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1 year ago Thank you very much for your reply and help, which is of great help to me！
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---- Replied Message ---- | From | @.> | | Date | 08/15/2023 16:58 | | To | @.> | | Cc | @.>@.> | | Subject | Re: [llada60/ICP_Learning-based-6D-Pose-Estimation] May I ask if there are any supporting articles for this code to refer to？ (Issue #1) |
ICP-based: [1] Horn B K P. Closed-form solution of absolute orientation using unit quaternions[J]. Josa a, 1987, 4(4): 629-642. [2] Umeyama S. Least-squares estimation of transformation parameters between two point patterns[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1991, 13(04): 376-380.
Shape-agnostic Learning-based: [1] Zhou Y, Barnes C, Lu J, et al. On the continuity of rotation representations in neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 5745-5753. [2] Wang C, Xu D, Zhu Y, et al. Densefusion: 6d object pose estimation by iterative dense fusion[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 3343-3352. [3] Xiang Y, Schmidt T, Narayanan V, et al. Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes[J]. arXiv preprint arXiv:1711.00199, 2017. [4] Levinson J, Esteves C, Chen K, et al. An analysis of svd for deep rotation estimation[J]. Advances in Neural Information Processing Systems, 2020, 33: 22554-22565.
Wish it will be helpful to you.
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ICP-based: [1] Horn B K P. Closed-form solution of absolute orientation using unit quaternions[J]. Josa a, 1987, 4(4): 629-642. [2] Umeyama S. Least-squares estimation of transformation parameters between two point patterns[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1991, 13(04): 376-380.
Shape-agnostic Learning-based: [1] Zhou Y, Barnes C, Lu J, et al. On the continuity of rotation representations in neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 5745-5753. [2] Wang C, Xu D, Zhu Y, et al. Densefusion: 6d object pose estimation by iterative dense fusion[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 3343-3352. [3] Xiang Y, Schmidt T, Narayanan V, et al. Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes[J]. arXiv preprint arXiv:1711.00199, 2017. [4] Levinson J, Esteves C, Chen K, et al. An analysis of svd for deep rotation estimation[J]. Advances in Neural Information Processing Systems, 2020, 33: 22554-22565.
Wish it will be helpful to you.