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Emerging 3D geometric foundation models, such as DUSt3R, offer a promising approach for in-the-wild 3D vision tasks. However, due to the high-dimensional nature of the problem space and scarcity of high-quality 3D data, these pre-trained models still struggle to generalize to many challenging circumstances, such as limited view overlap or low lighting. To address this, we propose LoRA3D, an efficient self-calibration pipeline to \(\textit{specialize}\) the pre-trained models to target scenes using their own multi-view predictions. Taking sparse RGB images as input, we leverage robust optimization techniques to refine multi-view predictions and align them into a global coordinate frame. In particular, we incorporate prediction confidence into the geometric optimization process, automatically re-weighting the confidence to better reflect point estimation accuracy. We use the calibrated confidence to generate high-quality pseudo labels for the calibrating views and use low-rank adaptation (LoRA) to fine-tune the models on the pseudo-labeled data. Our method does not require any external priors or manual labels. It completes the self-calibration process on a \(\textbf{single standard GPU within just 5 minutes}\). Each low-rank adapter requires only \(\textbf{18MB}\) of storage. We evaluated our method on \(\textbf{more than 160 scenes}\) from the Replica, TUM and Waymo Open datasets, achieving up to \(\textbf{88% performance improvement}\) on 3D reconstruction, multi-view pose estimation and novel-view rendering.