  A comprehensive semantic understanding of a scene is important for many
applications - but in what space should diverse semantic information (e.g.,
objects, scene categories, material types, texture, etc.) be grounded and what
should be its structure? Aspiring to have one unified structure that hosts
diverse types of semantics, we follow the Scene Graph paradigm in 3D,
generating a 3D Scene Graph. Given a 3D mesh and registered panoramic images,
we construct a graph that spans the entire building and includes semantics on
objects (e.g., class, material, and other attributes), rooms (e.g., scene
category, volume, etc.) and cameras (e.g., location, etc.), as well as the
relationships among these entities.
  However, this process is prohibitively labor heavy if done manually. To
alleviate this we devise a semi-automatic framework that employs existing
detection methods and enhances them using two main constraints: I. framing of
query images sampled on panoramas to maximize the performance of 2D detectors,
and II. multi-view consistency enforcement across 2D detections that originate
in different camera locations.
