Abstract:

[Objective] In the field of computer vision and graphics, the task of original point cloud surface reconstruction is still challenging. Most current studies learn various features in implicit space and fuse them directly. But this method is difficult to accurately interpret 3D models, and there are incomplete surfaces caused by the disappearance of features in the reconstructed models. [Methods] To solve this problem, this paper introduces a new implicit representation method, which divides features into global features and local features. First, the local features after segmentation are learned, and the local point cloud learns local features in the potential implicit space to quickly and accurately obtain the point cloud features. Then, the deep features of each part are implicitly fused, the learned global features are fused and the surface model is reconstructed. [Results] The method converts a 3D global shape into multiple local shapes for modeling. The local shapes are divided into global unity through deep feature extraction, and the implicit surfaces of 3D shapes can be extracted more effectively, so as to reconstruct 3D surfaces. We will name it Implicit Partition Deep Feature Fusion (IPDFF). [Limitations] Although the IPDFF model is suitable for complex models, it is not effective for the reconstruction of missing point clouds in complex regions or point clouds with complex features. [Conclusions] In the experimental results, IPDFF is superior to other baseline methods in visual reconstruction effect and several quantitative indexes, and the reconstructed surface has strong robustness and practicability, and the model has stronger detail features.

Key words: three-dimensional image processing, surface reconstruction, deep learning, point cloud feature fusion, point cloud feature extraction