基于原型形状先验的半监督单视图三维重建

Zhen Xing, Hengduo Li, Zuxuan Wu, Yu-Gang Jiang
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引用次数: 10

摘要

现有的单视图三维重建方法的性能严重依赖于大规模的三维注释。但是,收集这样的注释既繁琐又昂贵。半监督学习可以作为一种替代方法来减少对手动标签的需求,但在3D重建中仍未被探索。受最近半监督图像分类任务的成功启发,我们提出了一种用于3D重建的半监督框架SSP3D。特别地,我们引入了一个注意力引导的原型形状先验模块来指导真实物体的重建。我们进一步引入了一个判别器引导模块来激励更好的形状生成,以及一个正则化器来容忍有噪声的训练样本。在ShapeNet基准测试中,在不同的标注比率(即1%、5%、10%和20%)下,本文提出的方法明显优于先前的监督方法。此外,在10%的标记率下,我们的方法在转移到真实的Pix3D数据集时也表现良好。我们还证明了我们的方法可以用很少的新监督数据转移到新的类别。在流行的ShapeNet数据集上的实验表明,我们的方法比零射击基线优于12%以上,我们还进行了严格的消融和分析来验证我们的方法。
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Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors
The performance of existing single-view 3D reconstruction methods heavily relies on large-scale 3D annotations. However, such annotations are tedious and expensive to collect. Semi-supervised learning serves as an alternative way to mitigate the need for manual labels, but remains unexplored in 3D reconstruction. Inspired by the recent success of semi-supervised image classification tasks, we propose SSP3D, a semi-supervised framework for 3D reconstruction. In particular, we introduce an attention-guided prototype shape prior module for guiding realistic object reconstruction. We further introduce a discriminator-guided module to incentivize better shape generation, as well as a regularizer to tolerate noisy training samples. On the ShapeNet benchmark, the proposed approach outperforms previous supervised methods by clear margins under various labeling ratios, (i.e., 1%, 5% , 10% and 20%). Moreover, our approach also performs well when transferring to real-world Pix3D datasets under labeling ratios of 10%. We also demonstrate our method could transfer to novel categories with few novel supervised data. Experiments on the popular ShapeNet dataset show that our method outperforms the zero-shot baseline by over 12% and we also perform rigorous ablations and analysis to validate our approach.
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