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基于邻域点聚合和全连接残差特征的分片隐式曲面学习网络

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针对形状全局表面隐式场学习的点云曲面重建中通常缺乏形状表面局部细节的问题,采用基于面片的分片表示并训练三维形状的局部符号距离场,提出一种基于邻域点聚合和全连接残差特征的分片隐式曲面学习网络。以离散点云数据为输入,首先采用最远点采样策略得到表征形状球形面片的初始中心位置和半径;然后分别计算每个面片内各采样点相对面片中心的偏移值,并将其作为采样点的相对位置,其中,处于重叠区域内的面片采样点具有多个相对位置;在神经网络编码器中,利用邻域点聚合模块将各相对坐标编码为256维隐向量,以提取相邻采样点的特征信息,并对每个形状面片中的采样点隐向量进行最大化操作,以表征该面片的隐特征;在神经网络解码器中,利用多层全连接残差特征解码模块进行解码,得到各采样点相对位置的形状符号距离值,而对位于不同面片重叠区域内采样点的符号距离值进行加权求和,得到其相应的符号距离值;最后利用Marching Cube算法提取采样点符号距离值的零等值面,得到三维形状网格模型。在ShapeNet,ABC和Famous这3个数据集上的实验结果表明,所提网络在有效地重建形状整体结构的同时,能很好地保持其形状表面细节;在ShapeNet数据集上,IoU指标为83。9,CD指标为0。032,取得了比DeepSDF、占用网络和卷积占用网络更好的重建结果。
Patch-Based Implicit Surface Learning Network by Combining Neighborhood Point Grouping and Fully Connected Residual Feature
Due to its lack of surface details of the underlying point cloud shape for implicit field learning,a novel patch-based surface learning network based on neighborhood point grouping and fully connected residual feature is proposed which adopts a patch-based representation for training the local sign distance field of 3D shapes.With the discrete point cloud data as input,the initial center position and radius of spherical patch rep-resenting 3D shape are obtained by applying the farthest point sampling strategy;then,the relative positions of sampling points located in each surface patch relative to its patch center can be calculated,whilst the sampling points located in the overlapping regions always exist several relative positions;for the network encoder,each relative position will be encoded into 256 dimensional latent vector using the neighboring point grouping layer,and the latent vectors of sampling points located in each surface patch are maximized to represent their latent features;for the network decoder,a multi-layer fully connected residual feature decoding module is adopted to decode the sign distance value of its relative position for each sampling point,whilst that of the sampling point located in the overlapping regions for different patches can be calculated as a weighted sum of their distance values;finally,the Marching Cube algorithm is applied to extract the zero iso-surface of sign distance values for sampling points to obtain final 3D mesh model.By using ShapeNet,ABC and Famous dataset,experimen-tal results demonstrate that our patch-based point cloud learning network can effectively reconstruct the overall shape structure whilst maintaining its surface details.If testing on ShapeNet datasets,the IoU index is 83.9,and the CD index is 0.032.The reconstruction results by our proposed network is superior to that of DeepSDF,oc-cupancy network,or convolutional occupancy network.

point cloud3D reconstructionsigned distance fieldneighborhood point groupingresidual learning

缪永伟、张跃耀、王海鹏、刘复昌、范然、张旭东

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浙江理工大学计算机科学与技术学院 杭州 310018

杭州师范大学信息科学与技术学院 杭州 311121

浙江树人学院信息科技学院 杭州 310015

点云 三维重建 符号距离场 邻域点聚合 残差学习

2024

计算机辅助设计与图形学学报
中国计算机学会

计算机辅助设计与图形学学报

CSTPCD北大核心
影响因子:0.892
ISSN:1003-9775
年,卷(期):2024.36(10)