Scholarlink

<p>This study introduces DMR-PointHop, standing for deep learning-based multi-resolution PointHop, a novel multi-scale fusion-based method for 3D point cloud registration. The method leverages multi-level downsampling to create datasets of varying scales and establishes local reference coordinate systems for feature extraction at each point. The extraction, fusion, and hierarchical learning of features across different scales enable DMR-PointHop to capture information from local to global levels. Subsequently, it identifies a salient subset of points with reliable correspondence for accurate point cloud matching prediction. The method utilizes singular value decomposition to compute rigid transformations, ensuring precise registration with robustness to rotation and translation. The study demonstrates that DMR-PointHop contributes to advancements in artificial intelligence by providing a more efficient and environmentally friendly point cloud registration solution compared to traditional methods and some deep learning approaches.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Efficient multi-scale 3D point cloud registration

  • 期刊论文

  • 发表日期：

    2025年1月8日

查看全文
  • Zhijie Tang,
  • Ke Meng

摘要

This study introduces DMR-PointHop, standing for deep learning-based multi-resolution PointHop, a novel multi-scale fusion-based method for 3D point cloud registration. The method leverages multi-level downsampling to create datasets of varying scales and establishes local reference coordinate systems for feature extraction at each point. The extraction, fusion, and hierarchical learning of features across different scales enable DMR-PointHop to capture information from local to global levels. Subsequently, it identifies a salient subset of points with reliable correspondence for accurate point cloud matching prediction. The method utilizes singular value decomposition to compute rigid transformations, ensuring precise registration with robustness to rotation and translation. The study demonstrates that DMR-PointHop contributes to advancements in artificial intelligence by providing a more efficient and environmentally friendly point cloud registration solution compared to traditional methods and some deep learning approaches.