M2CNet: LiDAR 3D Semantic Segmentation Based on Multi-level Multi-view Cross-attention Fusion for Autonomous Vehicles

Light Detection and Ranging (LiDAR) point cloud semantic segmentation is an essential task for autonomous vehicles, enabling precise environment perception for safe navigation. Existing point-based and voxel-based methods face limitations related to computational costs and quantization errors, especially dealing with large-scale point clouds. In contrast, 2D projection methods, including Range View and Bird's Eye View, have gained popularity for their balance of real-time performance and accuracy. However, most projection approaches overlook critical issues arising from projection, including 1) inter-class occlusion, 2) boundary-blurring, and 3) inadequate multi-scale feature extraction. To address these issues, we propose a Multi-level Multi-view Cross-attention Fusion Network (M2CNet), which fully leverages complementary information across different views. First, we employ parallel encoder-decoder networks to extract semantic information within a multi-task segmentation framework. Given the high correlation between views, we incorporate a Multi-view Feature Cross Fusion Module (MFCFM) to facilitate cross-view feature association, capturing potential complementarities to mitigate information loss from single-view projections. Then, we place a Multi-cascaded Enhanced Semantic Context Atrous Spatial Pyramid Pooling (MESCASPP) module between the encoder and decoder to capture multi-scale contextual information, enhancing pixel relationships effectively. Finally, the Kernel Point Convolution (KPConv) is employed to refine point-wise features at the output level. The proposed method is evaluated on urban datasets SemanticKITTI and nuScenes (achieving mIoU scores of 65.7% and 78.9% respectively), the off-road dataset RELLIS-3D (44.6%), and a real-world test set. Experimental results demonstrate that our method surpasses representative methods in both effectiveness and scene adaptability. The source code is publicly available at https://github.com/Terminal-lidar/M2CNet.