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- W2604913409 abstract "To the best of increasing robotic vision in 3D conceptual for recognizing this living world, this paper proposed a 3D recognition system by combining the local feature and global verification technique. To approach this, we modified the state-of-art methods and organized it as a robust hybrid flow. Another contribution to this paper, we release the finest parameters to the Kinect sensor as well as the dataset. In the proposed framework, we expect the pre-process can deal with range filtering, noise reduction, and point cloud refinement. After this, the captured point cloud is more reliable and better to describe the object surface. The Second part is focused on recognition and pose estimation. We here refer two robust methods, SHOT descriptor and Hough Voting, one for the local feature generation and the other contributes to the object alignment. Finally, through the ICP to refine the pose matrix, we remove the false positive while verifying the good instance. Moreover, we design a keypoint selective mechanism after the hypothesis verification stage back into local conception." @default.
- W2604913409 created "2017-04-14" @default.
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- W2604913409 date "2017-01-01" @default.
- W2604913409 modified "2023-09-27" @default.
- W2604913409 title "A 3D Recognition System with Local-Global Collaboration" @default.
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- W2604913409 doi "https://doi.org/10.1007/978-3-319-54427-4_2" @default.
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