  Understanding spatial relations (e.g., "laptop on table") in visual input is
important for both humans and robots. Existing datasets are insufficient as
they lack large-scale, high-quality 3D ground truth information, which is
critical for learning spatial relations. In this paper, we fill this gap by
constructing Rel3D: the first large-scale, human-annotated dataset for
grounding spatial relations in 3D. Rel3D enables quantifying the effectiveness
of 3D information in predicting spatial relations on large-scale human data.
Moreover, we propose minimally contrastive data collection -- a novel
crowdsourcing method for reducing dataset bias. The 3D scenes in our dataset
come in minimally contrastive pairs: two scenes in a pair are almost identical,
but a spatial relation holds in one and fails in the other. We empirically
validate that minimally contrastive examples can diagnose issues with current
relation detection models as well as lead to sample-efficient training. Code
and data are available at https://github.com/princeton-vl/Rel3D.
