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Developability refers to the process of creating a surface without anytearing or shearing from a two-dimensional plane. It finds practicalapplications in the fabrication industry. An essential characteristic of adevelopable 3D surface is its zero Gaussian curvature, which means that eitherone or both of the principal curvatures are zero. This paper introduces amethod for reconstructing an approximate developable surface from a neuralimplicit surface. The central idea of our method involves incorporating aregularization term that operates on the second-order derivatives of the neuralimplicits, effectively promoting zero Gaussian curvature. Implicit surfacesoffer the advantage of smoother deformation with infinite resolution,overcoming the high polygonal constraints of state-of-the-art methods usingdiscrete representations. We draw inspiration from the properties of surfacecurvature and employ rank minimization techniques derived from compressedsensing. Experimental results on both developable and non-developable surfaces,including those affected by noise, validate the generalizability of our method.

Developability Approximation for Neural Implicits through Rank Minimization