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New SAGE framework adapts 3D models using internet video supervision

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SAGE, a framework designed to adapt 3D geometric foundation models using weakly supervised data from internet videos. This approach addresses the scarcity of annotated 3D data by leveraging abundant, diverse video streams. SAGE employs a pipeline that selects informative trajectories, uses sparse geometric anchoring for structural guidance, and applies dense differentiable consistency for multi-view constraints. The framework has demonstrated significant improvements, reducing Chamfer Distance by 20-42% on benchmarks like 7Scenes, TUM-RGBD, and Matterport3D, and enhancing VGGT performance. AI

IMPACT Enables more scalable training of 3D geometric models by leveraging readily available internet video data.

RANK_REASON Academic paper detailing a new framework and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New SAGE framework adapts 3D models using internet video supervision

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Academic paper detailing a new framework and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zihui Gao, Ke Liu, Donny Y. Chen, Duochao Shi, Guosheng Lin, Jia-Wang Bian, Hao Chen, Chunhua Shen ·

    Scalable Adaptation of 3D Geometric Foundation Models via Weak Supervision from Internet Video

    arXiv:2602.07891v2 Announce Type: replace-cross Abstract: Geometric foundation models show promise in 3D reconstruction. However, their progress is severely constrained by the scarcity of diverse, large-scale 3D annotations. While static-scene Internet videos are abundant and div…

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