Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds. Permutation-equivariant networks have become a popular solution-they operate on individual data points with simple perceptrons and extract contextual information with global pooling. This can be achieved with a simple normalization of the feature maps, a global operation that is unaffected by the order. In this paper, we propose Attentive Context Normalization (ACN), a simple yet effective technique to build permutation-equivariant networks robust to outliers. Specifically, we show how to normalize the feature maps with weights that are estimated within the network, excluding outliers from this normalization. We use this mechanism to leverage two types of attention: local and global-by combining them, our method is able to find the essential data points in high-dimensional space to solve a given task. We demonstrate through extensive experiments that our approach, which we call Attentive Context Networks (ACNe), provides a significant leap in performance compared to the state-of-the-art on camera pose estimation, robust fitting, and point cloud classification under noise and outliers. Source code: https://github.com/vcg-uvic/acne.
翻译:计算机视野中的许多问题要求处理以点云形式出现的稀少、无序的数据。变异-异端网络已经成为一个受欢迎的解决方案,它们以简单的光谱运行于单个数据点,通过全球共享来提取背景信息。这可以通过地貌地图的简单正常化来实现,这是一个不受命令影响的全球性行动。在本文件中,我们提议了“加速背景正常化”这一简单而有效的方法,用以建立对外部线强力的变异-异端网络。具体地说,我们展示了如何使地貌地图与网络内估计的重量实现正常化,将外部线从这一正常化中排除出来。我们利用这一机制来利用两种类型的注意:将地方和全球的注意结合起来,我们的方法能够找到高空空间的基本数据点来完成某项特定任务。我们通过广泛的实验表明,我们的方法,即我们称之为“惯性环境网络”的“Attention-conternal 网络(ACNe)”,与摄影的状态显示估计、稳健的安装和点云的分类在噪音和外部线下。源码: http://giev/qubqu/des codedection: