Multilateral Cascading Network for Semantic Segmentation of Large-Scale Outdoor Point Clouds

• URL: http://arxiv.org/abs/2409.13983v2
• Date: Sun, 15 Dec 2024 05:18:36 GMT
• Title: Multilateral Cascading Network for Semantic Segmentation of Large-Scale Outdoor Point Clouds
• Authors: Haoran Gong, Haodong Wang, Di Wang,
• Abstract summary: Multilateral Cascading Network (MCNet) designed to address this challenge. MCNet comprises two key components: a Multilateral Cascading Attention Enhancement (MCAE) module, and a Point Cross Stage Partial (P-CSP) module. 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.
• Score: 6.253217784798542
• License:
• 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.

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