Traditional shape descriptors have been gradually replaced by convolutional
neural networks due to their superior performance in feature extraction and
classification. The state-of-the-art methods recognize object shapes via image
reconstruction or pixel classification. However , these methods are biased
toward texture information and overlook the essential shape descriptions, thus,
they fail to generalize to unseen shapes. We are the first to propose a fewshot
shape descriptor (FSSD) to recognize object shapes given only one or a few
samples. We employ an embedding module for FSSD to extract
transformation-invariant shape features. Secondly, we develop a dual attention
mechanism to decompose and reconstruct the shape features via learnable shape
primitives. In this way, any shape can be formed through a finite set basis,
and the learned representation model is highly interpretable and extendable to
unseen shapes. Thirdly, we propose a decoding module to include the supervision
of shape masks and edges and align the original and reconstructed shape
features, enforcing the learned features to be more shape-aware. Lastly, all
the proposed modules are assembled into a few-shot shape recognition scheme.
Experiments on five datasets show that our FSSD significantly improves the
shape classification compared to the state-of-the-art under the few-shot
setting.

我们提出了一种少样本形状描述符（FSSD），通过嵌入模块提取具有转换不变性的形状特征，使用可学习的形状原语分解和重构形状特征，还提出解码模块通过形状掩码和边缘对齐原始和重构的形状特征以使其更具形状感知能力，最后将所有模块组装成了一个少样本形状识别方案，实验证明我们的 FSSD 在少样本情况下相比最先进方法明显提升了形状分类。

通过学习深度形状感知特征进行少样本形状识别

Few-shot Shape Recognition by Learning Deep Shape-aware Features

Point set registration is defined as a process to determine the spatial
transformation from the source point set to the target one. Existing methods
often iteratively search for the optimal geometric transformation to register a
given pair of point sets, driven by minimizing a predefined alignment loss
function. In contrast, the proposed point registration neural network (PR-Net)
actively learns the registration pattern as a parametric function from a
training dataset, consequently predict the desired geometric transformation to
align a pair of point sets. PR-Net can transfer the learned knowledge (i.e.
registration pattern) from registering training pairs to testing ones without
additional iterative optimization. Specifically, in this paper, we develop
novel techniques to learn shape descriptors from point sets that help formulate
a clear correlation between source and target point sets. With the defined
correlation, PR-Net tends to predict the transformation so that the source and
target point sets can be statistically aligned, which in turn leads to an
optimal spatial geometric registration. PR-Net achieves robust and superior
performance for non-rigid registration of point sets, even in presence of
Gaussian noise, outliers, and missing points, but requires much less time for
registering large number of pairs. More importantly, for a new pair of point
sets, PR-Net is able to directly predict the desired transformation using the
learned model without repetitive iterative optimization routine. Our code is
available at this https URL

该论文介绍了一种基于神经网络的点集配准方法 PR-Net，该方法通过学习训练数据集中的点集的形状描述符，预测了点集之间的空间变换，以最优的方式配准点集，具有较强的鲁棒性，并可以应用于非刚性配准，对于新的点集配准，可以直接使用训练好的模型预测所需的变换。