The application belongs to the technical field of composite
machining, and discloses a composite workpiece element automatic extraction method based on three-dimensional
point cloud semantic segmentation, which comprises the following steps: obtaining a two-dimensional image and a three-dimensional
point cloud of a composite workpiece element; respectively performing data preprocessing on the two-dimensional image and the three-dimensional
point cloud of the composite workpiece element to obtain a co-configuration image set composed of a three-dimensional
point set and a two-dimensional image; extracting the composite workpiece element in the two-dimensional
image based on a
convolutional neural network and obtaining a two-dimensional
feature mapping set; establishing the corresponding relationship between the two-dimensional image and the three-dimensional point cloud of the composite workpiece element by using a multi-view aggregation model based on
deep learning; and performing semantic segmentation on the three-dimensional point cloud to obtain corresponding point cloud sets of different composite workpiece elements. The application can realize the automatic denoising segmentation and extraction of a point cloud file scanned by a
laser after the point cloud file is imported into an
algorithm, and can segment the workpiece element according to the characteristics of the
composite material, mark the belonging element into the belonging information of each point, and facilitate the extraction and calling.