Learning a Category-level Object Pose Estimator without Pose Annotations
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913305679888384 |
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| author | Tian, Fengrui Liu, Yaoyao Kortylewski, Adam Duan, Yueqi Du, Shaoyi Yuille, Alan Wang, Angtian |
| author_facet | Tian, Fengrui Liu, Yaoyao Kortylewski, Adam Duan, Yueqi Du, Shaoyi Yuille, Alan Wang, Angtian |
| contents | 3D object pose estimation is a challenging task. Previous works always require thousands of object images with annotated poses for learning the 3D pose correspondence, which is laborious and time-consuming for labeling. In this paper, we propose to learn a category-level 3D object pose estimator without pose annotations. Instead of using manually annotated images, we leverage diffusion models (e.g., Zero-1-to-3) to generate a set of images under controlled pose differences and propose to learn our object pose estimator with those images. Directly using the original diffusion model leads to images with noisy poses and artifacts. To tackle this issue, firstly, we exploit an image encoder, which is learned from a specially designed contrastive pose learning, to filter the unreasonable details and extract image feature maps. Additionally, we propose a novel learning strategy that allows the model to learn object poses from those generated image sets without knowing the alignment of their canonical poses. Experimental results show that our method has the capability of category-level object pose estimation from a single shot setting (as pose definition), while significantly outperforming other state-of-the-art methods on the few-shot category-level object pose estimation benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05626 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning a Category-level Object Pose Estimator without Pose Annotations Tian, Fengrui Liu, Yaoyao Kortylewski, Adam Duan, Yueqi Du, Shaoyi Yuille, Alan Wang, Angtian Computer Vision and Pattern Recognition 3D object pose estimation is a challenging task. Previous works always require thousands of object images with annotated poses for learning the 3D pose correspondence, which is laborious and time-consuming for labeling. In this paper, we propose to learn a category-level 3D object pose estimator without pose annotations. Instead of using manually annotated images, we leverage diffusion models (e.g., Zero-1-to-3) to generate a set of images under controlled pose differences and propose to learn our object pose estimator with those images. Directly using the original diffusion model leads to images with noisy poses and artifacts. To tackle this issue, firstly, we exploit an image encoder, which is learned from a specially designed contrastive pose learning, to filter the unreasonable details and extract image feature maps. Additionally, we propose a novel learning strategy that allows the model to learn object poses from those generated image sets without knowing the alignment of their canonical poses. Experimental results show that our method has the capability of category-level object pose estimation from a single shot setting (as pose definition), while significantly outperforming other state-of-the-art methods on the few-shot category-level object pose estimation benchmarks. |
| title | Learning a Category-level Object Pose Estimator without Pose Annotations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.05626 |