IntriAdapt: Generalizable Intrinsic-Guided Test-Time Adaptation for Metric Geometry Estimation

Key points

• In this paper, we instead use camera intrinsics as an output-side geometric verifier.1
• We propose IntriAdapt, a TTA framework that minimizes this closed-form geometric residual.1
• By optimizing a lightweight LoRA adapter on a frozen geometry foundation model, our method aligns the prediction with the specific camera model without depth sensors or external priors.1
• Experiments on ten single-image and seven sequence benchmarks across multiple base models show that IntriAdapt consistently outperforms both unadapted and oracle-intrinsics baselines in metric accuracy and relative geometry.1

Sources (1)

• [1]IntriAdapt: Generalizable Intrinsic-Guided Test-Time Adaptation for Metric Geometry Estimation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 10:22 AM

  Recovering metric 3D geometry from images is fundamentally ill-posed due to perspective projection ambiguity.

  In this paper, we instead use camera intrinsics as an output-side geometric verifier.

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