Work

Real-Time LiDAR 3D Semantic Segmentation via Multi-View and Cross-Modal Compact Featuring Two-Branch Knowledge Distillation

Year: 2026

Type: article

Abstract: Simultaneous online mapping and semantic segmentation using handheld scanners supports various environmental inspection and measurement tasks. For such scanners, combing visual and LiDAR data is beneficial for improving the segmentation performance. But the direct fusion of multi-modal and multi-view features faces challenges in terms of both real-time performance and robustness. To address these challenges, this paper proposes a multi-view and cross-modal knowledge distillation method for supporting runtime LiDAR-only semantic segmentation. The proposed method hierarchically compacts multi-view and cross-model priors and distills them into two branches to improve segmentation accuracy. In addition, we design an improved data augmentation technique based on PolarMix for rendering more realistic point cloud scenes. The experimental results on the SemanticKITTI and nuScenes datasets demonstrate that the mIoU of our approach outperforms the state-of-the-art knowledge-distillation-based methods. In addition, mapping experiments using a handheld scanner demonstrate the proposed method's superior real-time performance and accuracy.

Source: Sensors

DOI: 10.3390/s26061860

Authors: Yun Zhang, Kun Qian, Zihan Zhang, Min’ao Zhang, Hai Yu

Institutions: Southeast University, China Electric Power Research Institute

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Basic and Applied Basic Research Foundation of Guangdong Province