mlsanityData to 14 Oct 2026

MoRe: Monocular Geometry Refinement via Graph Optimization for Cross-View Consistency

arXiv 2510.07119 ↗First posted 8 Oct 2025cs.CVcited by 3

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Abstract (arXiv)

Monocular 3D foundation models offer an extensible solution for perception tasks, making them attractive for broader 3D vision applications. In this paper, we propose MoRe, a training-free Monocular Geometry Refinement method designed to improve cross-view consistency and achieve scale alignment. To induce inter-frame relationships, our method employs feature matching between frames to establish correspondences. Rather than applying simple least squares optimization on these matched points, we formulate a graph-based optimization framework that performs local planar approximation using the estimated 3D points and surface normals estimated by monocular foundation models. This formulation addresses the scale ambiguity inherent in monocular geometric priors while preserving the underlying 3D structure. We further demonstrate that MoRe not only enhances 3D reconstruction but also improves novel view synthesis, particularly in sparse view rendering scenarios.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 45 of its 72 references that are in the map. First posted on arXiv 8 Oct 2025.

Builds on

45 of 72 references.

References · 45

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  2. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  3. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,117
  4. 2023DUSt3R: Geometric 3D Vision Made Easycited by 1,567
  5. 2025VGGT: Visual Geometry Grounded Transformercited by 1,539
  6. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
  7. 2024Depth Anything V2cited by 1,422
  8. 2017Matterport3D: Learning from RGB-D Data in Indoor Environmentscited by 1,376
  9. 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,360
  10. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  11. 2024Grounding Image Matching in 3D with MASt3Rcited by 983
  12. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
33 more
  1. 2018MVSNet: Depth Inference for Unstructured Multi-view Stereocited by 788
  2. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  3. 2018MegaDepth: Learning Single-View Depth Prediction from Internet Photoscited by 737
  4. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 723
  5. 2024MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motioncited by 442
  6. 2019Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matchingcited by 417
  7. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 403
  8. 2024Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimationcited by 395
  9. 2024UniDepth: Universal Monocular Metric Depth Estimationcited by 382
  10. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  11. 2024MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervisioncited by 299
  12. 2023FSGS: Real-Time Few-shot View Synthesis using Gaussian Splattingcited by 279
  13. 2020PatchmatchNet: Learned Multi-View Patchmatch Stereocited by 207
  14. 2024MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priorscited by 205
  15. 2016DeMoN: Depth and Motion Network for Learning Monocular Stereocited by 201
  16. 2018DeepV2D: Video to Depth with Differentiable Structure from Motioncited by 185
  17. 2022DKM: Dense Kernelized Feature Matching for Geometry Estimationcited by 177
  18. 2024MASt3R-SfM: a Fully-Integrated Solution for Unconstrained Structure-from-Motioncited by 162
  19. 2019Cost Volume Pyramid Based Depth Inference for Multi-View Stereocited by 154
  20. 2019Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awarenesscited by 151
  21. 2019Learning the Depths of Moving People by Watching Frozen Peoplecited by 120
  22. 2024MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereocited by 87
  23. 2019Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volumecited by 59
  24. 2022Multiview Stereo with Cascaded Epipolar RAFTcited by 51
  25. 2025Light3R-SfM: Towards Feed-forward Structure-from-Motioncited by 47
  26. 2021MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutionscited by 43
  27. 2022A Benchmark and a Baseline for Robust Multi-view Depth Estimationcited by 39
  28. 2025MP-SfM: Monocular Surface Priors for Robust Structure-from-Motioncited by 26
  29. 2025EDGS: Eliminating Densification for Efficient Convergence of 3DGScited by 22
  30. 2025Relative Pose Estimation through Affine Corrections of Monocular Depth Priorscited by 18
  31. 2019Joint Graph-based Depth Refinement and Normal Estimationcited by 12
  32. 2021DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenescited by 5
  33. 2022SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learningcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026VGGT-Omegacited by 26
  2. 2026Wid3R: Wide Field-of-View 3D Reconstruction via Camera Model Conditioningcited by 4
  3. 2026BLASt3R: Bundle Adjustment of Any Image Set with Multi-View Matching and Monocular Priorscited by 0