.png){kind=icon}
返回
Learning Robust Graph-Convolutional Representations for Point Cloud Denoising
.png){kind=icon}
PRE
AI
DOI：10.1109/JSTSP.2020.3047471.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Point clouds are an increasingly relevant geometric data type but they are often corrupted by noise and affected by the presence of outliers. We propose a deep learning method that can simultaneously denoise a point cloud and remove outliers in a single model. The core of the proposed method is a graph-convolutional neural network able to efficiently deal with the irregular domain and the permutation invariance problem typical of point clouds. The network is fully-convolutional and can build complex hierarchies of features by dynamically constructing neighborhood graphs from similarity among the high-dimensional feature representations of the points. The proposed approach outperforms state-of-the-art denoising methods showing robust performance in the challenging setup of high noise levels and in presence of structured noise.
Keyword:
Three-dimensional displays
Noise reduction
Convolution
Feature extraction
Task analysis
Noise measurement
Anomaly detection
Point cloud
denoising
outlier removal
graph neural networks
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.7
论文数:
1.9K
被引数:
1.1W
机构
引用论文
.png){kind=icon}
分享收藏
分享收藏分享收藏分享收藏
分享收藏Direct excitatory opiate effects mediated by non-synaptic actions on rat medial vestibular neurons
PREAI
分享收藏
查看更多内容