JOURNAL ARTICLE

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

Abstract

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. 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.

Keywords:
Panopticon Modal Computer science Artificial intelligence Computer graphics (images) Computer vision Sociology Materials science

Metrics

4
Cited By
2.12
FWCI (Field Weighted Citation Impact)
34
Refs
0.80
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Multimodal Machine Learning Applications
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Video Analysis and Summarization
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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