MVLoMa: Multi-View Local Feature Matching with Any Geometry Foundation Model

Xinyang Lu; Shengzhe Wei; Sheng Tang; Weiqi Zhang; Juntong Fang; Zesong Li

cs.CVOct 22, 2026

MVLoMa: Multi-View Local Feature Matching with Any Geometry Foundation Model

Authors: Xinyang Lu, Shengzhe Wei, Sheng Tang, Weiqi Zhang, Juntong Fang, Zesong Li

Organizations: Kosmo Research · School of Astronautics, Harbin Institute of Technology · School of Software, Tsinghua University

Abstract

Local feature matching is typically formulated for image pairs, although structure-from-motion and visual localization commonly match one reference image against many retrieved or covisible neighbours. Applying a pairwise matcher independently to these centre-neighbour edges duplicates reference-side computation and prevents the matcher from exploiting complementary multi-view geometric evidence from the remaining observations. To address these limitations, we introduce MVLoMa, a sparse star-graph matcher that extends LoMa from image pairs to multiple views. MVLoMa first uses a frozen multi-view geometry model to jointly describe all images in a star, then injects the resulting cross-view geometric context into local descriptors. It then matches the centre image to all neighbours in a single forward pass through per-view self-attention and bidirectional centre-neighbour cross-attention. For supervision, we derive positive correspondences only from depth-valid, mutually visible dense warps and explicitly penalize high-confidence matches that violate epipolar geometry. On 25-view star graphs from HyperSim, ScanNet++, Mip-NeRF 360, and Tanks and Temples, MVLoMa obtains the best macro-average relative-pose AUC among the compared methods at all three thresholds (58.65/85.41/90.99). Its matcher is 2.20 times faster than sequential evaluation of the 24 centre-neighbour edges. The same design supports four tested frozen geometry encoders -- DA3, $π^3$ , VGGT, and VGGT- $Ω$ -- with DA3-Giant performing best on all reported encoder-ablation metrics. Controlled ablations further isolate the effects of geometry-aware descriptors, visibility filtering, geometric negative supervision, and joint multi-view inference.

Figures & tables

Figure 1: Overview of MVLoMa . Given one source (centre) image and multiple target (neighbour) images, a trainable per-view VGG branch extracts local features, while a frozen multi-view geometry foundation model supplies geometry-aware patch features for the same views. A trainable convolutional refiner fuses the two streams into descriptors sampled at 4096 keypoints per image. The multi-view star-graph matcher then jointly processes the centre and all neighbours and outputs sparse centre–neighbour correspondences for every edge. Flames denote trainable modules and the snowflake denotes the frozen foundation model.

Two-view
Method	HyperSim	ScanNet++	Mip-NeRF 360	T&T	Average
LightGlue	20.20/47.34/55.72	22.84/61.83/74.49	51.28/83.46/90.02	52.42/84.69/91.08	36.69/69.33/77.83
LoMa-L	51.99/77.00/82.86	37.68/ 75.57 / 85.14	71.69/93.25/96.50	68.22 / 91.96 / 95.74	57.40/84.44/90.06
RoMa	45.06/64.82/69.78	36.81/73.88/83.70	75.42 /93.27/95.88	66.56/90.99/95.01	55.96/80.74/86.09
RoMa v2	52.93/ 80.65 / 86.92	33.02/73.33/84.58	74.81 / 94.16 / 97.00	67.06/91.32/95.37	56.95/84.87/ 90.97
Multi-view

Table 1: Relative pose AUC on 25-view star graphs; each cell reports AUC@

1^{\circ}/5^{\circ}/10^{\circ}

in that order. MV-RoMa uses 13 views owing to out-of-memory at 25 views. Best and second-best values are bold and underlined. Average is the macro-average over the four benchmarks at each threshold; T&T denotes Tanks and Temples.

	HyperSim			Mip-NeRF 360
Variant	@1	@5	@10	@1	@5	@10
Full	52.84	78.42	84.34	73.85	93.96	96.92
w/o GT filtering	51.93	77.75	83.77	73.69	93.85	96.83
w/o negative loss	51.04	76.96	83.19	72.72	93.42	96.55

Table 2: GT filtering and geometric negative loss (DA3-G; four-view training; test at 784). Best and second-best values are bold and underlined.

	HyperSim		AerialMD
Backbone	$o>0.35$	$o>0.05$	$o>0.35$	$o>0.05$
DA3-G	69.27	61.14	77.97	48.16
DINOv2-G	68.11	49.70	77.56	47.51
DINOv2-L	67.72	48.29	77.33	47.08

Table 3: Controlled frozen-backbone ablation at resolution 784. We report AUC@

1^{\circ}

at two RoMa v2 overlap thresholds ( Edstedt et al., 2026 ) . Best and second-best values are bold and underlined.

	HyperSim			MegaDepth-X			TerraSky3D
Encoder	@1	@5	@10	@1	@5	@10	@1	@5	@10
DA3-G	53.90	80.02	85.99	18.88	52.97	65.77	55.53	83.88	90.04
$\pi^{3}$	51.50	76.56	82.57	18.37	52.78	65.42	53.77	82.81	89.45
VGGT	52.28	78.17	84.26	18.05	52.22	64.70	53.21	81.92	88.56
VGGT- $\Omega$	52.07	77.70	83.46	18.43	52.91	65.58	55.21	83.38	89.78

Table 4: Frozen foundation encoder ablation for MVLoMa (2–8 train views, 25 test views, resolution 784). Best and second-best values are bold and underlined.

