The continual improvement of 3D sensors has driven the development of algorithms to perform point cloud analysis. In fact, techniques for point cloud classification and segmentation have in recent years achieved incredible performance driven in part by leveraging large synthetic datasets. Unfortunately these same state-of-the-art approaches perform poorly when applied to incomplete point clouds. This limitation of existing algorithms is particularly concerning since point clouds generated by 3D sensors in the real world are usually incomplete due to perspective view or occlusion by other objects. This paper proposes a general model for partial point clouds analysis wherein the latent feature encoding a complete point cloud is inferred by applying a point set voting strategy. In particular, each local point set constructs a vote that corresponds to a distribution in the latent space, and the optimal latent feature is the one with the highest probability. This approach ensures that any subsequent point cloud analysis is robust to partial observation while simultaneously guaranteeing that the proposed model is able to output multiple possible results. This paper illustrates that this proposed method achieves the state-of-the-art performance on shape classification, part segmentation and point cloud completion.

中文翻译：

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部分点云分析的点集投票

3D 传感器的不断改进推动了执行点云分析的算法的发展。事实上，近年来点云分类和分割技术取得了令人难以置信的性能，部分原因在于利用大型合成数据集。不幸的是，这些相同的最先进方法在应用于不完整的点云时表现不佳。现有算法的这种限制尤其令人担忧，因为现实世界中的 3D 传感器生成的点云通常由于透视图或其他对象的遮挡而不完整。本文提出了一种用于部分点云分析的通用模型，其中通过应用点集投票策略来推断编码完整点云的潜在特征。特别是，每个局部点集构造一个与潜在空间中的分布相对应的投票，最优潜在特征是概率最高的那个。这种方法确保任何后续的点云分析对局部观察都是稳健的，同时保证所提出的模型能够输出多个可能的结果。本文说明该方法在形状分类、零件分割和点云补全方面达到了最先进的性能。