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L6DNet: Light 6 DoF Network for Robust and Precise Object Pose Estimation with Small Datasets

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Academic year: 2021

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Fig. 1: To estimate the pose of an object we propose to use a single RGB-D image (a) to predict the position of a set of sparse 3D keypoints (shown as spheres in b) in the camera coordinate system, which are then registered in 3D with corresponding points
Fig. 2: Overview of our pipeline solution: first (a), patches are extracted from an RGB-D image and classified as object or background
Fig. 3: Examples of probability maps for the cat, driller and can from the LineMod dataset
Fig. 5: Example of predicted 3D points. From left to right: the cropped input image, the cluster of 3D votes V ˆ j where each color corresponds to votes for a single point, the aggregated points Y˜ j obtained by mean-shift (best seen in color)
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