.png){kind=icon}
返回
Multi-scale sparse convolution and point convolution adaptive fusion point cloud semantic segmentation method
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-025-88905-5.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Semantic segmentation of LIDAR point clouds is essential for autonomous driving. However, current methods often suffer from low segmentation accuracy and feature redundancy. To address these issues, this paper proposes a novel approach based on adaptive fusion of multi-scale sparse convolution and point convolution. First, addressing the drawbacks of redundant feature extraction with existing sparse 3D convolutions, we introduce an asymmetric importance of space locations (IoSL) sparse 3D convolution module. By prioritizing the importance of input feature positions, this module enhances the sparse learning performance of critical feature information. Additionally, it strengthens the extraction capability of intrinsic feature information in both vertical and horizontal directions. Second, to mitigate significant differences between single-type and single-scale features, we propose a multi-scale feature fusion cross-gating module. This module employs gating mechanisms to improve fusion accuracy between different scale receptive fields. It utilizes a cross self-attention mechanism to adapt to the unique propagation features of point features and voxels, enhancing feature fusion performance. Experimental comparisons and ablation studies conducted on the SemanticKITTI and nuScenes datasets validate the generality and effectiveness of the proposed approach. Compared with state-of-the-art methods, our approach significantly improves accuracy and robustness.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
28.3W
被引数:
83.5W
机构
引用论文
PCSCNet: Fast 3D semantic segmentation of LiDAR point cloud for autonomous car using point convolution and sparse convolution networkPCSCNet: 基于点卷积和稀疏卷积网络的自动驾驶汽车激光雷达点云快速三维语义分割OAAI
OAAI分享收藏GLSNet plus plus : Global and Local-Stream Feature Fusion for LiDAR Point Cloud Semantic Segmentation Using GNN Demixing Block.png)
IEEE SENSORS JOURNAL
OAAI
IEEE SENSORS JOURNALIF4.5
OAAI分享收藏Semantic segmentation of large-scale point clouds by integrating attention mechanisms and transformer models融合注意力机制和transformer模型的大规模点云语义分割
PREAI
分享收藏Pressure-Based Bioassay Perceived by a Flexible Pressure Sensor with Synergistic Enhancement of the Photothermal Effect通过具有协同增强光热效应的柔性压力传感器感知的基于压力的生物测定
PREAI
分享收藏
查看更多内容