SQA3D: Situated Question Answering in 3D Scenes

Xiaojian Ma2✶     Silong Yong1,3✶     Zilong Zheng1     Qing Li1     Yitao Liang1,4     Song-Chun Zhu1,2,3,4     Siyuan Huang1    

✶ indicates equal contribution

1Beijing Institute for General Artificial Intelligence (BIGAI)
2UCLA
3Tsinghua University   4Peking University

arXiv  | Code  | Slides  

Abstract

We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D). Given a scene context (e.g., 3D scan), SQA3D requires the tested agent to first understand its situation (position, orientation, etc.) in the 3D scene as described by text, then reason about its surrounding environment and answer a question under that situation. Based upon 650 scenes from ScanNet, we provide a dataset centered around 6.8k unique situations, along with 20.4k descriptions and 33.4k diverse reasoning questions for these situations. These questions examine a wide spectrum of reasoning capabilities for an intelligent agent, ranging from spatial relation comprehension to commonsense understanding, navigation, and multi-hop reasoning. SQA3D imposes a significant challenge to current multi-modal especially 3D reasoning models. We evaluate various state-of-the-art approaches and find that the best one only achieves an overall score of 47.20%, while amateur human participants can reach 90.06%. We believe SQA3D could facilitate future embodied AI research with stronger situation understanding and reasoning capability.

SQA data sample

Scene
    Situation

Situation

Question

Note: Click the select dropdown to select a scene and a corresponding situation in the scene. Drag to move your view around.

Bibtex

If you find our project useful, please consider citing us:


@inproceedings{ma2022sqa3d,
    title={SQA3D: Situated Question Answering in 3D Scenes},
    author={Ma, Xiaojian and Yong, Silong and Zheng, Zilong and Li, Qing and Liang, Yitao and Zhu, Song-Chun and Huang, Siyuan},
    booktitle={International Conference on Learning Representations},
    year={2023},
    url={https://openreview.net/forum?id=IDJx97BC38}
}