  We introduce the Reality-linked 3D Scenes (R3DS) dataset of synthetic 3D
scenes mirroring the real-world scene arrangements from Matterport3D panoramas.
Compared to prior work, R3DS has more complete and densely populated scenes
with objects linked to real-world observations in panoramas. R3DS also provides
an object support hierarchy, and matching object sets (e.g., same chairs around
a dining table) for each scene. Overall, R3DS contains 19K objects represented
by 3,784 distinct CAD models from over 100 object categories. We demonstrate
the effectiveness of R3DS on the Panoramic Scene Understanding task. We find
that: 1) training on R3DS enables better generalization; 2) support relation
prediction trained with R3DS improves performance compared to heuristically
calculated support; and 3) R3DS offers a challenging benchmark for future work
on panoramic scene understanding.
