📢✅MP-SfM: Monocular Surface Priors for Robust Structure-from-Motion
MP-SfM redefines classical Structure-from-Motion by tightly integrating monocular depth and surface normal priors into incremental SfM, enabling robust 3D reconstruction from sparse, unstructured image collections.
Key Highlights:
✅Monocular Depth Surface Normal Fusion — Augments traditional SfM with priors from off-the-shelf deep networks (e.g., Metric3D-v2, DSINE), eliminating the need for three-view overlap.
✅Two-View Track Reconstruction — Enables multi-view 3D estimation from as few as two views by lifting single-view features to 3D, overcoming the three-view constraint of conventional pipelines like COLMAP.
✅Depth-Constrained Bundle Adjustment — Jointly optimizes poses, 3D points, and depth maps with robust loss functions and normal-based integration, improving accuracy even under noisy priors.
✅Principled Uncertainty Propagation — Incorporates predictive uncertainty from monocular models, allowing dynamic weighting in optimization and future-proofing against model improvements.
✅Depth Consistency Check — Rejects symmetry-induced misregistrations via dense geometric verification, reasoning over occlusion and free space across views.
✅High Robustness in Adverse Scenarios — Outperforms COLMAP, GLOMAP, StudioSfM, and MASt3R-SfM on ETH3D, SMERF, RealEstate10k, and Tanks & Temples, particularly in low-parallax and low-overlap conditions.
✅Dense and Sparse Feature Agnostic — Works with both sparse SuperPoint LightGlue and dense RoMa/MASt3R tracks, demonstrating adaptability across matching paradigms.
✅Efficient Alternating Optimization — Avoids Schur complement by splitting joint refinement into image-wise and structure-wise blocks, balancing accuracy and scalability.
✅Ablated and Benchmarked — Extensively validated through 50 controlled experiments across triplet, minimal-overlap, and full-scene reconstructions; ablation studies confirm each module’s contribution.
✅Open-Source & Modular — Code available at
github.com/cvg/mpsfm, designed to plug into existing SfM workflows with minimal tuning.
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Paper:
arxiv.org/abs/2504.20040
Github:
github.com/cvg/mpsfm
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#colmap #3DReconstruction #MASt3R_SfM #MP_SfM #StructureFromMotion