Estimating 6D poses of objects from images is an important problem in various applications such as robot manipulation and virtual reality. While direct regression of images to object poses has limited accuracy, matching rendered images of an object against the input image can produce accurate results. In this work, we propose a novel deep neural network for 6D pose matching named DeepIM. Given an initial pose estimation, our network is able to iteratively refine the pose by matching the rendered image against the observed image. The network is trained to predict a relative pose transformation using a disentangled representation of 3D location and 3D orientation and an iterative training process. Experiments on two commonly used benchmarks for 6D pose estimation demonstrate that DeepIM achieves large improvements over state-ofthe-art methods. We furthermore show that DeepIM is able to match previously unseen objects.
DeepIM REF uses a FlowNet backbone architecture to predict a relative SE(3) transformation to match the colored rendered image of an object using initial pose to the observed image.
DeepIM: Deep Iterative Matching for 6D Pose Estimation
International Journal of Computer Vision
International Journal of Computer Vision
Computer Science