Scholarlink

<p>Point clouds have become essential for digital heritage preservation, aiding in the identification and classification of complex structural elements. However, most datasets rely on single-modal data, limiting their ability to describe real-world scenarios comprehensively. This paper introduces the Real-World Multi-modal Ancient Architecture Point Cloud Semantic Segmentation Dataset (RW-MAPCSD), which includes multi-modal data such as point clouds, line drawings, color, and depth projections, enabling multi-modal analysis of ancient buildings. To address data imbalance, we propose a novel segmentation network, Mask2former-KNN 3D Network (MK3DNet). The network projects point clouds into images while preserving point indices, using image segmentation techniques for initial segmentation. Results are then mapped back to the point cloud and refined with the K-nearest neighbors (KNN) algorithm. Experimental results show significant improvements, with mIoU and OA of 77.47% and 90.85%, respectively, surpassing the Point Transformer network by 21.94% and 5.87%.</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

Cross modal networks for point cloud semantic segmentation of Chinese ancient buildings

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月3日

查看全文
  • Zhiying Xie,
  • Hanxin Liu,
  • Yuanrong He,
  • Yuanhao Shi,
  • Peng Yu,
  • Junqiang Ai,
  • Liang Zhong

摘要

Point clouds have become essential for digital heritage preservation, aiding in the identification and classification of complex structural elements. However, most datasets rely on single-modal data, limiting their ability to describe real-world scenarios comprehensively. This paper introduces the Real-World Multi-modal Ancient Architecture Point Cloud Semantic Segmentation Dataset (RW-MAPCSD), which includes multi-modal data such as point clouds, line drawings, color, and depth projections, enabling multi-modal analysis of ancient buildings. To address data imbalance, we propose a novel segmentation network, Mask2former-KNN 3D Network (MK3DNet). The network projects point clouds into images while preserving point indices, using image segmentation techniques for initial segmentation. Results are then mapped back to the point cloud and refined with the K-nearest neighbors (KNN) algorithm. Experimental results show significant improvements, with mIoU and OA of 77.47% and 90.85%, respectively, surpassing the Point Transformer network by 21.94% and 5.87%.