* Title : Deep Learning based Surface Reconstruction from Point Clouds and Integer Transfer Scheme.

* Abstract : 

In this presentation, we introduce an advanced deep learning approach for reconstructing surfaces from unorganized point clouds and Integer Transfer Scheme(ITS). By leveraging an implicit surface representation through a level set function, our method ensures watertight results and seamlessly adapts to various topologies. We employ the p-Poisson equation to precisely learn the signed distance function (SDF), improving accuracy through a variable splitting strategy that incorporates the SDF gradient as an auxiliary variable. Additionally, we enforce a curl-free condition on the auxiliary variable to exploit the irrotational nature of conservative vector fields. Our numerical results illustrate that this strategic integration of partial differential equations and key vector field characteristics efficiently reconstructs high-quality surfaces without the need for prior surface knowledge.  Also, We present the ITS to solve scalar conservation laws as antisymmetric integer transfers between cells. ITS guarantees exact mass conservation in integer arithmetic, while maintaining monotonicity and the TVD property.