| Abstrakt: |
This paper presents an approach to 3D model reconstruction from point cloud data using modern neural network architectures. The method is based on PointNet++ and Transformer. PointNet++ plays a key role, providing efficient feature extraction and encoding of complex geometries in 3D scenes. This is achieved by recursively applying PointNet++ to nested partitions of the input point set in a metric space. Convex decomposition, which is an important step in the proposed approach, transforms complex 3D objects into sets of simpler convex shapes. This facilitates data processing and makes the reconstruction process more manageable. Then, Transformer trains the model on the extracted features, which enables the generation of high-quality reconstructions. It should be noted that Transformer is used only to determine positions of the walls and detect object boundaries. This combination of technologies allows us to achieve high accuracy of 3D model reconstruction. The main idea of the method is to segment the point cloud into small fragments, which are then reconstructed as polygon meshes. To restore missing points in point cloud data, a method based on the L1-median algorithm and local features of the point cloud is used. This approach can adapt to different geometric structures and correct topological connectivity errors. The proposed method is compared with several modern approaches, demonstrating its potential in various fields, including architecture, engineering, digitalization of cultural heritage, as well as augmented and mixed reality systems. This confirms its wide applicability and great perspectives for further development. [ABSTRACT FROM AUTHOR] |
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