Most deep pose estimation methods need to be trained for specific object
instances or categories. In this work we propose a completely generic deep pose
estimation approach, which does not require the network to have been trained on
relevant categories, nor objects in a category to have a canonical pose. We
believe this is a crucial step to design robotic systems that can interact with
new objects in the wild not belonging to a predefined category. Our main
insight is to dynamically condition pose estimation with a representation of
the 3D shape of the target object. More precisely, we train a Convolutional
Neural Network that takes as input both a test image and a 3D model, and
outputs the relative 3D pose of the object in the input image with respect to
the 3D model. We demonstrate that our method boosts performances for supervised
category pose estimation on standard benchmarks, namely Pascal3D+, ObjectNet3D
and Pix3D, on which we provide results superior to the state of the art. More
importantly, we show that our network trained on everyday man-made objects from
ShapeNet generalizes without any additional training to completely new types of
3D objects by providing results on the LINEMOD dataset as well as on natural
entities such as animals from ImageNet.

我们提出了一种完全通用的深度姿态估计方法，通过动态条件姿态估计和目标物体的 3D 模型表示相结合，能够对不属于预定义类别的自然实体进行训练并将其推广到全新类型的 3D 对象。