Point cloud reconstruction and deformation based on point primitives

With the rapid development of 3D point cloud data acquisition technology, its application in fields such as virtual reality, augmented reality, medical imaging analysis, and robotics is becoming increasingly widespread. Although current research mainly focuses on implicit space point cloud processing, an increasing amount of evidence suggests that explicit space methods, especially those based on point data primitives, can more effectively achieve neural surface reconstruction and point cloud deformation. This study completes reconstruction by imposing constraints in point space, normal space, and image space, and use isometric constraints to construct a point cloud deformation field for precise deformation of point cloud models. Experiments on the Studio dataset and the Stanford 3D Scanning Repository demonstrated good neural surface reconstruction effects. Compared to the Instant NGP method, the reconstruction loss was reduced by 13.9% and 27.7%, respectively, and effectively avoided reconstruction loopholes. Ablation experiments further confirmed the role of AIAP constraints in improving the quality of point cloud deformation, with optimization effects reaching 7.4%, respectively. The point cloud reconstruction and deformation method based on point primitives proposed in this paper, from explicit semantic space constraints and guided learning processes, not only completes the reconstruction and deformation of point clouds with high fidelity but also shows strong robustness and generalization.