Object pose increases intraclass object variance which makes object recognition from 2D images harder. To render a classifier robust to pose variations, most deep neural networks try to eliminate the influence of pose by using large datasets with many poses for each class. Here, we propose a different approach: a class-agnostic object pose transformation network (OPT-Net) can transform an image along 3D yaw and pitch axes to synthesize additional poses continuously. Synthesized images lead to better training of an object classifier. We design a novel eliminate-add structure to explicitly disentangle pose from object identity: first eliminate pose information of the input image and then add target pose information (regularized as continuous variables) to synthesize any target pose. We trained OPT-Net on images of toy vehicles shot on a turntable from the iLab-20M dataset. After training on unbalanced discrete poses (5 classes with 6 poses per object instance, plus 5 classes with only 2 poses), we show that OPT-Net can synthesize balanced continuous new poses along yaw and pitch axes with high quality. Training a ResNet-18 classifier with original plus synthesized poses improves mAP accuracy by 9% overtraining on original poses only. Further, the pre-trained OPT-Net can generalize to new object classes, which we demonstrate on both iLab-20M and RGB-D. We also show that the learned features can generalize to ImageNet.

翻译：使2D 图像中的对象识别更难。 为使分类器坚固, 以产生变异, 多数深神经网络试图通过使用大型数据集来消除外形的影响, 大型数据集在每类中配置很多。 在这里, 我们提出一种不同的方法: 类不可知天体构成变形网络( OPT- Net) 可以将图像转换成 3D 亚乌 和 投方轴, 以持续合成额外的外形。 同步图像可以更好地训练对象分类器。 我们设计了一个新的消除添加结构, 以明确区分对象身份中的外形: 首先消除输入图像的外形信息, 然后添加目标显示成形信息( 常规变形为连续变量) 以合成任何目标的外形。 我们训练了从 iLab-20M 数据集中拍摄的玩具飞行器图像。 在进行关于不平衡的离心器成像的训练后( 5个类为每件6个, 加上仅2个配置的5个类), 我们显示 OW- 网络可以将平衡的连续配置和高品质的外形轴组合。 在 Res18-20 + 组合中训练一个原始的图像中, 我们只能通过 学习 。