We propose a novel Deformed Implicit Field (DIF) representation for modeling
3D shapes of a category and generating dense correspondences among shapes. With
DIF, a 3D shape is represented by a template implicit field shared across the
category, together with a 3D deformation field and a correction field dedicated
for each shape instance. Shape correspondences can be easily established using
their deformation fields. Our neural network, dubbed DIF-Net, jointly learns a
shape latent space and these fields for 3D objects belonging to a category
without using any correspondence or part label. The learned DIF-Net can also
provides reliable correspondence uncertainty measurement reflecting shape
structure discrepancy. Experiments show that DIF-Net not only produces
high-fidelity 3D shapes but also builds high-quality dense correspondences
across different shapes. We also demonstrate several applications such as
texture transfer and shape editing, where our method achieves compelling
results that cannot be achieved by previous methods.

本论文提出了 Deformed Implicit Field (DIF) 表示法来建模 3D 物品的形状，实现物品之间稠密的对应关系。DIF-Net 神经网络通过学习一个特定类别的物品的形态潜在空间和这些物品的 Deformation Field 来实现对应关系的建立以及对物品形状差异的度量，还考虑了材质和形状编辑等应用。

Deformed Implicit Field：学习稠密对应关系建模三维形状

Deformed Implicit Field: Modeling 3D Shapes with Learned Dense  Correspondence

The availability of affordable 3D full body reconstruction systems has given
rise to free-viewpoint video (FVV) of human shapes. Most existing solutions
produce temporally uncorrelated point clouds or meshes with unknown
point/vertex correspondences. Individually compressing each frame is
ineffective and still yields to ultra-large data sizes. We present an
end-to-end deep learning scheme to establish dense shape correspondences and
subsequently compress the data. Our approach uses sparse set of "panoramic"
depth maps or PDMs, each emulating an inward-viewing concentric mosaics. We
then develop a learning-based technique to learn pixel-wise feature descriptors
on PDMs. The results are fed into an autoencoder-based network for compression.
Comprehensive experiments demonstrate our solution is robust and effective on
both public and our newly captured datasets.

采用端到端的深度学习方法建立密集的形状对应关系，并随后压缩 3D 全身重建数据。

利用全景深度图对人体进行 4D 对应

4D Human Body Correspondences from Panoramic Depth Maps

We present a robust method to find region-level correspondences between
shapes, which are invariant to changes in geometry and applicable across
multiple shape representations. We generate simplified shape graphs by jointly
decomposing the shapes, and devise an adapted graph-matching technique, from
which we infer correspondences between shape regions. The simplified shape
graphs are designed to primarily capture the overall structure of the shapes,
without reflecting precise information about the geometry of each region, which
enables us to find correspondences between shapes that might have significant
geometric differences. Moreover, due to the special care we take to ensure the
robustness of each part of our pipeline, our method can find correspondences
between shapes with different representations, such as triangular meshes and
point clouds. We demonstrate that the region-wise matching that we obtain can
be used to find correspondences between feature points, reveal the intrinsic
self-similarities of each shape, and even construct point-to-point maps across
shapes. Our method is both time and space efficient, leading to a pipeline that
is significantly faster than comparable approaches. We demonstrate the
performance of our approach through an extensive quantitative and qualitative
evaluation on several benchmarks where we achieve comparable or superior
performance to existing methods.

提出一种鲁棒的方法，使用简化形状图形和图形匹配技术来找到不同表示方法下的形状区域对应关系，并且可以用于构建点对点的形状之间映射。