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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.16471 |
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| _version_ | 1866929631463997440 |
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| author | Sapoutzoglou, Panagiotis Giapitzakis, Georgios Floros, Georgios Terzakis, George Pateraki, Maria |
| author_facet | Sapoutzoglou, Panagiotis Giapitzakis, Georgios Floros, Georgios Terzakis, George Pateraki, Maria |
| contents | We propose a generic procedure for assessing 6D object pose estimates. Our approach relies on the evaluation of discrepancies in the geometry of the observed object, in particular its respective estimated back-projection in 3D, against a putative functional shape representation comprising mixtures of Gaussian Processes, that act as a template. Each Gaussian Process is trained to yield a fragment of the object's surface in a radial fashion with respect to designated reference points. We further define a pose confidence measure as the average probability of pixel back-projections in the Gaussian mixture. The goal of our experiments is two-fold. a) We demonstrate that our functional representation is sufficiently accurate as a shape template on which the probability of back-projected object points can be evaluated, and, b) we show that the resulting confidence scores based on these probabilities are indeed a consistent quality measure of pose. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16471 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images Sapoutzoglou, Panagiotis Giapitzakis, Georgios Floros, Georgios Terzakis, George Pateraki, Maria Computer Vision and Pattern Recognition We propose a generic procedure for assessing 6D object pose estimates. Our approach relies on the evaluation of discrepancies in the geometry of the observed object, in particular its respective estimated back-projection in 3D, against a putative functional shape representation comprising mixtures of Gaussian Processes, that act as a template. Each Gaussian Process is trained to yield a fragment of the object's surface in a radial fashion with respect to designated reference points. We further define a pose confidence measure as the average probability of pixel back-projections in the Gaussian mixture. The goal of our experiments is two-fold. a) We demonstrate that our functional representation is sufficiently accurate as a shape template on which the probability of back-projected object points can be evaluated, and, b) we show that the resulting confidence scores based on these probabilities are indeed a consistent quality measure of pose. |
| title | COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.16471 |