基于多维自适应特征融合和信息通道注意力机制的点云完成网络
Di Tian1, Jiahang Shi1, Jiabo Li1
1Mechanical Engineering College, Xi'an Shiyou University, Xi'an 710065, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究介绍了点云完成的新方法,通过有效重建本地细节和全球特征来提高3D数据完整性. 这种新型网络增强了细节重建,用于实际的3D感知应用.
科学领域:
- 计算机视觉 计算机视觉
- 3D数据处理 3D数据处理
- 几何深度学习 几何深度学习
背景情况:
- 点云数据对于3D感知至关重要,但由于设备限制和环境因素,它们往往含有漏洞.
- 现有的点云完成方法往往优先考虑全球特征,忽视本地结构细节和细粒度控制.
- 这导致数据不完整,重建点云的完整性降低.
研究的目的:
- 为了解决当前点云完成技术的局限性.
- 开发一种有效地捕获本地和全球特征的方法,以改善细节重建.
- 为了提高点云数据的整体特征表示和完成精度.
主要方法:
- 提出了一组组合多层感知器 (SCMP) 模块,用于同时进行本地和全球特征提取.
- 引入了一个Squeeze Excitation Pooling Network (SEP-Net) 模块,用于适应性道注意力和功能增强.
- 开发了一个特征融合点碎形网络 (FFPF-Net),将这些模块集成为多维特征融合和渐进的精细化.
主要成果:
- 在ShapeNet-Part和MVP数据集上,FFPF-Net表现出与L-GAN和PCN相比显著的改进,平均预测错误减少1.3和1.4.
- 在ShapeNet-Part上实现了0.783的平均完成错误,在MVP上达到0.824,展示了增强的细节重建.
- 该方法有效地解决了局部细节信息的丢失,并提高了数据完整性.
结论:
- 拟议的FPFF-Net通过将本地和全球特征提取与注意力机制相结合,有效地提高了点云完成性能.
- 网络能够重建细节的能力提高了数据完整性,使其适合于实际的3D感知任务.
- 这一进步促进了点云数据在各种现实世界的场景中更广泛地应用.
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