Spatial reasoning on multi-view line drawings by state-of-the-art supervised deep networks is recently shown with puzzling low performances on the SPARE3D dataset. Based on the fact that self-supervised learning is helpful when a large number of data are available, we propose two self-supervised learning approaches to improve the baseline performance for view consistency reasoning and camera pose reasoning tasks on the SPARE3D dataset. For the first task, we use a self-supervised binary classification network to contrast the line drawing differences between various views of any two similar 3D objects, enabling the trained networks to effectively learn detail-sensitive yet view-invariant line drawing representations of 3D objects. For the second type of task, we propose a self-supervised multi-class classification framework to train a model to select the correct corresponding view from which a line drawing is rendered. Our method is even helpful for the downstream tasks with unseen camera poses. Experiments show that our method could significantly increase the baseline performance in SPARE3D, while some popular self-supervised learning methods cannot.

翻译：SPARE3D 数据集的低性能令人费解。基于以下事实:当有大量数据可用时,自我监督的学习很有帮助,我们建议了两种自监督的学习方法来改进基线性能,以便查看一致性的推理,相机在SPARE3D 数据集中提出推理任务。对于第一项任务,我们使用一个自监督的二进制分类网络来比较任何两个类似的 3D 对象的不同观点之间的线性能差异,使受过培训的网络能够有效地学习3D 对象的详细敏感但不易查看的线性图示。对于第二类任务,我们建议了一个自监督的多级分类框架来训练一个模型来选择正确的观点,从而从中绘制线。我们的方法甚至有助于使用看不见的相机的下游任务。实验表明,我们的方法可以大大提高SPARE3D 的基线性能,而一些受欢迎的自我监督的学习方法则无法做到。