360+x: A Panoptic Multi-modal Scene Understanding Dataset
<|reference_start|>360+x: A Panoptic Multi-modal Scene Understanding Dataset: Human perception of the world is shaped by a multitude of viewpoints and modalities. While many existing datasets focus on scene understanding from a certain perspective (e.g. egocentric or third-person views), our dataset offers a panoptic perspective (i.e. multiple viewpoints with multiple data modalities). Specifically, we encapsulate third-person panoramic and front views, as well as egocentric monocular/binocular views with rich modalities including video, multi-channel audio, directional binaural delay, location data and textual scene descriptions within each scene captured, presenting comprehensive observation of the world. Figure 1 offers a glimpse of all 28 scene categories of our 360+x dataset. To the best of our knowledge, this is the first database that covers multiple viewpoints with multiple data modalities to mimic how daily information is accessed in the real world. Through our benchmark analysis, we presented 5 different scene understanding tasks on the proposed 360+x dataset to evaluate the impact and benefit of each data modality and perspective in panoptic scene understanding. We hope this unique dataset could broaden the scope of comprehensive scene understanding and encourage the community to approach these problems from more diverse perspectives.<|reference_end|>
@article{chen2024360+x:,
    title={360+x: A Panoptic Multi-modal Scene Understanding Dataset},
    author={Hao Chen, Yuqi Hou, Chenyuan Qu, Irene Testini, Xiaohan Hong, Jianbo
  Jiao},
    journal={The IEEE/CVF Computer Vision and Pattern Recognition Conference
  (CVPR) 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00989},
    primaryClass={cs.CV cs.AI cs.MM cs.SD eess.AS}
}