Large-scale point cloud consists of a multitude of individual objects,
thereby encompassing rich structural and underlying semantic contextual
information, resulting in a challenging problem in efficiently segmenting a
point cloud. Most existing researches mainly focus on capturing intricate local
features without giving due consideration to global ones, thus failing to
leverage semantic context. In this paper, we propose a Similarity-Weighted
Convolution and local-global Fusion Network, named SWCF-Net, which takes into
account both local and global features. We propose a Similarity-Weighted
Convolution (SWConv) to effectively extract local features, where similarity
weights are incorporated into the convolution operation to enhance the
generalization capabilities. Then, we employ a downsampling operation on the K
and V channels within the attention module, thereby reducing the quadratic
complexity to linear, enabling the Transformer to deal with large-scale point
clouds. At last, orthogonal components are extracted in the global features and
then aggregated with local features, thereby eliminating redundant information
between local and global features and consequently promoting efficiency. We
evaluate SWCF-Net on large-scale outdoor datasets SemanticKITTI and Toronto3D.
Our experimental results demonstrate the effectiveness of the proposed network.
Our method achieves a competitive result with less computational cost, and is
able to handle large-scale point clouds efficiently.

通过提出了一种名为 SWCF-Net 的相似度加权卷积和局部 - 全局融合网络，结合了局部和全局特征，以提高大规模点云的分割效率和语义上下文理解能力。通过将相似度权重融入卷积操作中，以增强泛化能力，从而有效地提取局部特征。同时，在注意力模块中对关键通道进行下采样操作，实现了线性复杂度，使得 Transformer 能够高效处理大规模点云。最后，从全局特征中提取正交分量，并与局部特征进行聚合，消除局部和全局特征之间的冗余信息，从而提高了效率。实验结果在大规模室外数据集 SemanticKITTI 和 Toronto3D 上验证了 SWCF-Net 的有效性，并且与较低计算成本相比，获得了有竞争力的结果，能够高效处理大规模点云。