TL;DR该论文提出了一种基于 OPT-Net 的类别不可知的物体姿态变换网络,可以将图像沿着 3D 偏航和俯仰轴转换以连续地合成额外的姿态,从而提高物体分类器的训练精度。
Abstract
object pose increases interclass 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