TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance

Author:

Wen HongtaoORCID,Yan JianhangORCID,Peng WanliORCID,Sun YiORCID

Publisher

Springer Nature Switzerland

Reference34 articles.

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2. Breyer, M., Chung, J.J., Ott, L., Siegwart, R., Nieto, J.: Volumetric grasping network: real-time 6 dof grasp detection in clutter. arXiv preprint arXiv:2101.01132 (2021)

3. Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. Technical Report arXiv:1512.03012 [cs.GR]. Stanford University – Princeton University – Toyota Technological Institute at Chicago (2015)

4. Chen, D., Li, J., Wang, Z., Xu, K.: Learning canonical shape space for category-level 6D object pose and size estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11973–11982 (2020)

5. Chen, W., Jia, X., Chang, H.J., Duan, J., Shen, L., Leonardis, A.: Fs-net: fast shape-based network for category-level 6D object pose estimation with decoupled rotation mechanism. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1581–1590 (2021)

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