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Abstract

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