Scholarlink

<p>The acquisition of point cloud data is often disrupted by noise, which affects the accuracy of downstream tasks such as surface analysis and precision measurement. This noise reduction process is known as point cloud denoising or filtering. Current deep learning-based methods face challenges, including limited model diversity, insufficient robustness in handling diverse data, and difficulties in ensuring that noisy points converge quickly and effectively to a smooth surface. To address these issues, this paper proposes a new model called KANFilter. KANFilter consists of multiple KANModules, each of which is composed of a KANBlock and a decoder to simulate the real denoising process in an integrated manner. Additionally, a new denoising target is designed to capture the relationships among intermediate filtering results during the training process. Experimental results show that this strategy significantly improves the quality of filtering results, achieving a maximum improvement of 39.3% in the CD (Chamfer Distance) metric at a 10K resolution compared to previous algorithms, and a 25% improvement in convergence speed relative to conventional algorithms.The source code can be found at: <a href="https://github.com/zhouxyly/KANFilter">https://github.com/zhouxyly/KANFilter</a></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

Kanfilter: a simple and effective multi-module point cloud denoising model

  • 期刊论文

  • 发表日期：

    2025年7月31日

查看全文
  • Xiaoyu Zhou,
  • Chenyang Shi,
  • Donglin Zhu,
  • Changjun Zhou

摘要

The acquisition of point cloud data is often disrupted by noise, which affects the accuracy of downstream tasks such as surface analysis and precision measurement. This noise reduction process is known as point cloud denoising or filtering. Current deep learning-based methods face challenges, including limited model diversity, insufficient robustness in handling diverse data, and difficulties in ensuring that noisy points converge quickly and effectively to a smooth surface. To address these issues, this paper proposes a new model called KANFilter. KANFilter consists of multiple KANModules, each of which is composed of a KANBlock and a decoder to simulate the real denoising process in an integrated manner. Additionally, a new denoising target is designed to capture the relationships among intermediate filtering results during the training process. Experimental results show that this strategy significantly improves the quality of filtering results, achieving a maximum improvement of 39.3% in the CD (Chamfer Distance) metric at a 10K resolution compared to previous algorithms, and a 25% improvement in convergence speed relative to conventional algorithms.The source code can be found at: https://github.com/zhouxyly/KANFilter