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For automatic processing of point clouds their segmentation is one of the most important processes. The methods based on curvature and other higher level derivatives often lead to over segmentation, which later needs a lot of manual editing. We present a method for segmentation of point clouds using smoothness constraint, which finds smoothly connected areas in point clouds. It uses only local surface normals and point connectivity which can be enforced using either k-nearest or fixed distance neighbours. The presented method requires a small number of intuitive parameters, which provide a tradeoff between under-and over-segmentation. The application of the presented algorithm on industrial point clouds shows its effectiveness compared to curvature based approaches.

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