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English(EN) Sinkhorn-CPD: Robust point cloud registration via unbalanced entropic optimal transport

新的Sinkhorn-CPD方法增强了点云配准的鲁棒性

研究人员开发了Sinkhorn-CPD,一种用于点云配准的新方法,它改进了传统的相干点漂移(CPD)算法。通过采用非均衡熵最优传输,Sinkhorn-CPD能够有效处理CPD常见的挑战,如异常值和部分重叠。新方法利用双重Kullback-Leibler惩罚和广义Sinkhorn迭代进行高效计算。实验表明,Sinkhorn-CPD在各种基准测试中均达到了最先进的精度和鲁棒性能。 AI

影响 增强了点云配准的鲁棒性,有望改进机器人和3D重建等应用。

排序理由 该集群包含一篇详细介绍点云配准新方法的学术论文。

在 arXiv cs.CV 阅读 →

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新的Sinkhorn-CPD方法增强了点云配准的鲁棒性

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jin Zhang, Mingyang Zhao, Bing Liu, Xin Jiang ·

    Sinkhorn-CPD:通过非平衡熵最优传输实现鲁棒点云配准

    arXiv:2606.16672v1 Announce Type: new Abstract: Coherent Point Drift (CPD) is widely used for rigid point cloud registration because of its soft correspondences and closed-form parameter updates. However, CPD's target-side marginal constraint forces every observation, including o…

  2. arXiv cs.CV TIER_1 English(EN) · Xin Jiang ·

    Sinkhorn-CPD:通过非平衡熵最优传输实现鲁棒点云配准

    Coherent Point Drift (CPD) is widely used for rigid point cloud registration because of its soft correspondences and closed-form parameter updates. However, CPD's target-side marginal constraint forces every observation, including outliers, to receive exactly unit probability mas…