Polyhedral Complex Derivation from Piecewise Trilinear Networks

Recent advancements in visualizing deep neural networks provide insights into their structures and mesh extraction from Continuous Piecewise Affine (CPWA) functions. Meanwhile, developments in neural surface representation learning incorporate non-linear positional encoding, addressing issues like spectral bias; however, this poses challenges in applying mesh extraction techniques based on CPWA functions. Moreover, we introduce a method for approximating intersecting points among three hypersurfaces contributing to broader applications.