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Unsupervised 3D Local Feature Learning by Circle Convolutional Restricted Boltzmann Machine

机译:圆卷积约束玻尔兹曼机的无监督3D局部特征学习

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摘要

Extracting local features from 3D shapes is an important and challenging task that usually requires carefully designed 3D shape descriptors. However, these descriptors are hand-crafted and require intensive human intervention with prior knowledge. To tackle this issue, we propose a novel deep learning model, namely circle convolutional restricted Boltzmann machine (CCRBM), for unsupervised 3D local feature learning. CCRBM is specially designed to learn from raw 3D representations. It effectively overcomes obstacles such as irregular vertex topology, orientation ambiguity on the 3D surface, and rigid or slightly non-rigid transformation invariance in the hierarchical learning of 3D data that cannot be resolved by the existing deep learning models. Specifically, by introducing the novel circle convolution, CCRBM holds a novel ring-like multi-layer structure to learn 3D local features in a structure preserving manner. Circle convolution convolves across 3D local regions via rotating a novel circular sector convolution window in a consistent circular direction. In the process of circle convolution, extra points are sampled in each 3D local region and projected onto the tangent plane of the center of the region. In this way, the projection distances in each sector window are employed to constitute a novel local raw 3D representation called projection distance distribution (PDD). In addition, to eliminate the initial location ambiguity of a sector window, the Fourier transform modulus is used to transform the PDD into the Fourier domain, which is then conveyed to CCRBM. Experiments using the learned local features are conducted on three aspects: global shape retrieval, partial shape retrieval, and shape correspondence. The experimental results show that the learned local features outperform other state-of-the-art 3D shape descriptors.
机译:从3D形状中提取局部特征是一项重要且具有挑战性的任务,通常需要精心设计的3D形状描述符。但是,这些描述符是手工制作的,并且需要具有先验知识的人工干预。为解决此问题,我们提出了一种新颖的深度学习模型,即无约束3D局部特征学习的圆卷积受限玻尔兹曼机(CCRBM)。 CCRBM专为从原始3D表示中学习而设计。它有效地克服了现有的深度学习模型无法解决的障碍,例如不规则的顶点拓扑,3D表面的方向模糊性以及3D数据的分层学习中的刚性或轻微非刚性变换不变性。具体而言,通过引入新颖的圆卷积,CCRBM拥有新颖的环状多层结构,以结构保留的方式学习3D局部特征。圆形卷积通过沿一致的圆形方向旋转新颖的圆形扇形卷积窗口在3D局部区域上卷积。在圆卷积过程中,在每个3D局部区域中采样额外的点,并将其投影到该区域中心的切线平面上。以这种方式,每个扇区窗口中的投影距离被用来构成新颖的本地原始3D表示,称为投影距离分布(PDD)。另外,为了消除扇区窗口的初始位置歧义,使用傅立叶变换模数将PDD变换为傅立叶域,然后将其传递给CCRBM。使用学习到的局部特征进行的实验从三个方面进行:整体形状检索,部分形状检索和形状对应。实验结果表明,学习的局部特征优于其他最新的3D形状描述符。

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