计算机科学
点云
GSM演进的增强数据速率
渲染(计算机图形)
人工智能
模式识别(心理学)
比例(比率)
正常
稳健性(进化)
边缘检测
特征(语言学)
点(几何)
计算机视觉
算法
曲面(拓扑)
数学
图像(数学)
图像处理
生物化学
化学
物理
几何学
量子力学
基因
语言学
哲学
作者
Haoyi Xiu,Xin Liu,Weimin Wang,Kyoung-Sook Kim,M. Matsuoka
标识
DOI:10.1145/3581783.3613762
摘要
Estimating surface normals from 3D point clouds is critical for various applications, including surface reconstruction and rendering. While existing methods for normal estimation perform well in regions where normals change slowly, they tend to fail where normals vary rapidly. To address this issue, we propose a novel approach called MSECNet, which improves estimation in normal varying regions by treating normal variation modeling as an edge detection problem. MSECNet consists of a backbone network and a multi-scale edge conditioning (MSEC) stream. The MSEC stream achieves robust edge detection through multi-scale feature fusion and adaptive edge detection. The detected edges are then combined with the output of the backbone network using the edge conditioning module to produce edge-aware representations. Extensive experiments show that MSECNet outperforms existing methods on both synthetic (PCPNet) and real-world (SceneNN) datasets while running significantly faster. We also conduct various analyses to investigate the contribution of each component in the MSEC stream. Finally, we demonstrate the effectiveness of our approach in surface reconstruction.
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