We present a new versatile building block for deep point cloud processing architectures that is equally suited for diverse tasks. This building block combines the ideas of spatial transformers and multi-view convolutional networks with the efficiency of standard convolutional layers in two and three-dimensional dense grids. The new block operates via multiple parallel heads, whereas each head differentiably rasterizes feature representations of individual points into a low-dimensional space, and then uses dense convolution to propagate information across points. The results of the processing of individual heads are then combined together resulting in the update of point features. Using the new block, we build architectures for both discriminative (point cloud segmentation, point cloud classification) and generative (point cloud inpainting and image-based point cloud reconstruction) tasks. The resulting architectures achieve state-of-the-art performance for these tasks, demonstrating the versatility and universality of the new block for point cloud processing.

翻译：我们为深点云层处理结构展示了新的多功能构件,这种构件同样适合多种任务。 这个构件将空间变压器和多视图变速网络的理念与标准变速层在两维和三维密度电网中的效率相结合。 新构件通过多个平行头部运行,而每个头部将单个点的特征表达方式不同地分解成一个低维空间,然后用密集的变速来在不同点传播信息。 然后将单个头的处理结果合并在一起,从而更新点特征。 我们利用新构件,为区分性(点云分解、点云分级)和基因(点云的成形和图像的点云重造)任务构建了结构。 由此形成的构件可以实现这些任务的最新性表现, 展示了点云处理新块的多功能性和普遍性。