Point Cloud Processor And Comparison

About Client

HELIX is increasing value in the built world by creating the foundation for collecting and understanding data quickly, easily and affordably–not just for hundreds of buildings but for all the buildings at a fraction of the normal time and cost.

Domain: BIM, CAD, Cloud, Microservices

Location: US

Challenges

  • To optimize massive point cloud data (generally produced by LIDAR scanners for buildings) and to process them in a low memory pod.
  • Compare point cloud with 3D mesh and create deviation maps.

Solutions

  • To resolve the memory issues involving the processing of a massive point cloud, we built a streamable version of the point cloud processor. We designed all our point cloud processors to support the stream mode. The new point cloud processor seamlessly works on 2GB of RAM without compromising performance, even if the point cloud contains billions of points.

  • Comparing point cloud and a 3D mesh is really a simple task, But writing a deviation mesh was a challenge. So we applied some edge algorithms which resulted in output pretty much as expected. Refer images for inputs and outputs.

 

Fig. 1: Input point cloud

 

Fig. 2: Input mesh

 

Fig. 3: Output mesh

Fig 3 : Output mesh: When compared point cloud and mesh in the above images the result is as follows. The portion in Red is actually a deviation between point cloud and mesh, meaning it is present in mesh but not present in the point cloud.

Language and SDK

C++, Python

Contact Us

BIM Application Development
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