Aplitop. Land Surveying and Civil Engineering Software. video-library

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Video Library
From Scan to Deliverables: SHARE3DCAM SLAM + Aplitop Tcp PointCloud Editor in Action
From Scan to Deliverables: SHARE3DCAM SLAM + Aplitop Tcp PointCloud Editor in Action
 Training English
Webinar | From Scan to Deliverables: SHARE3DCAM SLAM + Aplitop Tcp PointCloud Editor in Action
Webinar | From Scan to Deliverables: SHARE3DCAM SLAM + Aplitop Tcp PointCloud Editor in Action
 Webinars English
 tcpPointCloud Editor

Join SHARE3DCAM and Aplitop for a free live webinar showing the complete workflow from SLAM scanning in the field to professional CAD-ready deliverables. 

tcpMDT | Leading Civil Engineering and Surveying Solution
tcpMDT | Leading Civil Engineering and Surveying Solution
 Promotional English
 tcpMDT

TcpMDT is a tool that allows you to process topographic surveys, interpreting different data sources, in order to analyze and design civil engineering projects.

 

tcpMDT Photovoltaic | Flexible Structure Tracker
tcpMDT Photovoltaic | Flexible Structure Tracker
 Training English
 TcpMDT, TcpMDT Photovoltaic

The latest version of tcpMDT Photovoltaics, the specialized software for optimizing earthworks in solar farms, now includes a new type of tracker: adaptive trackers.

tcpGPS | General Laser GLRM GNSS Receiver Configuration
tcpGPS | General Laser GLRM GNSS Receiver Configuration
 Training English
 TcpGPS

This video shows the available configuration options for the General Laser GLRM receivers.

 

tcp PointCloud Editor | AI Classification and Segmentation of Point Clouds in Indoor Scenes
tcp PointCloud Editor | AI Classification and Segmentation of Point Clouds in Indoor Scenes
 Training English
 Tcp PointCloud Editor
In this video, we show how to classify points in an indoor point cloud using both automatic and manual tools. A specifically trained artificial intelligence model is used to classify points into categories such as floor, ceiling, walls, windows, doors, chairs, and tables. In addition, the categories are customized, and their names and colors are adjusted. ...
tcp PointCloud Editor | AI Classification and Segmentation of Point Clouds in Outdoor Scenes
tcp PointCloud Editor | AI Classification and Segmentation of Point Clouds in Outdoor Scenes
 Training English
 Tcp PointCloud Editor
In this video, we use artificial intelligence to classify points in an outdoor point cloud. A trained model categorizes points into various classes such as terrain, buildings, vegetation, vehicles, and street lighting. In addition, categories are customized by adjusting names and colors. Automatic classification results are examined and errors are corrected ...
tcp PointCloud Editor | Drawing on an Indoor Point Cloud
tcp PointCloud Editor | Drawing on an Indoor Point Cloud
 Training English
 Tcp PointCloud Editor
In this video, we explore the various tools offered by tcp PointCloud Editor for CAD drawing. We start working with a sorted point cloud, activating only the Walls category to model the building structure. We use the Layer Manager to create a new layer for the Floor Plan, assigning ita name, color, and line thickness. We draw a 3D polyline on the walls ...
tcp PointCloud Editor | How to Classify Ground Points from a LiDAR Flight (AI Model)
tcp PointCloud Editor | How to Classify Ground Points from a LiDAR Flight (AI Model)
 Training Español
 Tcp PointCloud Editor

This video shows how, after filtering out the noise, the point cloud is classified using an artificial intelligence model, segmenting it into different categories. This makes it easy to identify elements in the environment and isolate each category for further analysis or further work.

 

tcp PointCloud Editor | How to Classify Ground Points from a LiDAR Flight (CSF Filter)
tcp PointCloud Editor | How to Classify Ground Points from a LiDAR Flight (CSF Filter)
 Training English
 Tcp PointCloud Editor
In this video, we are going to clean the noise points of a LiDAR flight. We will start by removing the high noise, selecting it carefully so as not to lose definition in the important data. Then, we will filter out the intermediate and first of several returns, since they are not part of the terrain. This will reduce the number of points and speed up ...
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