3D Point Clouds are becoming ubiquitous - people collect them with scanners, with drones, and even with phones. This high-density, high-accuracy reality capture promises to revolutionize the fields of engineering, operations and design by allowing the creation of virtual worlds, and the mining of 3D data for multiple purposes as a project progresses. Even self-driving cars will become reality capture machines as they collect terrabytes of data while navigating streets.
However, collecting the point cloud is only the first step. To be truly useful, the 3D data must be converted into CAD information - otherwise it's limited to being a visualization tool. And while many tools are being created to extract features from 3D point clouds, there is currently no agreed upon definitions of how 3D features are to be defined. This greatly limits the ability to transfer information between appplications, as opposed to raw data.
So, the purpose of the OpenLSEF initiative is to create a common language describing how features in 3D point clouds should be defined. By establishing definitions and terminology, products from providers can be standardized, designers can expect consistency, self-driving cars can share high-definition maps and tool-makers can focus on ensuring extraction algorithms return expected results. It's frustrating to be a drafter (or AI) trying to learn what curbs (or kerbs) look like if no one can agree whether flow line or back-of-curb is the defining feature.
OpenLSEF is a user-created initiative focusing on standardizing extraction definition in the AEC (architecture, engineering and construction) field, as well as transmission, utilities and BIM (building information management). These are living standards relating to the meaning of extracted data, as opposed to simply focusing on actual file format standards. As such, OpenLSEF is data-format agnostic and is meaningful whether you deal in DWG, DGN or SHP files.