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数据加权的多变量通用高斯混合模型:应用到点云强大的注册应用
Bingwei Ge1, Fatma Najar1, Nizar Bouguila1
1Concordia Institute for Information Systems Engineering, Concordia University, 1515 St. Catherine Street West, Montreal, QC H3G 2W1, Canada.
Journal of imaging
|September 27, 2023
概括
本研究介绍了一种新的方法,用于3D点云注册,使用通用高斯混合模型和随机优化. 该算法有效地处理噪声和异常值,改善特征提取,以获得准确的场景匹配.
科学领域:
- 计算机视觉 计算机视觉
- 计算几何学的计算几何学
- 机器学习 机器学习
背景情况:
- 点云注册对于3D场景重建和分析至关重要.
- 现有的方法经常与噪声,异常值和变化的数据密度作斗争.
- 对于现实世界的应用,需要强大而准确的注册算法.
研究的目的:
- 提出一个新的加权多变量通用高斯混合模型,用于点云注册.
- 为了提高登记准确性和对噪声和异常值的稳定性.
- 为参数估计开发一种高效的随机优化方法.
主要方法:
- 使用加权多变量通用高斯混合模型.
- 使用预期最大化 (EM) 算法与固定点方法进行参数更新.
- 使用最小消息长度 (MML) 标准确定组件的数量.
- 应用KL分歧作为损失函数用于随机优化.
- 在自建点云上评估性能,以进行刚性注册.
主要成果:
- 拟议的算法大大降低了噪音和异常值的影响.
- 从数据密集型地区有效提取关键特征.
- 在刚性点云注册中表现出强大的性能.
- 该方法显示了准确的3D场景匹配的前景.
结论:
- 加权多变量通用高斯混合模型与随机优化相结合,为点云注册提供了强大的解决方案.
- 该算法的处理杂数据和提取突出特征的能力使其适合复杂的3D环境.
- 这种方法推进了3D点云处理和注册的最新技术.
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