Learning discriminative shape representation directly on point clouds is still challenging in 3D shape analysis and understanding. Recent studies usually involve three steps: first splitting a point cloud into some local regions, then extracting the corresponding feature of each local region, and finally aggregating all individual local region features into a global feature as shape representation using simple max-pooling. However, such pooling-based feature aggregation methods do not adequately take the spatial relationships (e.g. the relative locations to other regions) between local regions into account, which greatly limits the ability to learn discriminative shape representation. To address this issue, we propose a novel deep learning network, named Point2SpatialCapsule, for aggregating features and spatial relationships of local regions on point clouds, which aims to learn more discriminative shape representation. Compared with the traditional max-pooling based feature aggregation networks, Point2SpatialCapsule can explicitly learn not only geometric features of local regions but also the spatial relationships among them. Point2SpatialCapsule consists of two main modules. To resolve the disorder problem of local regions, the first module, named geometric feature aggregation , is designed to aggregate the local region features into the learnable cluster centers, which explicitly encodes the spatial locations from the original 3D space. The second module, named spatial relationship aggregation , is proposed for further aggregating the clustered features and the spatial relationships among them in the feature space using the spatial-aware capsules developed in this article. Compared to the previous capsule network based methods, the feature routing on the spatial-aware capsules can learn more discriminative spatial relationships among local regions for point clouds, which establishes a direct mapping between log priors and the spatial locations through feature clusters. Experimental results demonstrate that Point2SpatialCapsule outperforms the state-of-the-art methods in the 3D shape classification, retrieval and segmentation tasks under the well-known ModelNet and ShapeNet datasets.

中文翻译：

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Point2SpatialCapsule：使用空间感知型胶囊聚合点云上局部区域的特征和空间关系。

在3D形状分析和理解中，直接在点云上学习判别形状表示仍然具有挑战性。最近的研究通常包括三个步骤：首先将点云划分为一些局部区域，然后提取每个局部区域的相应特征，最后使用简单的最大池将所有局部区域的特征汇总为形状表示的全局特征。然而，这样的基于池的特征聚合方法没有充分考虑局部区域之间的空间关系（例如，与其他区域的相对位置），这极大地限制了学习判别形状表示的能力。为了解决这个问题，我们提出了一个新颖的深度学习网络，名为Point2SpatialCapsule，用于聚集点云上局部区域的特征和空间关系，目的是学习更多的判别性形状表示。与传统的基于最大池的特征聚合网络相比，Point2SpatialCapsule不仅可以显式学习局部区域的几何特征，还可以显式学习局部区域之间的空间关系。Point2SpatialCapsule由两个主要模块组成。为了解决局部区域的混乱问题，第一个模块名为几何特征聚集 旨在将局部区域特征聚合到可学习的聚类中心，该聚类中心显式地编码原始3D空间中的空间位置。第二个模块，名为空间关系聚集 提出了利用本文开发的空间感知胶囊在特征空间中进一步聚集聚类特征及其之间的空间关系的方法。与以前的基于胶囊网络的方法相比，空间感知胶囊上的特征路由可以了解点云局部区域之间更具区别的空间关系，从而通过特征聚类在对数先验和空间位置之间建立直接映射。实验结果表明，在著名的ModelNet和ShapeNet数据集下，Point2SpatialCapsule优于3D形状分类，检索和分割任务中的最新方法。