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Efficient 6DoF pose estimation for multi-instance objects from a single image
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DOI：10.1016/j.imavis.2025.105882.png)
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Abstract
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Estimating 6 degrees of freedom poses for multiple objects from a single image and making it practical in industry is difficult since several metrics, like accuracy, speed and complexity must be traded. This study adopts a fast bottom-up approach to estimate poses for multi-instance objects in an image simultaneously. We design a convolutional neural network with simple end-to-end training to output 4 feature maps: error mask, semantic mask, center vector map and 6D coordinate map (6DCM). Specifically, 6DCM is capable of providing the rear-side 3D object point clouds information that are originally invisible from the camera's viewpoint. This procedure enriches shape information about target objects which can be used to construct each instance's 2D-3D correspondences for pose parameter estimation. Experimental results show that our proposed bottom-up approach is fast and can process a single image containing 7 objects at 25 frames per second with competitive accuracy to other top-down methods.
Keywords:
6DoF
Object pose estimation
Bottom-up approach
Multi-instances objects
Deep learning neural network
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Detecting Object Surface Keypoints From a Single RGB Image via Deep Learning Network for 6-DoF Pose Estimation.png)
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OAAIShareSaveShareSaveShareSaveREG-Net: Improving 6DoF Object Pose Estimation With 2D Keypoint Long-Short-Range-Aware Registration
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