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Mar, 2022
REGTR: 基于Transformer的端到端点云对应
REGTR: End-to-end Point Cloud Correspondences with Transformers
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Zi Jian Yew, Gim Hee Lee
TL;DR
该论文提出了一种新的端到端方法来直接预测配准操作中的对应点,利用transformer网络结构中的自注意力和交叉注意力机制来替代传统的特征匹配和RANSAC算法,该方法在3DMatch和ModelNet基准上均取得了最先进的成绩。
Abstract
Despite recent success in incorporating learning into
point cloud registration
, many works focus on
learning feature descriptors
and continue to rely on nearest-neighbor feature matching and outlier filtering thr
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