The application discloses a feature-driven
point cloud completion method and
system for a CAD parameterized model, relates to the technical field of three-dimensional
geometry processing and
computer vision, and comprises the following steps: constructing a multi-source feature line collection and
standardization system, obtaining feature line candidates through multiple paths such as analysis of CAD information,
mesh point cloud geometric clues and
interactive editing rules, and outputting a unified format feature representation after consistency evaluation, cleaning and repairing; designing a local
feature extraction method based on adaptive weights of multiple relationships, dynamically adjusting weights according to the relationship between target points and neighbor points in three-dimensional space and high-dimensional feature space in a DGCNN framework, and quantifying the contribution of each neighbor point to the local feature of the target point; and proposing a feature line deep information injection technology, fusing feature line geometric information into a
point cloud completion
deep learning model through an
encoder, using a feature line weighted
chamfer distance loss function to strengthen the fidelity of boundary and
acute angle regions, and effectively improving the accuracy and geometric detail retention capability of
point cloud completion.