Geometric Foundation Model Distillation for Efficient Lunar 3D Reconstruction

Large 3D foundation models such as MASt3R achieve state-of-the-art stereo reconstruction but are computationally demanding fordeployment under strict hardware constraints—a critical limitation indomains such as planetary exploration, where onboard computing isseverely restricted. We study how far such models can be compressedthrough knowledge distillation, using lunar stereo reconstruction as achallenging and practically relevant case study. Starting from a 688M-parameter MASt3R teacher fine-tuned on lunar imagery, we distill itsdense geometric predictions into a family of lightweight students span-ning different encoder types (CNN vs. ViT), decoder widths and depths,and training strategies. To bridge the dimensional mismatch betweenteacher and student, we propose a structured SVD-based initializationthat projects the teacher’s decoder weights into the student’s smallerlatent space, yielding a warm start that significantly improves conver-gence and final performance. Based on our results on lunar data, wecan obtain a distilled student that retains most of teacher’s reconstruc-tion accuracy while reducing the model size up to 7×, and even out-performs a baseline trained directly with sparse ground-truth annota-tions. Beyond compression, our study highlights both principles andpractical insights for distilling geometric foundation models: a convo-lutional encoder underperforms transformer-based alternatives (thoughpretraining availability remains a confounding factor), preserving en-coder capacity is more critical than maintaining a large decoder, feature-level distillation consistently outperforms output-only supervision, andSVD-based initialization improves optimisation stability. These findingsprovide practical guidelines for deploying 3D reconstruction models inresource-constrained environments. Code is publicly available at https://clementinegrethen.github.io/publications/ECCV.html.