We present PPFNet - Point Pair Feature NETwork for deeply learning a globally
informed 3D local feature descriptor to find correspondences in unorganized
point clouds. PPFNet learns local descriptors on pure geometry and is highly
aware of the global context, an important cue in deep learning. Our 3D
representation is computed as a collection of point-pair-features combined with
the points and normals within a local vicinity. Our permutation invariant
network design is inspired by PointNet and sets PPFNet to be ordering-free. As
opposed to voxelization, our method is able to consume raw point clouds to
exploit the full sparsity. PPFNet uses a novel $\textit{N-tuple}$ loss and
architecture injecting the global information naturally into the local
descriptor. It shows that context awareness also boosts the local feature
representation. Qualitative and quantitative evaluations of our network suggest
increased recall, improved robustness and invariance as well as a vital step in
the 3D descriptor extraction performance.

本文提出 PPFNet——Point Pair Feature 网络，用于深度学习全局细节感知的三维局部特征描述符，以在无组织的点云中找到对应点。通过在纯几何图形上学习本地描述符，并高度关注全局上下文，实现 3D 表示为点对特征及局部邻域内的点和法向量的组合，并使用新型 N-tuple loss 和架构将全局信息自然地注入本地描述符中，实现量化和定性评价，证明了上述方法在 3D 描述符提取性能中的作用。

PPFNet: 全局上下文感知的局部特征用于强健的三维点匹配

PPFNet: Global Context Aware Local Features for Robust 3D Point Matching

We propose an attentive local feature descriptor suitable for large-scale
image retrieval, referred to as DELF (DEep Local Feature). The new feature is
based on convolutional neural networks, which are trained only with image-level
annotations on a landmark image dataset. To identify semantically useful local
features for image retrieval, we also propose an attention mechanism for
keypoint selection, which shares most network layers with the descriptor. This
framework can be used for image retrieval as a drop-in replacement for other
keypoint detectors and descriptors, enabling more accurate feature matching and
geometric verification. Our system produces reliable confidence scores to
reject false positives---in particular, it is robust against queries that have
no correct match in the database. To evaluate the proposed descriptor, we
introduce a new large-scale dataset, referred to as Google-Landmarks dataset,
which involves challenges in both database and query such as background
clutter, partial occlusion, multiple landmarks, objects in variable scales,
etc. We show that DELF outperforms the state-of-the-art global and local
descriptors in the large-scale setting by significant margins. Code and dataset
can be found at the project webpage:
this https URL .

我们提出了一种基于卷积神经网络的关注局部特征描述符 DELF，它可以作为关键点探测器和描述符的一种替代品，能够产生可靠的置信度得分，能够有效地进行图像检索。我们对 Google-Landmarks 数据集进行了测试，结果表明 DELF 在大规模情况下优于最先进的全局和局部描述符。