  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. To study the reason behind the low performance and to further our
understandings of these tasks, we design controlled experiments on both input
data and network designs. Guided by the hindsight from these experiment
results, we propose a simple contrastive learning approach along with other
network modifications to improve the baseline performance. Our approach uses a
self-supervised binary classification network to compare the line drawing
differences between various views of any two similar 3D objects. It enables
deep networks to effectively learn detail-sensitive yet view-invariant line
drawing representations of 3D objects. Experiments show that our method could
significantly increase the baseline performance in SPARE3D, while some popular
self-supervised learning methods cannot.
