  Natural language applied to natural 2D images describes a fundamentally 3D
world. We present the Voxel-informed Language Grounder (VLG), a language
grounding model that leverages 3D geometric information in the form of voxel
maps derived from the visual input using a volumetric reconstruction model. We
show that VLG significantly improves grounding accuracy on SNARE, an object
reference game task. At the time of writing, VLG holds the top place on the
SNARE leaderboard, achieving SOTA results with a 2.0% absolute improvement.
