  We present a dataset of large-scale indoor spaces that provides a variety of
mutually registered modalities from 2D, 2.5D and 3D domains, with
instance-level semantic and geometric annotations. The dataset covers over
6,000m2 and contains over 70,000 RGB images, along with the corresponding
depths, surface normals, semantic annotations, global XYZ images (all in forms
of both regular and 360{\deg} equirectangular images) as well as camera
information. It also includes registered raw and semantically annotated 3D
meshes and point clouds. The dataset enables development of joint and
cross-modal learning models and potentially unsupervised approaches utilizing
the regularities present in large-scale indoor spaces. The dataset is available
here: http://3Dsemantics.stanford.edu/
