MV-RoMa: From Pairwise Matching into Multi-View Track Reconstruction

2026-03-29

AI Summary

This work addresses the limitations of existing pairwise image matching methods, which often yield fragmented and geometrically inconsistent correspondences in multi-view settings, hindering high-quality 3D reconstruction. To overcome this, we propose MV-RoMa, a geometrically consistent dense multi-view matching model that jointly estimates dense correspondences from a source image to multiple co-visible images, enabling coherent multi-view trajectory reconstruction. Our approach introduces a novel multi-view encoder and a pixel-level attention refinement module, which leverages pairwise matches as geometric priors to enhance consistency while avoiding the high computational cost of full cross-attention. Additionally, we design an SfM-oriented trajectory integration strategy for post-processing. Experiments demonstrate that MV-RoMa significantly outperforms current methods on multiple challenging benchmarks, producing denser, more reliable correspondences and thereby enabling more accurate 3D reconstruction.

Abstract

Establishing consistent correspondences across images is essential for 3D vision tasks such as structure-from-motion (SfM), yet most existing matchers operate in a pairwise manner, often producing fragmented and geometrically inconsistent tracks when their predictions are chained across views. We propose MV-RoMa, a multi-view dense matching model that jointly estimates dense correspondences from a source image to multiple co-visible targets. Specifically, we design an efficient model architecture which avoids high computational cost of full cross-attention for multi-view feature interaction: (i) multi-view encoder that leverages pair-wise matching results as a geometric prior, and (ii) multi-view matching refiner that refines correspondences using pixel-wise attention. Additionally, we propose a post-processing strategy that integrates our model's consistent multi-view correspondences as high-quality tracks for SfM. Across diverse and challenging benchmarks, MV-RoMa produces more reliable correspondences and substantially denser, more accurate 3D reconstructions than existing sparse and dense matching methods. Project page: https://icetea-cv.github.io/mv-roma/.

Problem

• multi-view matching
• geometric consistency
• 3D reconstruction
• structure-from-motion
• correspondence fragmentation

Innovation

• multi-view matching
• dense correspondence
• structure-from-motion
• geometric consistency
• pixel-wise attention