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.

Strengths: The authors have produced a modestly large 3D scene data set (about 10K scenes) in pairs of positive and negative relationships. The authors thus have taken care to generate a data set that gives as much weight to negative examples as to positive ones. They have also dealt with various language ambiguity issues, as spatial relationships for a given view may be based either on the observer's frame or the object's frame of reference. The authors argue, and demonstrate by a small study, the advantage of 3D data for determining spatial relationships over purely 2D approaches. They also show that their minimally contrastive examples allow learning with increased sample efficiency.

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.