  Recent neural networks based surface reconstruction can be roughly divided
into two categories, one warping templates explicitly and the other
representing 3D surfaces implicitly. To enjoy the advantages of both, we
propose a novel 3D representation, Neural Vector Fields (NVF), which adopts the
explicit learning process to manipulate meshes and implicit unsigned distance
function (UDF) representation to break the barriers in resolution and topology.
This is achieved by directly predicting the displacements from surface queries
and modeling shapes as Vector Fields, rather than relying on network
differentiation to obtain direction fields as most existing UDF-based methods
do. In this way, our approach is capable of encoding both the distance and the
direction fields so that the calculation of direction fields is
differentiation-free, circumventing the non-trivial surface extraction step.
Furthermore, building upon NVFs, we propose to incorporate two types of shape
codebooks, \ie, NVFs (Lite or Ultra), to promote cross-category reconstruction
through encoding cross-object priors. Moreover, we propose a new regularization
based on analyzing the zero-curl property of NVFs, and implement this through
the fully differentiable framework of our NVF (ultra). We evaluate both NVFs on
four surface reconstruction scenarios, including watertight vs non-watertight
shapes, category-agnostic reconstruction vs category-unseen reconstruction,
category-specific, and cross-domain reconstruction.
