LiDAR: point cloud classification, a crucial step!

January 18, 2023 Déborah

There is no denying that LiDAR technology is nowadays more than present, it is ubiquitous. Whether through the professional sector or the personal sector, LiDAR never ceases to surprise us with multiple and diverse uses – industrial measures, environmental assessment, cartography, architecture, archaeology, vehicle automation… Even your iPhone has it! Its operation and efficiency then go through the development of 3D points clouds and their classification. This one is usually essential but sometimes a real challenge… In order to better understand this process, let's look closer to 3D points cloud classification.

What is point cloud classification?

First of all, what is classification? The classification makes it possible to give a class (a category) to each point in the cloud. Then it is possible to recognize the objects of the point cloud. Generally, two classification results are given: semantic classification (it gives a “meaning” to each point, the point is given a label corresponding to the recognized object) and panoptic classification (each object is segmented and exported individually).

But, what is the use of this classification?

Then, we may wonder what point cloud classification is made for. The classification possesses several uses. In particular, it makes it possible to better visualize the point cloud, to explain it or even to simplify it. Indeed, a raw cloud is much less clear than a classified cloud. Thus, by removing the parasitic points (useless objects, such as cars for instance), it becomes then possible to lighten the cloud and to process it with greater ease and precision. In one word, point cloud classification helps to extract relevant information from the cloud and realize business applications. It is therefore crucial in order to guarantee precision and clarity!

Highway Automated Classification, TheCrossProduct

And how to classify point clouds? 

Finally, we can then wonder about the existing classification methods as its elaboration is so essential. It is necessary to underline that the classification of point clouds can take different forms. Indeed, the classification can be done manually but also automatically… But then, what are they? Manual classification is a process that can be laborious. Indeed, it is slow, expensive and imprecise, as the proverb says: “To err is human!”. One can then be assisted by tools making it possible to detect plane zones for the ground or the walls but it then implies a training of the staff and its elaboration is always slow and laborious. Then comes the automated classification method by an AI (Artificial Intelligence) that the user must train and configure. However, once again, this process can be difficult insofar as the user must annotate clouds and have knowledge of machine learning to properly train this AI… Finally, there is the automated classification method specific to each business need. With this method, the user just has to send the data to the cloud, no need to configure or train anything. This is a hybrid solution mixing AI, deterministic algorithms and specific business knowledge to provide a turnkey solution! There is some software such as the product Railway Automated Classification or Highway Automated Classification of the deeptech TheCrossProduct which allows a fast, automatic and reliable classification of these point clouds. Indeed, thanks to its industrial and academic experience in the field of 3D point cloud processing research, TheCrossProduct offers state-of-the-art software.


Railway Automated Classification, TheCrossProduct

In a nutshell, point cloud classification is a key step. This process can then be carried out using automatic tools, relevant and adapted to a business need, such as Railway Automated Classification of TheCrossProduct. These tools guarantee fast and reliable processing for the benefit of innovation, development and safety!


Still more questions about point clouds and LiDAR? How to visualize point clouds ? How to modelize point clouds? Find some answers here:!

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