  In recent years, neural signed distance function (SDF) has become one of the
most effective representation methods for 3D models. By learning continuous
SDFs in 3D space, neural networks can predict the distance from a given query
space point to its closest object surface,whose positive and negative signs
denote inside and outside of the object, respectively. Training a specific
network for each 3D model, which individually embeds its shape, can realize
compressed representation of objects by storing fewer network (and possibly
latent) parameters. Consequently, reconstruction through network inference and
surface recovery can be achieved. In this paper, we propose an SDF prediction
network using explicit key spheres as input. Key spheres are extracted from the
internal space of objects, whose centers either have relatively larger SDF
values (sphere radii), or are located at essential positions. By inputting the
spatial information of multiple spheres which imply different local shapes, the
proposed method can significantly improve the reconstruction accuracy with a
negligible storage cost. Compared to previous works, our method achieves the
high-fidelity and high-compression 3D object coding and reconstruction.
Experiments conducted on three datasets verify the superior performance of our
method.
