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arXiv · 2311.17940

Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames

Chao Chen·Mingzhi Zhu·Ankush Pratap Singh·Yu Yan·Felix Juefei-Xu·Chen Feng
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Abstract

Humans are remarkably efficient at forming spatial understanding from just a few visual observations. When browsing real estate or navigating unfamiliar spaces, they intuitively select a small set of views that summarize the spatial layout. Inspired by this ability, we introduce scene summarization, the task of condensing long, continuous scene videos into a compact set of spatially diverse keyframes that facilitate global spatial reasoning. Unlike conventional video summarization-which focuses on user-edited, fragmented clips and often ignores spatial continuity-our goal is to mimic how humans abstract spatial layout from sparse views. We propose SceneSum, a two-stage self-supervised pipeline that first clusters video frames using visual place recognition to promote spatial diversity, then selects representative keyframes from each cluster under resource constraints. When camera trajectories are available, a lightweight supervised loss further refines clustering and selection. Experiments on real and simulated indoor datasets show that SceneSum produces more spatially informative summaries and outperforms existing video summarization baselines.

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Chao Chen, Mingzhi Zhu, Ankush Pratap Singh, Yu Yan, Felix Juefei-Xu, Chen Feng. 2023-11-28. Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames. https://arxiv.org/abs/2311.17940

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