Abstract: Aiming at the distortion of building façade TIN grid model which caused by the dense image matching (DIM) point cloud with noise, this paper references the idea of sample learning and proposes the corresponding DIM point cloud filtering method to remove the noise point and improve the model distortion after classifying the facades. Among them, for the facade with planar structure, this paper first learns and calculates the parameters of mathematical facade model from the selected point cloud sample, and then sets the threshold for the model to filter; for the façade with curved structure, after analyzing the characteristic of the facade DIM point cloud, this paper first marks the selected point cloud sample as two types of façade point and non-facade point, then learns the feature of the marked sample and calculates the optimal regression coefficient needed for the filtering classifier by the logistic regression algorithm to remove the noise point cloud. Finally, the experimental result showed that the filtering method can effectively identify and segment the noise point cloud from the whole facade point cloud, and the facade model which built by the filtered point cloud was be refined and optimized.

Key words: building facade, DIM point cloud filtering, mathematical facade model construction, sample learning, Logistic regression algorithm