Point cloud contour extraction method based on improved pass-through dimensionality reduction

Obtaining a point cloud model that considers the overall information of large steel components as well as the local information of bolt holes is an important approach for the virtual assembly of large steel structures. This paper proposes a point cloud extraction method to improve the uniformity of the dimensionality reduction distribution. Firstly, the point cloud data characterizing the overall information of the large-size component and the local information of the bolt-hole group are obtained by combining vertical 3D laser scanning and handheld 3D laser scanning; the point cloud was then triaxially equidistant and reduced in dimension based on improved straight-pass filtering, and the corner point cloud extracted using a planar point cloud distribution uniformity algorithm; finally, the point cloud is restored to the same space to complete the contour extraction of the point cloud. The accuracy of the contour extraction method was verified by conducting point cloud feature extraction tests using standard components. Compared to conventional feature extraction, the method provides targeted local feature extraction for bars with a certain regularity of geometric configuration, reducing the time required for feature extraction and providing a brief database for the virtual assembly of steel joist beams.