Implicit neural representations of 3D shapes form strong priors that areuseful for various applications, such as single and multiple view 3Dreconstruction. A downside of existing neural representations is that theyrequire multiple network evaluations for rendering, which leads to highcomputational costs. This limitation forms a bottleneck particularly in thecontext of inverse problems, such as image-based 3D reconstruction. To addressthis issue, in this paper (i) we propose a novel hybrid 3D objectrepresentation based on a signed distance function (SDF) that we augment with adirectional distance function (DDF), so that we can predict distances to theobject surface from any point on a sphere enclosing the object. Moreover, (ii)using the proposed hybrid representation we address the multi-view consistencyproblem common in existing DDF representations. We evaluate our novel hybridrepresentation on the task of single-view depth reconstruction and show thatour method is several times faster compared to competing methods, while at thesame time achieving better reconstruction accuracy.<br
