Existing techniques to encode spatial invariance within deep convolutional
neural networks only model 2D transformation fields. This does not account for
the fact that objects in a 2D space are a projection of 3D ones, and thus they
have limited ability to severe object viewpoint changes. To overcome this
limitation, we introduce a learnable module, cylindrical convolutional networks
(CCNs), that exploit cylindrical representation of a convolutional kernel
defined in the 3D space. CCNs extract a view-specific feature through a
view-specific convolutional kernel to predict object category scores at each
viewpoint. With the view-specific feature, we simultaneously determine
objective category and viewpoints using the proposed sinusoidal soft-argmax
module. Our experiments demonstrate the effectiveness of the cylindrical
convolutional networks on joint object detection and viewpoint estimation.

通过提出可学习的圆柱卷积神经网络并利用空间中定义的卷积核的圆柱形表示，能够提取视点特定的特征来预测每个视点上的物体类别分数，同时使用所提出的 sinusoidal soft-argmax 模块来确定目标类别和视点。实验证明了圆柱形卷积神经网络在联合目标检测和视点估计方面的有效性。