Prior work to infer 3D texture use either texture atlases, which require
uv-mappings and hence have discontinuities, or colored voxels, which are memory
inefficient and limited in resolution. Recent work, predicts RGB color at every
XYZ coordinate forming a texture field, but focus on completing texture given a
single 2D image. Instead, we focus on 3D texture and geometry completion from
partial and incomplete 3D scans. IF-Nets have recently achieved
state-of-the-art results on 3D geometry completion using a multi-scale deep
feature encoding, but the outputs lack texture. In this work, we generalize
IF-Nets to texture completion from partial textured scans of humans and
arbitrary objects. Our key insight is that 3D texture completion benefits from
incorporating local and global deep features extracted from both the 3D partial
texture and completed geometry. Specifically, given the partial 3D texture and
the 3D geometry completed with IF-Nets, our model successfully in-paints the
missing texture parts in consistence with the completed geometry. Our model won
the SHARP ECCV'20 challenge, achieving highest performance on all challenges.

本研究旨在实现通过 IF-Nets 模型进行基于局部和全局深度特征提取，从部分三维纹理和已完成几何体上完成 3D 纹理的完整。我们的模型成功地用靠近已完成几何体的内插法来处理丢失的纹理部分，并在 SHARP ECCV20 挑战中取得了最高表现。

使用隐式特征网络从部分 3D 数据进行纹理补全

Implicit Feature Networks for Texture Completion from Partial 3D Data

While many works focus on 3D reconstruction from images, in this paper, we
focus on 3D shape reconstruction and completion from a variety of 3D inputs,
which are deficient in some respect: low and high resolution voxels, sparse and
dense point clouds, complete or incomplete. Processing of such 3D inputs is an
increasingly important problem as they are the output of 3D scanners, which are
becoming more accessible, and are the intermediate output of 3D computer vision
algorithms. Recently, learned implicit functions have shown great promise as
they produce continuous reconstructions. However, we identified two limitations
in reconstruction from 3D inputs: 1) details present in the input data are not
retained, and 2) poor reconstruction of articulated humans. To solve this, we
propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs,
can handle multiple topologies, and complete shapes for missing or sparse input
data retaining the nice properties of recent learned implicit functions, but
critically they can also retain detail when it is present in the input data,
and can reconstruct articulated humans. Our work differs from prior work in two
crucial aspects. First, instead of using a single vector to encode a 3D shape,
we extract a learnable 3-dimensional multi-scale tensor of deep features, which
is aligned with the original Euclidean space embedding the shape. Second,
instead of classifying x-y-z point coordinates directly, we classify deep
features extracted from the tensor at a continuous query point. We show that
this forces our model to make decisions based on global and local shape
structure, as opposed to point coordinates, which are arbitrary under Euclidean
transformations. Experiments demonstrate that IF-Nets clearly outperform prior
work in 3D object reconstruction in ShapeNet, and obtain significantly more
accurate 3D human reconstructions.

本文提出了一种名为 IF-Nets 的新模型来解决从不同类型的三维输入中恢复和补全三维物体的问题，IF-Nets 可提取深度特征，保留输入数据中存在的细节，并可以重建活动的人体模型。实验证明 IF-Nets 在 ShapeNet 中的三维物体重建和人体重建方面明显优于以往的工作。