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Video2Asset framework constructs persistent 3D street-scene assets from video

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Video2Asset, a novel online framework designed to construct persistent 3D street-scene assets from multi-view video. This system addresses the challenge of accumulating intermittent observations while maintaining object identities and avoiding fragmentation. Video2Asset utilizes occupancy as an intermediate representation, incorporating Historical Carrier Guidance for instance decoding and an identity encoder that fuses 3D features with multi-view appearance. Experiments on datasets like nuScenes show a significant reduction in asset fragmentation, improving temporal identity consistency and overall asset quality. AI

IMPACT Enhances 3D scene reconstruction capabilities, potentially improving applications in autonomous driving and virtual reality.

RANK_REASON The cluster contains a research paper detailing a new framework for 3D asset construction from video. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Video2Asset framework constructs persistent 3D street-scene assets from video

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The cluster contains a research paper detailing a new framework for 3D asset construction from video. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Zihan Zheng, Chunlei Cai, Zhenlong Wu, Linjun Li, Yichen Gong, Xiaoyun Zhang, Qiang Hu ·

    Video2Asset: Online Construction of Persistent 3D Street-Scene Assets

    arXiv:2611.09661v1 Announce Type: new Abstract: Online 3D street-scene asset construction turns multi-view video into a continually updated scene of independently editable objects. The central challenge is to accumulate partial, intermittent observations under motion and occlusio…

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