Implicit surface representations, such as signed-distance functions, combined
with deep learning have led to impressive models which can represent detailed
shapes of objects with arbitrary topology. Since a continuous function is
learned, the reconstructions can also be extracted at any arbitrary resolution.
However, large datasets such as ShapeNet are required to train such models. In
this paper, we present a new mid-level patch-based surface representation. At
the level of patches, objects across different categories share similarities,
which leads to more generalizable models. We then introduce a novel method to
learn this patch-based representation in a canonical space, such that it is as
object-agnostic as possible. We show that our representation trained on one
category of objects from ShapeNet can also well represent detailed shapes from
any other category. In addition, it can be trained using much fewer shapes,
compared to existing approaches. We show several applications of our new
representation, including shape interpolation and partial point cloud
completion. Due to explicit control over positions, orientations and scales of
patches, our representation is also more controllable compared to object-level
representations, which enables us to deform encoded shapes non-rigidly.

本文提出了一种基于 patch 的中层面向对象表示方法，将不同类别对象的相似性建模在 patch 级别，使模型更具有通用性，并且提出了一种新的在规范空间中学习 patch 表示的方法，该表示方法对物体的类别不敏感，在训练时只需要较少的数据集，在形状插值和局部点云完成等应用上也有良好的效果。

PatchNets：基于局部块的通用深度隐式 3D 形状表征

PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape  Representations

Due to the fact that it is prohibitively expensive to completely annotate
visual relationships, i.e., the (obj1, rel, obj2) triplets, relationship models
are inevitably biased to object classes of limited pairwise patterns, leading
to poor generalization to rare or unseen object combinations. Therefore, we are
interested in learning object-agnostic visual features for more generalizable
relationship models. By "agnostic", we mean that the feature is less likely
biased to the classes of paired objects. To alleviate the bias, we propose a
novel \texttt{Shuffle-Then-Assemble} pre-training strategy. First, we discard
all the triplet relationship annotations in an image, leaving two unpaired
object domains without obj1-obj2 alignment. Then, our feature learning is to
recover possible obj1-obj2 pairs. In particular, we design a cycle of residual
transformations between the two domains, to capture shared but not
object-specific visual patterns. Extensive experiments on two visual
relationship benchmarks show that by using our pre-trained features, naive
relationship models can be consistently improved and even outperform other
state-of-the-art relationship models. Code has been made available at:
https://github.com/yangxuntu/vrd.

本文提出了一种新的预训练策略 “洗牌 — 组装”，旨在学习对象不可知的视觉特征用于关系建模，其中通过残差变换来捕获共享但非特定于对象的视觉模式，实验结果表明，使用我们预训练的特征，关系模型不仅可以持续得到改进，甚至能够胜过其他最先进的关系模型。