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

The paper introduces SAGE, a framework that adapts 3D geometric foundation models using weak supervision from raw Internet videos. SAGE employs a hierarchical mining pipeline to generate informative training trajectories, sparse geometric anchoring via SfM point clouds, and dense differentiable consistency through 3D Gaussian rendering, while also replaying a small amount of supervised data to avoid catastrophic forgetting. Experiments on two different backbones show that SAGE reduces Chamfer Distance on unseen benchmarks by 20–42% and consistently improves performance, demonstrating that Internet video can serve as a scalable source for geometric learning.

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