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 Transformer to deal with large-scale point cloud. 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. The code is available at https://github.com/Sylva-Lin/SWCF-Net.

In this contribution, a recently conducted measurement campaign for indoor millimeter-wave (mm-wave) propagation channels is introduced. A vector network analyzer (VNA)-based channel sounder was exploited to record the channel characteristics at the frequency band from 28-30 GHz. A virtual uniform circular array (UCA) with a radius of 0.25 m was formed using a rotator with 360 steps. Moreover, by taking advantage of fiber-optic technique applied in the channel sounder, measurements at 50 positions were performed from an indoor hall to an indoor corridor along a long pre-defined route. A low-complexity high-resolution propagation estimation (HRPE) algorithm is exploited to estimate the propagation parameters of multipath components (MPCs). Based on the HRPE estimation results, a novel clustering identification and tracking algorithm is proposed to trace clusters. Composite channel characteristics, cluster-level characteristics and dynamic (or birth-death) behaviours of the clusters are investigated, which constitute a dynamic model for the indoor mm-wave channel.