Attention mechanism plays a more and more important role in point cloud analysis and channel attention is one of the hotspots. With so much channel information, it is difficult for neural networks to screen useful channel information. Thus, an adaptive channel encoding mechanism is proposed to capture channel relationships in this paper. It improves the quality of the representation generated by the network by explicitly encoding the interdependence between the channels of its features. Specifically, a channel-wise convolution (Channel-Conv) is proposed to adaptively learn the relationship between coordinates and features, so as to encode the channel. Different from the popular attention weight schemes, the Channel-Conv proposed in this paper realizes adaptability in convolution operation, rather than simply assigning different weights for channels. Extensive experiments on existing benchmarks verify our method achieves the state of the arts.
翻译:关注机制在云点分析和频道关注方面发挥越来越重要的作用,它是一个热点。由于有这么多的频道信息,神经网络很难筛选有用的频道信息。因此,建议采用适应性频道编码机制来捕捉本文件中的频道关系。它通过明确将网络特征各渠道之间的相互依存性编码来提高网络代表的质量。具体地说,建议以频道为方向的共变机制(Channel-Conv)来适应性地学习坐标和特征之间的关系,以便编码频道。与公众关注权重计划不同,本文中提议的Channel-Conv(Channel-Conv)与公众关注权重计划不同,它认识到了连带操作的适应性,而不是简单地为频道分配不同的权重。关于现有基准的广泛实验可以验证我们的方法是否达到了艺术的状态。