GSSP-KAN: An Efficient Kansformer-Based Network with Grouped Separable Sparse Convolution for Large-Scale LiDAR Point Cloud Semantic Segmentation
TL;DR
The proposed GSSP-KAN is an efficient semantic segmentation network that integrates Kansformer with grouped separable sparse convolution, while the Grouped Separable Sparse Convolution Block (GSSP_Block) reduces redundant self-attention computation and preserves fine-grained local geometric structures.
Abstract
Semantic segmentation of large-scale power-corridor LiDAR point clouds is essential for remote sensing-based transmission line inspection, vegetation encroachment monitoring, and intelligent grid maintenance. However, existing methods still struggle with massive data volumes, severe class imbalance, sparse power-related objects, and high computational cost in power corridor scenes. To address these challenges, this article proposes GSSP-KAN, an efficient semantic segmentation network that integrates Kansformer with grouped separable sparse convolution. The Kansformer module enhances nonlinear feature representation and contextual modeling, while the Grouped Separable Sparse Convolution Block (GSSP_Block) reduces redundant self-attention computation and preserves fine-grained local geometric structures. GSSP-KAN is evaluated on four large-scale datasets, including NW-3D, NeiMeng-3D, Nanning, and Toronto-3D. Experimental results show that GSSP-KAN achieves 98.20% OA/88.50% mIoU on NW-3D, 99.96%/98.58% on NeiMeng-3D, 98.20%/96.70% on Nanning, and 97.90%/83.80% on Toronto-3D. Compared with the baseline, the proposed model reduces the parameter count to 14.7 M and accelerates inference by 24.0% on NW-3D and 30.8% on Toronto-3D.