论文标题
使用多分辨率功能和可学习的合并在点云上的高级功能学习
Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling
论文作者
论文摘要
现有的Point Cloud特征学习网络通常结合采样，邻域分组，邻里功能学习和特征聚合的序列，以学习代表点云的全局上下文的高音点特征。不幸的是，由于采样和最大汇总而导致的有关粒度和非最大点特征的信息丢失可能会对现有网络的高音点特征产生不利影响，因此它们不足以代表点云的局部环境，而这反过来又可能阻碍网络区分优质形状。为了解决这个问题，我们使用多分辨率特征学习和可学习的池（LP）提出了一个新颖的点云学习网络PointStack。多分辨率特征学习是通过多层分辨率的各种分辨率的汇总点特征实现的，因此最终点功能既包含高音和高分辨率信息。另一方面，LP用作通用池函数，该功能通过带有可学习查询的注意机制来计算多分辨率点特征的加权总和，以便从所有可用点功能中提取所有可能的信息。因此，PointStack能够提取高语义点特征，并且有关粒度和非最大点特征的信息损失最小。因此，最终的汇总点特征可以有效地代表点云的全局和本地上下文。此外，网络头可以很好地理解点云的全局结构和局部形状细节，这使点stack能够在点云上推进特征学习的最新学习。这些代码可在https://github.com/kaist-avelab/pointstack上找到。
Existing point cloud feature learning networks often incorporate sequences of sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation to learn high-semantic point features that represent the global context of a point cloud. Unfortunately, the compounded loss of information concerning granularity and non-maximum point features due to sampling and max pooling could adversely affect the high-semantic point features from existing networks such that they are insufficient to represent the local context of a point cloud, which in turn may hinder the network in distinguishing fine shapes. To cope with this problem, we propose a novel point cloud feature learning network, PointStack, using multi-resolution feature learning and learnable pooling (LP). The multi-resolution feature learning is realized by aggregating point features of various resolutions in the multiple layers, so that the final point features contain both high-semantic and high-resolution information. On the other hand, the LP is used as a generalized pooling function that calculates the weighted sum of multi-resolution point features through the attention mechanism with learnable queries, in order to extract all possible information from all available point features. Consequently, PointStack is capable of extracting high-semantic point features with minimal loss of information concerning granularity and non-maximum point features. Therefore, the final aggregated point features can effectively represent both global and local contexts of a point cloud. In addition, both the global structure and the local shape details of a point cloud can be well comprehended by the network head, which enables PointStack to advance the state-of-the-art of feature learning on point clouds. The codes are available at https://github.com/kaist-avelab/PointStack.
