Skip to yearly menu bar Skip to main content


360+x: A Panoptic Multi-modal Scene Understanding Dataset

Hao Chen · Yuqi Hou · Chenyuan Qu · Irene Testini · Xiaohan Hong · Jianbo Jiao

Arch 4A-E Poster #291
[ ] [ Project Page ]
Fri 21 Jun 10:30 a.m. PDT — noon PDT
Oral presentation: Orals 5A Datasets and evaluation
Fri 21 Jun 9 a.m. PDT — 10:30 a.m. PDT


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 audio, location data and textual scene descriptions within each scene captured, presenting comprehensive observation of the world. 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. Extensive experimental analysis reveals the effectiveness of each data modality and perspective in enhancing panoptic scene understanding. We hope the unique dataset could broaden the scope of comprehensive scene understanding and encourage the community to approach these problems from more diverse perspectives.

Live content is unavailable. Log in and register to view live content