论文标题
多尺度网络具有注意点云语义分割的注意力多分辨率融合
Multi-scale Network with Attentional Multi-resolution Fusion for Point Cloud Semantic Segmentation
论文作者
论文摘要
在本文中，我们提出了一个综合的点云语义细分网络，该网络汇总了本地和全球多尺度信息。首先，我们提出一个角度相关点卷积（ACPCONV）模块，以有效地了解点的局部形状。其次，基于ACPCONV，我们引入了一个局部的多尺度拆分（MSS）块，该块在一个单个块中层次连接特征，并逐渐扩大了接受场，这对利用本地上下文是有益的。第三，受HRNet的启发，在2D图像视觉任务上具有出色的性能，我们构建了针对Point Cloud定制的HRNET，以学习全局多尺度上下文。最后，我们介绍了一种融合多分辨率预测的点上的注意融合方法，并进一步改善了点云语义分割性能。我们在几个基准数据集上的实验结果和消融表明，与现有方法相比，我们提出的方法有效，能够实现最先进的性能。
In this paper, we present a comprehensive point cloud semantic segmentation network that aggregates both local and global multi-scale information. First, we propose an Angle Correlation Point Convolution (ACPConv) module to effectively learn the local shapes of points. Second, based upon ACPConv, we introduce a local multi-scale split (MSS) block that hierarchically connects features within one single block and gradually enlarges the receptive field which is beneficial for exploiting the local context. Third, inspired by HRNet which has excellent performance on 2D image vision tasks, we build an HRNet customized for point cloud to learn global multi-scale context. Lastly, we introduce a point-wise attention fusion approach that fuses multi-resolution predictions and further improves point cloud semantic segmentation performance. Our experimental results and ablations on several benchmark datasets show that our proposed method is effective and able to achieve state-of-the-art performances compared to existing methods.
