measurement, interior design, historical building protection and more.
The forestry module enables you to extract ground points and vegetation in the forest model and separate the vegetation part,
automatically extract the parameters such as tree position, height, crown width, and breast diameter, etc., Generate and export single tree data reports for forestry investigation, management and planning.
Overview of FJD Trion Model Versions
Choose from five different software version to configure the functions that fit your application.
Use Case Highlight
Finally, we'll take a look at 4 use cases of Trion Model to see how point cloud data from various sources can be processed to produce 2D and 3D results for construction/demolition, stockpile measurement, forestry survey, and mine tunnel volume calculation.
Obtain complete point cloud data through on-site measurement, and output the drawings of the demolished factory building.
- Fieldwork: The operator used a handheld lidar scanner to scan the factory building to obtain on-site 3D point cloud data.
- Office: Imported the collected data into FJD Trion Model, use the point cloud registration and merging functions to merge multiple datasets; used Trion Model to automatically extract the vector lines, and quickly create the floor plan, which greatly reduced the processing hours, while meeting project requirements without additional investment in third-party software.
Through on-site measurement, quickly obtained the stockpile volume and generated a volume report.
- Fieldwork: Used airborne LiDAR equipment/handheld laser scanner/static scanner to scan the stockpile to obtain on-site 3D point cloud data.
- Office: Achieved high accuracy and efficiency in volume calculation by importing data into FJD Trion Model. Utilized the point cloud segmentation function to isolate the relevant pile area and accurately measure its volume. Generated a comprehensive report for easy reference. FJD Trion Model greatly enhanced precision and saved time in the calculation process.
Obtain forestry point cloud data through on-site measurement, and generate a report on single tree DBH, tree height and crown width.
- Fieldwork: Used airborne LiDAR equipment / handheld laser scanner/static scanner to scan the forest to obtain on-site 3D point cloud data.
- Office: FJD Trion Model efficiently integrated information collected from various platforms on site using point cloud registration and merging functions. By applying the forestry data extraction module, the team was able to classify forest point cloud data and separate ground points. This allowed the operator to automatically segment individual trees and extract essential data such as tree height, diameter at breast height, crown width, and tree species. Furthermore, the team conducted additional calculations to determine carbon storage in forest areas. These advancements have greatly reduced measurement cycles, labor intensity, costs, and environmental impact, while significantly improving overall efficiency.
Obtain the complete point cloud data of the underground mine through on-site measurement, and calculate the enclosed volume of a mine tunnel.
- Fieldwork: Used a handheld scanner/static scanner to scan the mine to obtain on-site 3D point cloud data.
- Office: Imported the data into the FJD Trion Model, and used the point cloud registration and merging functions to obtain a complete mine road point cloud; a closed surface can be generated based on the point cloud data triangulation , and then volume could be calculated with just one click to generate detailed 3D data for digital mining and management, effectively helping the digital development of underground space and mines.