Increasing the depth of Convolutional Neural Networks (CNNs) has been recognized to provide better generalization performance. However, in the case of 3D CNNs, stacking layers increases the number of learnable parameters linearly, making it more prone to learn redundant features. In this paper, we propose a novel 3D CNN structure that learns shared 2D triplanar features viewed from the three orthogonal planes, which we term S3PNet. Due to the reduced dimension of the convolutions, the proposed S3PNet is able to learn 3D representations with substantially fewer learnable parameters. Experimental evaluations show that the combination of 2D representations on the different orthogonal views learned through the S3PNet is sufficient and effective for 3D representation, with the results outperforming current methods based on fully 3D CNNs. We support this with extensive evaluations on widely used 3D data sources in computer vision: CAD models, LiDAR point clouds, RGB-D images, and 3D Computed Tomography scans. Experiments further demonstrate that S3PNet has better generalization capability for smaller training sets, and learns more of kernels with less redundancy compared to kernels learned from 3D CNNs.

卷积神经网络（CNN）的深度不断增加，可以提供更好的泛化性能。但是，在3D CNN的情况下，堆叠层会线性增加可学习参数的数量，从而更易于学习冗余特征。在本文中，我们提出了一种新颖的3D CNN结构，该结构可学习从三个正交平面（称为S3PNet）观察到的共享2D三边形特征。由于卷积的尺寸减小，因此所提出的S3PNet能够以明显更少的可学习参数学习3D表示。实验评估表明，通过S3PNet学习的不同正交视图上的2D表示形式相结合对于3D表示是足够有效的，其结果优于基于完全3D CNN的当前方法。我们通过对计算机视觉中广泛使用的3D数据源进行广泛评估来支持此工作：CAD模型，LiDAR点云，RGB-D图像和3D计算机断层扫描。实验进一步证明，与从3D CNN中学习的内核相比，S3PNet对较小的训练集具有更好的泛化能力，并且以更少的冗余学习了更多的内核。