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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 3/2020

05.11.2019

DeepIM: Deep Iterative Matching for 6D Pose Estimation

verfasst von: Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, Dieter Fox

Erschienen in: International Journal of Computer Vision | Ausgabe 3/2020

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Abstract

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-of-the-art methods. We furthermore show that DeepIM is able to match previously unseen objects.
Vorheriger Artikel CornerNet: Detecting Objects as Paired Keypoints
Nächster Artikel Differential Scene Flow from Light Field Gradients

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Metadaten
Titel
DeepIM: Deep Iterative Matching for 6D Pose Estimation
verfasst von
Yi Li
Gu Wang
Xiangyang Ji
Yu Xiang
Dieter Fox
Publikationsdatum
05.11.2019
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 3/2020
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-019-01250-9

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