# views	HyperSim	Mip-NeRF 360	T&T	TerraSky3D	Average
2	52.51/77.63/83.29	73.96/94.02/96.95	67.63/ 91.86 / 95.73	54.76/83.01/89.37	62.21/86.63/91.34
4	53.79/79.43/85.68	74.31 /94.02/96.95	67.85/ 91.89 / 95.78	54.58/83.13/89.29	62.63/87.12/91.92
7	53.90/79.13/85.14	74.32 / 94.06 / 96.97	67.89/91.82/95.70	54.97/83.42/89.75	62.77/87.11/91.89
13	54.47 / 80.12 / 85.88	74.20/94.04/96.96	67.92 /91.82/95.66	55.39 / 83.47 / 89.85	63.00 / 87.36 / 92.09
25	53.90 / 80.02 / 85.99	74.29/ 94.08 / 96.97	67.96 /91.84/95.68	55.53 / 83.88 / 90.04	62.92 / 87.45 / 92.17

Table 5: Inference view count for DA3-G MVLoMa trained with 2–8 views at resolution 784; each cell reports AUC@

1^{\circ}/5^{\circ}/10^{\circ}

. Average is the macro-average over the four benchmarks at each threshold. Best and second-best values are bold and underlined; T&T denotes Tanks and Temples.

Figure 2: Macro-average relative pose AUC from Table 5 versus the number of inference views. Each panel averages the corresponding AUC threshold over HyperSim, Mip-NeRF 360, Tanks and Temples, and TerraSky3D.

Views	Edges	Pair	Star	Sp.	Pair	Star	Sp.
		Theory (TFLOPs)			A800 (ms)
2	1	0.676	0.676	$1.00\times$	20.95	21.18	$0.99\times$
4	3	2.029	1.508	$1.35\times$	57.98	37.15	$1.56\times$
7	6	4.059	2.754	$1.47\times$	113.81	60.50	$1.88\times$
13	12	8.117	5.247	$1.55\times$	237.60	105.78	$2.25\times$
25	24	16.235	10.234	$1.59\times$	446.96	203.09	$2.20\times$

Table 6: Transformer compute and matcher-only runtime for nine LoMa-L layers. FLOPs count a multiply–add as two operations. Runtime uses one NVIDIA A800, batch size 1, and 2048 keypoints; values average 46 of 50 trials after dropping the two fastest and two slowest.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Dataset	Domain	GT source	Sample w.	Overlap sampling	Neg. w.
MegaDepth	Outdoor	MVS	1.0	5–35 / 35–100 (0.5 each)	1.0
MegaDepth (Re-MVS)	Outdoor	MVS	1.0	1–35 / 35–100 (0.5 each)	1.0
MegaDepth-X	Outdoor	MVS	1.0	1–35 / 35–100 (0.5 each)	0.01
HyperSim	Indoor	Graphics	1.0	5–50 / 50–100 (0.5 each)	0.01
ScanNet++	Indoor	Mesh	0.5	20–100 only	0.1
BlendedMVS	Aerial	Mesh	1.0	5–20 / 20–100 (0.5 each)	1.0

Appendix

Table 7: Training data mixture for the final DA3-G model. MVS denotes multi-view stereo; Sample w. and Neg. w. are dataset-sampling and geometric negative-loss weights.

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LoMa: Local Feature Matching Revisited

David Nordström, Johan Edstedt, Georg Bökman, Jonathan Astermark+5

Chalmers University of Technology · Linköping University · University of Amsterdam+1

Multi-View Geometry Image Matching Local Feature Matching

Open paper AskPDF arXiv

Local feature matching has long been a fundamental component of 3D vision systems such as Structure-from-Motion (SfM), yet progress has lagged behind the rapid advances of modern data-driven approaches. The newer approaches, such as feed-forward reconstruction models, have benefited extensively from scaling dataset sizes, whereas local feature matching models are still only trained on a few mid-sized datasets. In this paper, we revisit local feature matching from a data-driven perspective. In our approach, which we call LoMa, we combine large and diverse data mixtures, modern training recipes, scaled model capacity, and scaled compute, resulting in remarkable gains in performance. Since current standard benchmarks mainly rely on collecting sparse views from successful 3D reconstructions, the evaluation of progress in feature matching has been limited to relatively easy image pairs. To address the resulting saturation of benchmarks, we collect 1000 highly challenging image pairs from internet data into a new dataset called HardMatch. Ground truth correspondences for HardMatch are obtained via manual annotation by the authors. In our extensive benchmarking suite, we find that LoMa makes outstanding progress across the board, outperforming the state-of-the-art method ALIKED+LightGlue by +18.6 mAA on HardMatch, +29.5 mAA on WxBS, +21.4 (1m, 10

^\circ

) on InLoc, +24.2 AUC on RUBIK, and +12.4 mAA on IMC 2022. We release our code and models publicly at https://github.com/davnords/LoMa.

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