R3DS: Reality-linked 3D Scenes for Panoramic Scene Understanding

Qirui Wu, Sonia Raychaudhuri, Daniel Ritchie, Manolis Savva, Angel X Chang

Keywords: synthetic 3D scenes, dataset and benchmark, panoramic scene understanding

TL;DR: We introduce the Reality-linked 3D Scenes (R3DS) dataset of synthetic 3D scenes mirroring the real-world scene arrangements that offers a challenging benchmark for future work on panoramic scene understanding.

Abstract: 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.

Submission Number: 7