FocalPose++: Focal Length and Object Pose Estimation via Render and Compare
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913572935696384 |
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| author | Cífka, Martin Ponimatkin, Georgy Labbé, Yann Russell, Bryan Aubry, Mathieu Petrik, Vladimir Sivic, Josef |
| author_facet | Cífka, Martin Ponimatkin, Georgy Labbé, Yann Russell, Bryan Aubry, Mathieu Petrik, Vladimir Sivic, Josef |
| contents | We introduce FocalPose++, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are threefold. First, we derive a focal length update rule that extends an existing state-of-the-art render-and-compare 6D pose estimator to address the joint estimation task. Second, we investigate several different loss functions for jointly estimating the object pose and focal length. We find that a combination of direct focal length regression with a reprojection loss disentangling the contribution of translation, rotation, and focal length leads to improved results. Third, we explore the effect of different synthetic training data on the performance of our method. Specifically, we investigate different distributions used for sampling object's 6D pose and camera's focal length when rendering the synthetic images, and show that parametric distribution fitted on real training data works the best. We show results on three challenging benchmark datasets that depict known 3D models in uncontrolled settings. We demonstrate that our focal length and 6D pose estimates have lower error than the existing state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_02985 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FocalPose++: Focal Length and Object Pose Estimation via Render and Compare Cífka, Martin Ponimatkin, Georgy Labbé, Yann Russell, Bryan Aubry, Mathieu Petrik, Vladimir Sivic, Josef Computer Vision and Pattern Recognition We introduce FocalPose++, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are threefold. First, we derive a focal length update rule that extends an existing state-of-the-art render-and-compare 6D pose estimator to address the joint estimation task. Second, we investigate several different loss functions for jointly estimating the object pose and focal length. We find that a combination of direct focal length regression with a reprojection loss disentangling the contribution of translation, rotation, and focal length leads to improved results. Third, we explore the effect of different synthetic training data on the performance of our method. Specifically, we investigate different distributions used for sampling object's 6D pose and camera's focal length when rendering the synthetic images, and show that parametric distribution fitted on real training data works the best. We show results on three challenging benchmark datasets that depict known 3D models in uncontrolled settings. We demonstrate that our focal length and 6D pose estimates have lower error than the existing state-of-the-art methods. |
| title | FocalPose++: Focal Length and Object Pose Estimation via Render and Compare |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.02985 |