BeyondPoints: Curve Fusion and Attention-Driven Local Feature Learning for 3-D Semantic Segmentation

Efficient semantic segmentation of large-scale point cloud scenes is regarded as a fundamental and essential task for perceiving and understanding 3-D environments. It is also recognized as a key technology for environmental perception and intelligent decision-making in Internet of Things (IoT) applications. However, the diversity of objects and occlusion issues within scenes often hinder the ability of existing networks to effectively represent varying object shapes, leading to point information ambiguity and loss caused by pose variations. To address these challenges, a novel curve fusion and attention-driven local feature learning network (BeyondPoints) is proposed for point cloud segmentation. The proposed network consists of three key modules: 1) a local feature enhancement (LFE) module; 2) a dual-axis attention (DAA) module; and 3) a hybrid curve fusion (HCF) module. Specifically, the LFE module explicitly models spatial relationships and decouples local aggregation, effectively integrating additional geometric information into local features to compensate for point information loss and enhance environmental perception. To further alleviate local spatial perception ambiguity, the DAA module is designed to extract critical information from different spatial positions, thereby enhancing the representation of significant regions in point clouds and achieving more precise semantic segmentation. Finally, the HCF module serializes point cloud data to reduce model computational complexity while enabling cross-domain feature fusion, effectively integrating contextual information and suppressing noise interference. Extensive experiments on benchmark datasets, such as S3DIS, ScanNetV2, and SemanticKITTI, validate the exceptional segmentation performance of the proposed BeyondPoints network. Particularly, its superior performance in large-scale point cloud scenes underscores its potential to enhance environmental information perception in IoT scenarios.