.png){kind=icon}
返回
Local-non-local complementary learning network for 3D point cloud analysis
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-024-84248-9.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Point cloud analysis is integral to numerous applications, including mapping and autonomous driving. However, the unstructured and disordered nature of point clouds presents significant challenges for feature extraction. While both local and non-local features are essential for effective 3D point cloud analysis, existing methods often fail to seamlessly integrate these complementary features. To address this limitation, we propose the Local-Non-Local Complementary Learning Network (LNLCL-Net), a novel framework that enhances feature extraction and representation. Leveraging partial convolution, LNLCL-Net divides the feature map into distinct local and non-local components. Local features are modeled through relative positional relationships, while non-local features capture absolute positional information. A Complementary Interactive Attention module is introduced to enable adaptive integration of these features, enriching their complementary relationship. Extensive experiments on benchmark datasets, including ModelNet40, ScanObjectNN, and ShapeNet Part, demonstrate the superiority of our approach in both quantitative and qualitative metrics, achieving state-of-the-art performance in classification and segmentation tasks.
Keyword:
Point cloud
Classification
Segmentation
Local-no-local feature
Complementary Learning
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
Evidence for a specialized role of the locus coeruleus noradrenergic system in cortical circuitries and behavioral operations蓝斑去甲肾上腺素能系统在皮质回路和行为操作中具有特殊作用的证据OAAI
.png){kind=icon}
OAAI分享收藏分享收藏分享收藏
查看更多内容