  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.
