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Apr, 2023
通过自监督学习从无标签RGB-D视频中学习物体分割
Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos
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Shiyang Lu, Yunfu Deng, Abdeslam Boularias, Kostas Bekris
TL;DR
该论文提出了一个自超视自学的物体分割系统,其训练过程利用了点云的超分割结果,利用图匹配算法和点云配准结合检测出3D假标签上的再现物体模式并生成2D掩码。实验证明,该方法在真实和合成的视频数据集上的表现优于现有的无监督方法。
Abstract
This work proposes a
self-supervised learning system
for
segmenting rigid objects
in RGB images. The proposed pipeline is trained on unlabeled RGB-D videos of static objects, which can be captured with a camera c
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