This video shows how to detect railway tracks in LiDAR point clouds using VRMesh Survey. Rail Lidar Survey-LiDAR at 45 MPH - Duration: 0:56. Reality IMT Inc. VRMesh Studio A comprehensive solution covering automatic point cloud classification, feature extraction, and accurate point cloud meshing. It provides a streamlined workflow for the AEC industry. It includes all features in VRMesh. Features and Benefits. Automatic point cloud classification and feature extraction. This video shows point cloud classification, footprint extraction, 3D building creation, and grid surface generation in VRMesh Survey. You can import the res.
VRMesh Survey: An advanced solution for point cloud classification and feature extraction. It classifies vegetation, building roofs, and ground with no limitations on complex topography. It detects building footprints, powerlines, poles, tree crowns, railways and curbs. Description: Vrmesh Survey is an intelligent solution for automatic point cloud classification and bare-earth extraction. It automatically and accurately classifies LIDAR point cloud s into ground, vegetation, building, and others. More than 90% identification jobs will be done in a one-click process.
The goal of this case study is to discover the comprehensive workflow VRMesh provides for 3D point cloud and mesh processing. Many users tell us it is always a big challenge when they get billions of points and need to measure the volume between two surfaces. Generally, they have to use more than one software program to finish the task. Especially with extreme variations in terrain, such as steep mountains where most programs don’t work well. We will demonstrate the steps that will allow you to complete the challenging tasks using just VRMesh.
We will start with two sets of point clouds that come from a summer terrain scan and a winter terrain scan of the same region. The task is to calculate the snow volume at the location of interest.
Step 1: Import Large Point Clouds
Use the Index/Attach command to load point cloud files into the window. The benefit of this command is that it only retrieves inquiring points into the window, which will speed up the processing of large point clouds. VRMesh can handle large datasets in excess of one billion points. The program has no limit concerning the point cloud and mesh size.
Step 2: Classify Ground Points
VRMesh will automatically identify vegetation, building roofs and ground in LiDAR data and produce well classified points, no matter whether the data gathered from steep slopes covered with dense vegetation or areas where there are very few ground points available. The composite commands can be finished by one-click batch processing.
Step 3: Convert Point Clouds to Meshes
Generally, there are two ways to create a bare-earth surface. You may directly create a grid surface to represent the bare earth, which is simple and fast, but is not accurate enough and therefore not suitable for steep slopes.
Another way is to triangulate point clouds into meshes which is more accurate in representing the bare-earth topography. In this case, we show you how to create a triangulated ground surface to better represent the volume difference.
You may decimate and smooth the point cloud before triangulation. After the point cloud is converted into a triangle mesh, you can use mesh repair and editing tools to seam gaps, fill holes, and reduce the number of triangles into an acceptable amount. VRMesh provides advanced triangle mesh processing tools for dealing with complex meshes. The final surface mesh is shown below. All details on the surface are kept accurate.
Step 4: Cut and Fillet Two Surfaces
To calculate the volume between two surfaces, we need to trim both surfaces, and then fillet the two surfaces into a watertight object.
Step 5: Calculate Volume
Now we are ready to analyze the snow volume and area of the target terrain surface.
The workflow presented in this case study can be used for many fields such as land surveying, mining, transportation, urban planning and architecture.
Over ten years of development, VRMesh has become the most powerful and comprehensive point cloud and mesh processing software product covering everything from automatic point cloud classification to feature extraction, accurate point cloud meshing and advanced mesh repair and editing.
Please watch the video here: https://youtu.be/cIgta7ulRtk
SEATTLE, WA, July 11, 2016 – VirtualGrid announces the availability of VRMesh v9.2, the latest version of its powerful 3D point cloud and mesh processing software. VRMesh v9.2 adds a new function which automatically traces curb edges and fits curbs seamlessly on a surface mesh. Furthermore, in this version, point cloud classification is able to classify point clouds not only acquired from airborne, terrestrial, or mobile laser scanners, but also generated from UAV images.
This new release provides a complete workflow solution for transportation and urban design users, including automatic point cloud classification and feature extraction tools, accurate surface generation tools, and powerful curb fitting tools. It greatly shortens the time for road design and makes reality computing much easier.
The key features added in version 9.2 include:
- Classify Points from Photos command to classify point clouds generated from photos
- Fit Curb command to automatically trace curb edges and fit curbs on a surface mesh
- Create Spline Patch commandto create a spline surface from two parallel curves
- Seam Patch command to join a patch into a target object seamlessly
- Indexing native file formats from Faro, Reigl, and Z+F scanners: *.fls, *.rdbx, and *.zfs
To celebrate the new release, VRMesh offers a limited-time discount– save 20% on all product options. Meanwhile, VRMesh has created a lite version of VRMesh Survey for users who need the classification functions only. For more details, please visit their website www.vrmesh.com.
VirtualGrid is the developer of VRMesh, an advanced 3D point cloud and mesh processing software tool. They focus on LiDAR remote sensing, reverse engineering, and rapid prototyping industries.