NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-View Images

Figure 1. We show three groups of surface reconstruction from multi-view images. The front and back faces are rendered in blue and yellow respectively. Our method (left) is able to reconstruct high-fidelity and intricate surfaces of arbitrary topologies, including those non-watertight structures, e.g. the thin single-layer shoulder strap of the top (middle). In comparison, the state-of-the-art NeuS [48] method (right) can only generate watertight surfaces, resulting in thick, double-layer geometries.