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AI and Point Cloud Classification

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It is true that high-definition laser scanning has been with us for over two decades. However, today’s tools and solutions for scanning bear little resemblance to what many of us began using in those early days. What is now more rightly referred to as reality capture (RC) produces far more data, by orders of magnitude—massive amounts of data in far less time. Hardware, be it terrestrial laser scanners, mobile, or aerial systems, capture rich point clouds at rates of millions of points per second. AI and point cloud classification is the subject of this important article by Gavin Schrock as he interviews Dr. Bernhard Metzler.

For our continuing examination of the present state (and art) of reality capture, we began with a deep dive into how “simplicity” has become a driving force in the design of these systems and solutions. Simplicity does not mean simple, or “dumbed down”, but instead has delivered smoother and more efficient workflows without compromising quality. Our series continues with this look at the essential first step of downstream processing: point cloud segmentation, or classification (PCC).

Context from Chaos

Field software, workflow tools, and downstream processing have undergone a sea change in development to be able to keep up with this deluge of data. Higher levels of automation were an imperative; relying on traditional tools would only produce bottlenecks. Advances in automation for reality capture include in-field point cloud registration, a reduced need for targets, and has revolutionized the essential step of PCC.

“Above all, the technical achievements in lidar technology enabled the data acquisition of objects with higher resolution and shorter measurement times,” said Dr. Bernhard Metzler, Head of Imaging & Point Cloud, Hexagon Technology Centre. Hexagon is the parent company of Leica Geosystems, a leader in reality capture systems and software. “This technology combined with a lean measurement workflow has led to a significant increase of the efficiency of data capturing, which results in the generation of large amount in data represented as point clouds.”

As an example, a single full dome scan of a Leica RTC360 with a measurement rate of 2 million points per second and a scanning time of one and a half minutes results in a point cloud of about 200 million points. Given this, even small projects with just a few scanning setups can quickly end up in data volumes of several billions of points. And consider mobile mapping. Hence, the challenge is the further processing of these large amounts of data: billions of points that need to be cleaned up and classified to enable meaningful analysis and modelling.

For the complete article on AI and point cloud classification CLICK HERE.

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