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 of the new block for point cloud processing.

我们提出了一种新的深度点云处理架构的多功能建筑块，该建筑块将空间转换器和多视图卷积网络的思想与二维和三维密集网格中标准卷积层的效率相结合，实现信息的多个并行头的协同作业，以更新点特征，取得了点云处理方面生成和判别任务的最新成果。

云变压器：点云处理任务的通用方法

Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks

We propose "Areas of Attention", a novel attention-based model for automatic
image captioning. Our approach models the dependencies between image regions,
caption words, and the state of an RNN language model, using three pairwise
interactions. In contrast to previous attention-based approaches that associate
image regions only to the RNN state, our method allows a direct association
between caption words and image regions. During training these associations are
inferred from image-level captions, akin to weakly-supervised object detector
training. These associations help to improve captioning by localizing the
corresponding regions during testing. We also propose and compare different
ways of generating attention areas: CNN activation grids, object proposals, and
spatial transformers nets applied in a convolutional fashion. Spatial
transformers give the best results. They allow for image specific attention
areas, and can be trained jointly with the rest of the network. Our attention
mechanism and spatial transformer attention areas together yield
state-of-the-art results on the MSCOCO dataset.o meaningful latent semantic
structure in the generated captions.

提出了基于注意力机制的图像描述模型，该模型使用三种成对的交互来建立图像区域、描述文本和 RNN 语言模型之间的依赖关系，并运用空间变换器来生成最佳的区域关注机制，达到了 MSCOCO 数据集上的最佳结果。