Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

Neural implicit functions have emerged as a powerful representation for surfaces in 3D. Such a function can en-code a high quality surface with intricate details into the parameters of a deep neural network. However, optimizing for the parameters for accurate and robust reconstructions remains a challenge, especially when the input data is noisy or incomplete. In this work, we develop a hybrid neu...