Morphable models are fundamental to numerous human-centered processes as they
offer a simple yet expressive shape space. Creating such morphable models,
however, is both tedious and expensive. The main challenge is establishing
dense correspondences across raw scans that capture sufficient shape variation.
This is often addressed using a mix of significant manual intervention and
non-rigid registration. We observe that creating a shape space and solving for
dense correspondence are tightly coupled -- while dense correspondence is
needed to build shape spaces, an expressive shape space provides a reduced
dimensional space to regularize the search. We introduce BLiSS, a method to
solve both progressively. Starting from a small set of manually registered
scans to bootstrap the process, we enrich the shape space and then use that to
get new unregistered scans into correspondence automatically. The critical
component of BLiSS is a non-linear deformation model that captures details
missed by the low-dimensional shape space, thus allowing progressive enrichment
of the space.

BLiSS 是一种解决建立形状空间和密集对应的方法，通过使用非线性变形模型捕捉细节，实现对形状空间的逐步丰富。

BLiSS: 自举线性形状空间

BLiSS: Bootstrapped Linear Shape Space

Point completion refers to completing the missing geometries of an object
from incomplete observations. Main-stream methods predict the missing shapes by
decoding a global feature learned from the input point cloud, which often leads
to deficient results in preserving topology consistency and surface details. In
this work, we present ME-PCN, a point completion network that leverages
`emptiness' in 3D shape space. Given a single depth scan, previous methods
often encode the occupied partial shapes while ignoring the empty regions (e.g.
holes) in depth maps. In contrast, we argue that these `emptiness' clues
indicate shape boundaries that can be used to improve topology representation
and detail granularity on surfaces. Specifically, our ME-PCN encodes both the
occupied point cloud and the neighboring `empty points'. It estimates
coarse-grained but complete and reasonable surface points in the first stage,
followed by a refinement stage to produce fine-grained surface details.
Comprehensive experiments verify that our ME-PCN presents better qualitative
and quantitative performance against the state-of-the-art. Besides, we further
prove that our `emptiness' design is lightweight and easy to embed in existing
methods, which shows consistent effectiveness in improving the CD and EMD
scores.

本文介绍了一种基于三维形状空间 “空洞” 特征的点完整性网络 ME-PCN，该网络能够在保留拓扑一致性和表面细节的前提下预测出完整的表面点云，实验表明 ME-PCN 优于现有技术并且采用了一种轻量级的 “空洞” 设计。

ME-PCN：基于掩码空洞的点云补全

ME-PCN: Point Completion Conditioned on Mask Emptiness

There has been significant work on learning realistic, articulated, 3D models
of the human body. In contrast, there are few such models of animals, despite
many applications. The main challenge is that animals are much less cooperative
than humans. The best human body models are learned from thousands of 3D scans
of people in specific poses, which is infeasible with live animals.
Consequently, we learn our model from a small set of 3D scans of toy figurines
in arbitrary poses. We employ a novel part-based shape model to compute an
initial registration to the scans. We then normalize their pose, learn a
statistical shape model, and refine the registrations and the model together.
In this way, we accurately align animal scans from different quadruped families
with very different shapes and poses. With the registration to a common
template we learn a shape space representing animals including lions, cats,
dogs, horses, cows and hippos. Animal shapes can be sampled from the model,
posed, animated, and fit to data. We demonstrate generalization by fitting it
to images of real animals including species not seen in training.

该研究通过使用一个新颖的基于部件的形状模型，从玩具模型的 3D 扫描中学习了一个能表示狮子、猫、狗、马、奶牛和河马等动物形状的统计形状模型，并通过将人工智能技术运用于真实动物的图像中，证明了它的普适性。