3D shape models are becoming widely available and easier to capture, making
available 3D information crucial for progress in object classification. Current
state-of-the-art methods rely on CNNs to address this problem. Recently, we
witness two types of CNNs being developed: CNNs based upon volumetric
representations versus CNNs based upon multi-view representations. Empirical
results from these two types of CNNs exhibit a large gap, indicating that
existing volumetric CNN architectures and approaches are unable to fully
exploit the power of 3D representations. In this paper, we aim to improve both
volumetric CNNs and multi-view CNNs according to extensive analysis of existing
approaches. To this end, we introduce two distinct network architectures of
volumetric CNNs. In addition, we examine multi-view CNNs, where we introduce
multi-resolution filtering in 3D. Overall, we are able to outperform current
state-of-the-art methods for both volumetric CNNs and multi-view CNNs. We
provide extensive experiments designed to evaluate underlying design choices,
thus providing a better understanding of the space of methods available for
object classification on 3D data.

本文围绕三维信息在物体分类中的重要性，通过改进现有的立体卷积神经网络和多视卷积神经网络结构，引入了基于多分辨率过滤的三维多视卷积神经网络，取得了比现有技术更好的分类效果，并提供了相关实验结果和设计思路分析。