<p>The LiDAR point cloud semantic segmentation task aims to achieve 3D environmental perception of the surrounding scene. Range image-based methods have shown promising prospects for real-time processing in the field of autonomous driving. However, the projection process of generating range images has the problem of stretching the spatial distance relationship between the original points. Furthermore, most existing works lack the ability to effectively capture the target’s boundary features and cannot compensate for the loss of high-resolution encoder features during downsampling. To address these issues, we propose MSCSeg, a range image-based multi-scale contextual network for point cloud semantic segmentation. Firstly, a branched asymmetric convolution residual block (BAResBlock) is employed to specifically process the stretched range image while aggregating multi-scale contextual information from three branches at low computational cost. Subsequently, we introduce a local context multi-scale strip convolution block (CSCB), which helps the network capture boundary features of objects. Finally, we employ a feature cascade attention block (FCAB) to aggregate information from the encoder and decoder, alleviating the problem of insufficient detail in low-resolution features. Experiments conducted on the outdoor street scene datasets SemanticPOSS and SemanticKITTI demonstrate that our MSCSeg achieves competitive segmentation performance while maintaining real-time inference speed.</p>