  This paper presents a method for reconstructing watertight 3D surfaces from
unoriented point clouds. Starting with randomly initialized normals, the method
iteratively refines each normal by diffusing the gradient of the generalized
winding number (GWN) field. Upon convergence, the target surface is extracted
using the standard Marching Cubes algorithm. Our method is conceptually simple,
easy to implement, and does not require numerical solvers, which distinguishes
it from existing approaches. Designed for parallelization and scalability, it
efficiently handles large-scale models on both CPUs and GPUs. Experimental
results demonstrate that our method outperforms all existing methods in
reconstructing from unoriented point clouds, particularly in terms of runtime
performance. On large-scale models with 10 to 20 million points, our CUDA
implementation on an NVIDIA GTX 4090 GPU is typically 30-100x faster than iPSR,
the leading sequential method tested on a high-end PC with an Intel i9 CPU.
Furthermore, our approach exhibits superior robustness against noise and
effectively handles models with thin structures, surpassing existing methods.
We will make the source code publicly available to encourage further research
and applications.
