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English(EN) Scalable Adaptation of 3D Geometric Foundation Models via Weak Supervision from Internet Video

新的SAGE框架利用互联网视频监督适应3D模型

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了SAGE框架，该框架旨在利用互联网视频中的弱监督数据来适应3D几何基础模型。这种方法通过利用丰富多样的视频流来解决标注3D数据的稀缺性问题。SAGE采用了一个流程，该流程选择信息丰富的轨迹，使用稀疏几何锚点进行结构指导，并应用密集可微分一致性来施加多视图约束。该框架在7Scenes、TUM-RGBD和Matterport3D等基准测试中，将Chamfer Distance降低了20-42%，并提高了VGGT性能，展示了显著的改进。 AI

影响 通过利用现成的互联网视频数据，实现了3D几何模型更具可扩展性的训练。

排序理由 详细介绍新框架和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的SAGE框架利用互联网视频监督适应3D模型

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详细介绍新框架和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [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 ·

    通过互联网视频的弱监督实现3D几何基础模型的规模化适应

    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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