点云
稳健性(进化)
计算机科学
人工智能
倒角(几何图形)
噪音(视频)
公制(单位)
卷积神经网络
比例(比率)
模棱两可
算法
数学
图像(数学)
几何学
物理
基因
量子力学
经济
生物化学
化学
程序设计语言
运营管理
作者
Yingrui Wu,Mingyang Zhao,Keqiang Li,Weize Quan,Tianqi Yu,Jianfeng Yang,Xiaohong Jia,Dong‐Ming Yan
出处
期刊:Cornell University - arXiv
日期:2023-01-01
标识
DOI:10.48550/arxiv.2312.09154
摘要
This work presents an accurate and robust method for estimating normals from point clouds. In contrast to predecessor approaches that minimize the deviations between the annotated and the predicted normals directly, leading to direction inconsistency, we first propose a new metric termed Chamfer Normal Distance to address this issue. This not only mitigates the challenge but also facilitates network training and substantially enhances the network robustness against noise. Subsequently, we devise an innovative architecture that encompasses Multi-scale Local Feature Aggregation and Hierarchical Geometric Information Fusion. This design empowers the network to capture intricate geometric details more effectively and alleviate the ambiguity in scale selection. Extensive experiments demonstrate that our method achieves the state-of-the-art performance on both synthetic and real-world datasets, particularly in scenarios contaminated by noise. Our implementation is available at https://github.com/YingruiWoo/CMG-Net_Pytorch.
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