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Submission time (UTC) | April 24, 2022, 10:30 p.m. | ||||||||||
User | lahav | ||||||||||
Task | 6D localization of seen objects | ||||||||||
Dataset | YCB-V | ||||||||||
Training model type | Default | ||||||||||
Training image type | Synthetic + real | ||||||||||
Description | Single view. Dataset splits: train_real, train_synt | ||||||||||
Evaluation scores |
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User | lahav |
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Publication | Coupled Iterative Refinement for 6D Multi-Object Pose Estimation, CVPR 2022 |
Implementation | https://github.com/princeton- vl/Coupled-Iterative-Refinement |
Training image modalities | RGB |
Test image modalities | RGB |
Description | We propose a new approach to 6D object pose estimation which consists of an end-to-end differentiable architecture that makes use of geometric knowledge. Our approach it- eratively refines both pose and correspondence in a tightly coupled manner, allowing us to dynamically remove outliers to improve accuracy. We use a novel differentiable layer to perform pose refinement by solving an optimization problem we refer to as Bidirectional Depth-Augmented Perspective- N-Point (BD-PnP). |
Computer specifications | 2 RTX-3090s |