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。