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.

提出一种半监督三维重建框架 SSP3D，引入基于注意力引导的原型形状先验模块、鉴别器引导模块和正则化器来实现更好的形状生成，在 ShapeNet 基准测试中，在不同标注率下，性能均明显优越，并且对于少量有标注数据的新类别也能够取得很好的成果。