GFE-Net: Geometry-Enhanced Feature Extraction Network for Semantic Segmentation of Large-Scale LiDAR Point Clouds

Abstract

Accurate semantic segmentation of large-scale outdoor LiDAR point clouds remains a challenging endeavor, primarily due to ambiguous class transitions at object interfaces, non-uniform sampling density across the surveyed area, and shared geometric signatures among distinct object categories. This paper proposes GFE-Net (Geometry-Enhanced Feature Extraction Network), a hierarchical encoder–decoder architecture that systematically improves per-point feature characterization through three complementary design contributions: First, to mitigate the shortcomings of conventional fixed-neighborhood queries in regions of variable point density, a Structure-Guided Neighborhood Adaptation (SGNA) module is devised. At its core lies a morphology-driven contextual gating (MCG) unit that synthesizes neighbor-wise calibration weights from hierarchical shape descriptors fused with elevation difference statistics, allowing the network to preferentially amplify morphologically congruent neighbors while dampening spurious or cross-boundary contributions. Second, to strengthen semantic discrimination beyond what spatial locality alone affords, a Local–Global Interactive Enhancement (LGIE) module is presented. The LGIE module simultaneously distills precise local structure through Euclidean-space neighborhood graphs and captures scene-wide co-activation patterns via compact bilinear factorization of the latent feature space, merging both streams through a residual refinement mechanism that markedly improves inter-class separability. Third, to enforce label consistency at object interfaces without relying on post-processing heuristics, a Neighborhood Prediction Consistency (NPC) loss is introduced. Built upon a Gaussian distance-decay weighting kernel, the NPC loss assigns progressively stronger penalties to label mismatches between a query point and its geometrically proximate neighbors, thereby promoting spatially coherent predictions and attenuating boundary noise. GFE-Net is rigorously benchmarked on two widely adopted large-scale datasets—S3DIS and SensatUrban—yielding OA/mIoU of 89.6%/73.1% and 93.3%/61.1%, respectively. These results demonstrate competitive performance under the reported protocols. Detailed ablation studies and computational profiling further substantiate the efficacy of each individual component.