eDiGS: Extended Divergence-Guided Shape Implicit Neural Representation for Unoriented Point Clouds

In this chapter, we propose a new approach for learning shape implicit neural representations (INRs) from point cloud data that does not require normal vectors as input. We show that our method, which uses a soft constraint on the divergence of the distance function to the shape’s surface, can produce smooth solutions that accurately orient gradients to match the unknown normal at each point, even outperforming methods that use normal vectors directly. This work extends the latest work on divergence-guided sinusoidal activation INRs¹ to Gaussian activation INRs and provides extended theoretical analysis and results. We evaluate our approach on tasks related to surface reconstruction and shape space learning.