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Understanding spatial relations (e.g., "laptop on table") in visual input isimportant for both humans and robots. Existing datasets are insufficient asthey lack large-scale, high-quality 3D ground truth information, which iscritical for learning spatial relations. In this paper, we fill this gap byconstructing Rel3D: the first large-scale, human-annotated dataset forgrounding spatial relations in 3D. Rel3D enables quantifying the effectivenessof 3D information in predicting spatial relations on large-scale human data.Moreover, we propose minimally contrastive data collection -- a novelcrowdsourcing method for reducing dataset bias. The 3D scenes in our datasetcome 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 empiricallyvalidate that minimally contrastive examples can diagnose issues with currentrelation detection models as well as lead to sample-efficient training. Codeand data are available at https://github.com/princeton-vl/Rel3D.

Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D