Deep learning has achieved remarkable results in 3D shape analysis by
learning global shape features from the pixel-level over multiple views.
Previous methods, however, compute low-level features for entire views without
considering part-level information. In contrast, we propose a deep neural
network, called Parts4Feature, to learn 3D global features from part-level
information in multiple views. We introduce a novel definition of generally
semantic parts, which Parts4Feature learns to detect in multiple views from
different 3D shape segmentation benchmarks. A key idea of our architecture is
that it transfers the ability to detect semantically meaningful parts in
multiple views to learn 3D global features. Parts4Feature achieves this by
combining a local part detection branch and a global feature learning branch
with a shared region proposal module. The global feature learning branch
aggregates the detected parts in terms of learned part patterns with a novel
multi-attention mechanism, while the region proposal module enables locally and
globally discriminative information to be promoted by each other. We
demonstrate that Parts4Feature outperforms the state-of-the-art under three
large-scale 3D shape benchmarks.

本文介绍了一种名为 Parts4Feature 的深度学习网络，它可以从多视图的部分级信息中学习三维全局特征，利用多重关注机制将检测到的部分聚合到全局特征中， 并通过区域建议模块将局部和全局信息相互促进，从而实现了在三大 3D 形状基准测试中的良好表现。

Parts4Feature: 从多视图中的语义部分学习 3D 全局特征

Parts4Feature: Learning 3D Global Features from Generally Semantic Parts  in Multiple Views

In this paper, we propose a novel method called AlignedReID that extracts a
global feature which is jointly learned with local features. Global feature
learning benefits greatly from local feature learning, which performs an
alignment/matching by calculating the shortest path between two sets of local
features, without requiring extra supervision. After the joint learning, we
only keep the global feature to compute the similarities between images. Our
method achieves rank-1 accuracy of 94.4% on Market1501 and 97.8% on CUHK03,
outperforming state-of-the-art methods by a large margin. We also evaluate
human-level performance and demonstrate that our method is the first to surpass
human-level performance on Market1501 and CUHK03, two widely used Person ReID
datasets.

本文提出了一种名为 AlignedReID 的新方法，通过局部特征的对齐匹配和全局特征的共同学习，提高了全局特征的学习效果并且超越了现有的 Person ReID 方法。在 Market1501 和 CUHK03 这两个广泛应用的人员再识别数据集方面，相对于现有的最优方法，本方法的排名 1 准确度分别提高了 94.4% 和 97.8%，超过了人类的水平。