Improving the neural meshes algorithm for 3D surface reconstruction with edge swap operations

The reconstruction of a three-dimensional surface from a set of unorganized points is a fundamental process in a lot of applications, including laser range scanners, medical imaging, and others. This work presents a modified version of the Neural Meshes algorithm for surface reconstruction from point cloud, called Neural Meshes with Edge Swap, or Neural Meshes ES. From the original basic Neural Meshes algorithm we developed a new heuristic that include an edge-swap operation, making the algorithm less sensible to parameters variation, improving the quality of the generated surfaces. The results show that the edge-swap operation is able to avoid a series of incorrect reconstructed areas in the mesh, mainly in the concave structures.