Abstract
Abstract. Indoor point cloud data is easy to obtain and contains a large amount of data, but there is no connectivity, no structural attributes, and no semantic information between point clouds. It is very difficult to extract interior components. In order to solve the problem that it is difficult to extract the closed door in the indoor point cloud, and the indoor scanning point cloud data is noisy and redundant, a point cloud segmentation method is proposed to reduce the amount of data and the impact of noise. Because most of the indoor components are not on the same horizontal plane, different component information can be obtained by slicing the point cloud at different heights. In these slices, the component that always exists is the wall. By comparing other data with the point cloud data with a more complete wall, the required component can be extracted. The experimental results show that this method can accurately extract the indoor door component information and beam component information, providing a data basis for building model refinement.
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