# Multilateral Cascading Network for Semantic Segmentation of Large-Scale Outdoor Point Clouds Source: [Multilateral Cascading Network for Semantic Segmentation of Large-Scale Outdoor Point Clouds]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:49:31.122Z Authors: Haoran Gong, Haodong Wang, Di Wang Published: 2024-09-21T02:23:01.000Z ## Abstract Semantic segmentation of large-scale outdoor point clouds is of significant importance in environment perception and scene understanding. However, this task continues to present a significant research challenge, due to the inherent complexity of outdoor objects and their diverse distributions in real-world environments. In this study, we propose the Multilateral Cascading Network (MCNet) designed to address this challenge. The model comprises two key components: a Multilateral Cascading Attention Enhancement (MCAE) module, which facilitates the learning of complex local features through multilateral cascading operations; and a Point Cross Stage Partial (P-CSP) module, which fuses global and local features, thereby optimizing the integration of valuable feature information across multiple scales. Our proposed method demonstrates superior performance relative to state-of-the-art approaches across two widely recognized benchmark datasets: Toronto3D and SensatUrban. Especially on the city-scale SensatUrban dataset, our results surpassed the current best result by 2.1\\% in overall mIoU and yielded an improvement of 15.9\\% on average for small-sample object categories comprising less than 2\\% of the total samples, in comparison to the baseline method. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()