  Reconstructing a continuous surface from an unoritented 3D point cloud is a
fundamental task in 3D shape processing. In recent years, several methods have
been proposed to address this problem using implicit neural representations
(INRs). In this study, we propose a method to optimize INRs using energy-based
models (EBMs). By employing the absolute value of the coordinate-based neural
networks as the energy function, the INR can be optimized through the
estimation of the point cloud distribution by the EBM. In addition, appropriate
parameter settings of the EBM enable the model to consider the magnitude of
point cloud noise. Our experiments confirmed that the proposed method is more
robust against point cloud noise than conventional surface reconstruction
methods.
