LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation models

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 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 fine-tune the models using low-rank adaptation (LoRA) on the pseudo-labeled data.Our method does not require any external priors or manual labels. It completes the self-calibration process on a single standard GPU within just 5 minutes.Each low-rank adapter requires only 18MB of storage. We evaluated our method on more than 160 scenes from the Replica, TUM and Waymo Open datasets,achieving up to 88\% performance improvement on 3D reconstruction, multi-view pose estimation and novel-view rendering.For more details, please visit our project page at https://520xyxyzq.github.io/lora3d/.