面 向 开 放 表 面 的 神 经 移 动 立 方 体 算 法

Marching cubes (MC) is a classic algorithm for isosurface extraction. However, it can only be used to reconstruct closed surfaces, as it requires dividing the space into inside and outside. To solve this problem, a neural marching cubes algorithm for open surfaces is proposed. The key to our method is to introduce a new sort of point position called irrelative point. Since no isosurface exists between irrelevant points and inside or outside points, thus connecting the inside and outside of the target shapes. The determination of irrelevant points does not require complex networks or calculations, and can be directly determined by the distance from points to the surface. Meanwhile, a residual module introducing attention mechanism is adopted to replace the original network. In addition, new tessellations are designed, and open surfaces can be reconstructed with the help of irrelative points. Finally, a smoothing process is incorporated to improve the reconstruction quality of the border. By testing on both closed and open surfaces under various metrics, our experiments show that the proposed method achieves high-quality reconstruction of open surfaces while maintaining the capability of reconstructing closed surfaces.