However to get a managable file size the areas had to be so small compared to the whole that it became unusable.Īfter investigating more I've realized that the LiDAR points seem to be about 0.2' apart. I've also tried making multiple surface models of smaller areas. However the file size of the surface model is monstrous.Įarlier today some folks in the C3D forum helped me learn how to limit the elevation range of the surface model, but that didn't reduce the size as much as I thought it would or needed it to. I've figured out how to bring it into Recap Pro, then into C3D, then make a surface model. I received a LiDAR point cloud that covers a very large area. Please let me know if I'm in the wrong place. I'm not sure if this should be here or the Civil 3D forum. Recap Photo giving some error while creating 3d model 1.ReCap 360 & ReCap 360 Ultimate Release Notes 1.Question - Correct Application for Recap 1.The best way to solve such issues is to provide human supervision to the classifier. These are the projects that we trained our classifier because a lot of the current users are processing similar projects.You may notice that your tall stockpiles were recognized as buildings and your sharp-roofed buildings were categorized as high vegetations. Currently, the classifier works the best on projects with flat-roofed buildings, scattered high vegetation, ground-level asphalt roads and with a GSD (ground sampling distance) of around 5 centimeters. so the program can start learning from instruction and experiences. We need to teach it which are trees and which are buildings, etc. Imagine the initial classifier as a newborn baby learning to understand it’s surroundings in a similar way that a human being learns.
With the extraction of geometric and color features, we are able to provide a fast classification routine to all Pix4D users.” – Nicolai Häni Teach the algorithm to learn “I am glad to be part of the team that developed the feature using machine-learning technology. Our next steps are to use more training data and post-process the results to provide cleaner and more reliable results.” – Carlos Becker “We are very happy to see the huge response and early adoption of the point cloud classification from our users.
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