Domain Transfer for 3D Pose Estimation from Color Images without Manual Annotations
LEPETIT, Vincent
Melting the frontiers between Light, Shape and Matter [MANAO]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Melting the frontiers between Light, Shape and Matter [MANAO]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
LEPETIT, Vincent
Melting the frontiers between Light, Shape and Matter [MANAO]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
< Réduire
Melting the frontiers between Light, Shape and Matter [MANAO]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Langue
en
Communication dans un congrès avec actes
Ce document a été publié dans
ACCV, 2018, Perth.
Résumé en anglais
We introduce a novel learning method for 3D pose estimation from color images. While acquiring annotations for color images is a difficult task, our approach circumvents this problem by learning a mapping from paired color ...Lire la suite >
We introduce a novel learning method for 3D pose estimation from color images. While acquiring annotations for color images is a difficult task, our approach circumvents this problem by learning a mapping from paired color and depth images captured with an RGB-D camera. We jointly learn the pose from synthetic depth images that are easy to generate, and learn to align these synthetic depth images with the real depth images. We show our approach for the task of 3D hand pose estimation and 3D object pose estimation, both from color images only. Our method achieves performances comparable to state-of-the-art methods on popular benchmark datasets, without requiring any annotations for the color images.< Réduire
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