PointGrid:用于三维形状理解的深度网络

Truc Le, Y. Duan
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引用次数: 290

摘要

体积网格因其规律性被广泛应用于三维深度学习。然而,使用相对较低阶的局部近似函数,如分段常数函数(占用网格)或分段线性函数(距离场)来近似3D形状意味着它需要一个非常高分辨率的网格来表示更精细的几何细节,这可能会占用内存和计算效率低下。在这项工作中,我们提出了PointGrid,这是一个3D卷积网络,在每个网格单元中包含恒定数量的点,从而允许网络学习高阶局部近似函数,从而更好地表示局部几何形状细节。通过对流行的形状识别基准的实验,PointGrid在分类和分割方面展示了比现有深度学习方法更先进的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PointGrid: A Deep Network for 3D Shape Understanding
Volumetric grid is widely used for 3D deep learning due to its regularity. However the use of relatively lower order local approximation functions such as piece-wise constant function (occupancy grid) or piece-wise linear function (distance field) to approximate 3D shape means that it needs a very high-resolution grid to represent finer geometry details, which could be memory and computationally inefficient. In this work, we propose the PointGrid, a 3D convolutional network that incorporates a constant number of points within each grid cell thus allowing the network to learn higher order local approximation functions that could better represent the local geometry shape details. With experiments on popular shape recognition benchmarks, PointGrid demonstrates state-of-the-art performance over existing deep learning methods on both classification and segmentation.
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