Authors
Honglin Tan, Fen Chen, Tingna Liu, Sisi Zhang, Lian Huang, Zongju Peng
Published in
Applied optics. Volume 65. Issue 23. Pages 7791-7803. Aug 10, 2026.
Abstract
LiDAR semantic segmentation is significant in applications such as autonomous driving and robot navigation, as it greatly improves scene perception and object detection. However, the existing methods face the challenges of achieving high segmentation accuracy while maintaining low computational cost and complexity. In this paper, we propose a new, to our knowledge, efficient and accurate semantic segmentation network for LiDAR, called Range-FDSeg. To reduce the risk of information compression and loss when projecting 3D point cloud data onto 2D range images, we design a multi-channel fusion interactive learning (FIL) module. This module effectively integrates multimodal channels, such as coordinates, depth, and reflectivity, for interactive learning. As a result, FIL module can reduce the noise interference inherent in individual channels and capture the underlying relationships between different physical quantities. To further improve the performance, we introduce a lightweight and dynamic upsampler, called Dysample-S+. It effectively resolves the inherent challenges of traditional sampling methods through its adaptive weighting mechanism, which dynamically adjusts to local geometric patterns and density variations in raw point clouds. Extensive evaluations on publicly available benchmark datasets, including SemanticKITTI, SemanticPOSS, and NuScenes, demonstrate that the proposed Range-FDSeg outperforms most existing state-of-the-art methods.
PMID:
42593449
Bibliographic data and abstract were imported from PubMed on 13 Aug 2026.
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