Can we expect point cloud models as an endpoint?

by Matt Ball on December 16, 2011

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The workflows of capturing and extracting digital reality from point clouds has taken some leaps forward recently, given advancements in both computer hardware and software. The increased speed at which these large volumes of data are processed, and the automation of feature extraction, greatly improve the utility of the data. While, the workflow aims at easier ingestion and manipulation in existing systems, might there be applications where the point cloud is the only model?

The technology will continue to advance to make data capture cheaper and more efficient, and software better able to handle larger data sets. The idea of dealing only in point clouds, with systems that just manipulate, query and visualize the cloud, has a very strong appeal for efficiency’s sake. While it’s not realistic that the point cloud will be the only endpoint for all applications, there is a case to be made that this could be a superior approach for many applications.

Rapid Reality Updates

In areas of our built environment, we are improving upon our ability to catalog the 3D geometries around us. As more points are captured, the location of individual points never changes, but with greater density of points over time we achieve greater accuracy and better representation of change. Reducing reality to clouds of points allows us to isolate the points that are gone when our infrastructure changes as well as the cloud of points that are new, lending new resolution to change analysis.

With increasing calls to rethink our cities for greater efficiency, we require the ability to query change in order to improve designs. Having point clouds as the capture and end point makes them the first place to catalog and quantify volume of change. There are a great many applications where only a detailed view and the volume of change are needed, leaving the analysis of impacts to other systems.

Points of Truth

Today’s workflows, whether GIS, CAD or BIM, all take in reality and manipulate it with specialized design and analysis tools. It’s not long before individual decisions and inputs differ from what’s real, and getting back to reality is often a painful process. With point clouds, the precision of the points provides indisputable 3D data that corresponds with a real location. Making the cloud of points the central point of truth for reality and all measurements makes sense, because it can be calibrated and revisited without the assignation to individuals that introduces politics into the equation for precision.

The question of accuracy and a point of truth has plagued the GIS and CAD divide up until now. Systems can’t adequately address workflows when there’s contention over which capture of reality provides the best truth. With point clouds the argument becomes moot, because the point cloud is a direct capture of reality. Here, the introduction of machine measurement could provide an answer beyond the tools we use to visualize and enhance the data source.

Meaningless Manipulation

With the manipulation and querying of point clouds, leaving all data in that cloud format, would eliminate the need for a whole host of data manipulation issues. There’s no disputing that a great deal of error is introduced with different datums and projections of geospatial data. To the disciplines that aren’t versed in the nuances and distinctions of difference, these rules feel like controls that assert that only the educated are able to create and qualify geospatial data.

Stepping back from the geospatial perspective, it’s quite understandable that those not deemed geospatial experts would bridle with the controls that were largely introduced centuries ago. Point clouds are adequate for local and regional representations of truth without the geodetic steps necessary for mapping. Is it reasonable to think that we might move toward a global measurement that might eventually eliminate these complicated mathematical steps?

As convergence continues with the modeling and visualization tools that represent both our built environment and the environment, there will be a continued need to represent reality precisely to understand the nuances of our complex world. Sensors that produce point clouds will increase in demand as a growing number of disciplines embrace the advantages that they provide in quickly capturing and presenting reality. A future of point cloud only modeling environments is likely as the means to view and combine large data sets becomes easier. The thing for those versed in CAD, GIS and BIM to keep in mind is what they add and extract from this data, and how they might improve workflows even when that means stepping aside.

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