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3D Point Cloud Shape Detection for Indoor Modelling
A 10-step Python Guide to Automate 3D Shape Detection, Segmentation, Clustering, and Voxelization for Space Occupancy 3D Modeling of Indoor Point Cloud Datasets.
If you have experience with point clouds or data analysis, you know how crucial it is to spot patterns. Recognizing data points with similar patterns, or "objects," is important to gain more valuable insights. Our visual cognitive system accomplishes this task easily, but replicating this human ability through computational methods is a significant challenge.
The goal is to utilize the natural tendency of the human visual system to group sets of elements. 👀
But why is it useful?
First, it lets you easily access and work with specific parts of the data by grouping them into segments. Secondly, it makes the data processing faster by looking at regions instead of individual points. This can save a lot of time and energy. And finally, segmentation can help you find patterns and relationships you wouldn’t be able to see just by looking at the raw data. 🔍 Overall, segmentation is crucial for getting useful information from point…