Rel3D

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 largescale, 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.