Evaluation Leaderboard

Click a column to sort. Bars indicate relative performance per metric.

All submissions

Public benchmark

Methods are ranked by AUC (VCRE < 45px) by default. Lower-is-better metrics sort ascending first; higher-is-better metrics sort descending first.

Methods evaluated with a single reference image and a single query frame.

Method	AUC (VCRE < 45px)	AUC (VCRE < 90px)	Precision (VCRE < 45px)	Precision (VCRE < 90px)	Median Reproj. Error (px)	AUC (Err < 25cm, 5°)	Precision (Err < 25cm, 5°)	Median Trans. Error (m)	Median Rot. Error (°)	Estimates for % of frames
DUNE (ViT-Base) + MASt3RMert Bülent Sarıyıldız, Philippe Weinzaepfel, Thomas Lucas, Pau de Jorge, Diane Larlus, Yannis Kalantidis · NAVER LABS EuropePaper ↗Code ↗2024-11-14	0.840	0.943	64.4%	81.1%	47.43	0.768	55.9%	0.389	4.564	99.6%
DUNE (ViT-Small) + MASt3RMert Bülent Sarıyıldız, Philippe Weinzaepfel, Thomas Lucas, Pau de Jorge, Diane Larlus, Yannis Kalantidis · NAVER LABS EuropePaper ↗Code ↗2025-06-12	0.834	0.939	63.8%	79.5%	49.70	0.754	53.8%	0.411	4.596	99.9%
RoMa-OmegaDavid Nordström, David Nordström, Xinyue Zhang, Thibaut Loiseau, Vincent Lepetit, Fredrik Kahl · Chalmers University of TechnologyCode ↗2026-06-18	0.817	0.945	60.2%	79.2%	55.37	0.755	50.2%	0.440	5.041	100.0%
MASt3R (Ess.Mat + D.Scale)Vincent Leroy, Yohann Cabon and Jerome Revaud · Naver Labs EuropePaper ↗Code ↗2024-07-23	0.817	0.933	63.0%	79.3%	48.79	0.740	54.7%	0.368	2.202	99.4%
LoMa-GDavid Nordström, Johan Edstedt, Georg Bökman, Jonathan Astermark, Anders Heyden, Viktor Larsson, Mårten Wadenbäck, Michael Felsberg, Fredrik Kahl · Chalmers University of Technology, Linköping University, Lund University, University of AmsterdamPaper ↗Code ↗2026-05-06	0.796	0.925	57.2%	75.7%	56.70	0.754	48.8%	0.500	4.358	99.2%
RoMa w/ Metric3Dv2Shigeki Kobayashi · GO Drive Inc.2026-09-28	0.736	0.870	48.2%	63.2%	80.80	0.662	39.8%	0.830	11.90	100.0%
interp_metric3d_loftr_3d2dLili Zhao, Zhili Liu, Lei Yang, Meng Guo · China Mobile Research Institute, N/A, China Mobile Research Institute, China Mobile Research InstitutePaper ↗2024-07-25	0.681	0.796	39.9%	51.4%	125.6	0.593	31.9%	1.754	31.22	100.0%
Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth KnowledgeMingyu Xiao ; Runze Chen ; Haiyong Luo ; Fang Zhao;Juan Wang;XuePeng Ma · Beijing University of Posts and Telecommunications;Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences;Shouguang Cheng Zhi Feng Xing Technology Co., Ltd.Paper ↗2024-07-25	0.656	0.849	45.3%	65.0%	79.96	0.469	28.6%	0.828	11.74	99.0%
DAv3 & SuperGlue (Ess.Mat. + D.Scale)David Nordström · Chalmers University of Technology2026-01-29	0.641	0.780	36.0%	49.4%	126.0	0.551	27.5%	1.693	26.71	98.5%
RoMa w/ MicKey depth mapsRoMa: Edstedt et al. - MicKey: Barroso-Laguna et al. · RoMa: Linköping University. - MicKey: Niantic.2024-06-07	0.604	0.734	37.8%	52.4%	111.9	0.546	31.4%	1.177	15.60	99.9%
MicKey trained w/ Overlap ScoreAxel Barroso-Laguna, Sowmya Munukutla, Victor Adrian Prisacariu, Eric Brachmann · NianticPaper ↗Code ↗2023-11-07	0.572	0.748	31.2%	49.3%	129.5	0.325	13.3%	1.657	27.26	100.0%
MicKeyAxel Barroso-Laguna, Sowmya Munukutla, Victor Adrian Prisacariu, Eric Brachmann · NianticPaper ↗Code ↗2023-10-31	0.558	0.741	30.1%	49.2%	126.9	0.283	12.0%	1.591	26.00	100.0%
SuperGlue w/ MicKey depth mapsSuperGlue: Sarlie et al. - MicKey: Barroso-Laguna et al. · SuperGlue: ETH Zurich, Magic Leap, Inc. - MicKey: Niantic2024-07-02	0.556	0.711	29.8%	43.0%	139.9	0.490	23.5%	1.696	26.13	98.5%
LoFTR w/ MicKey depth mapsLoFTR: Sun et al. - MicKey: Barroso-Laguna et al. · LoFTR: Zhejiang University, SenseTime Research - MicKey: Niantic2024-07-02	0.550	0.715	27.2%	40.7%	155.0	0.467	20.3%	1.922	33.60	100.0%
Mickey Variant GT_DepthHitesh Jain, Sagar Verma · Granular AIPaper ↗2024-07-03	0.548	0.720	28.0%	45.5%	142.0	0.273	10.8%	1.842	30.78	100.0%
FAR (LoFTR)Chris Rockwell, Nilesh Kulkarni, Linyi Jin, JJ Park, Justin Johnson, David F. Fouhey · University of Michigan, New York UniversityPaper ↗Code ↗2024-02-16	0.481	0.680	25.3%	44.2%	137.1	0.392	17.7%	1.482	17.26	100.0%
DPT-KITTI & DUSt3R (Ess.Mat. + D.Scale)Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, Jerome Revaud · NAVER LABS EUROPEPaper ↗Code ↗2024-03-03	0.459	0.698	28.7%	50.4%	115.8	0.394	21.4%	0.989	7.131	99.6%
FAR (SuperGlue)Chris Rockwell, Nilesh Kulkarni, Linyi Jin, JJ Park, Justin Johnson, David F. Fouhey · University of Michigan, New York UniversityPaper ↗Code ↗2024-02-16	0.456	0.668	25.8%	44.2%	135.5	0.351	17.1%	1.498	17.28	100.0%
DPT-KITTI & RoMa (Ess.Mat. + D.Scale) Note: maybe buggyJohan Edstedt · Linköping University2023-06-24	0.437	0.670	26.4%	45.7%	128.9	0.408	22.9%	1.235	11.19	100.0%
KBR++ & LoFTR (PnP)Jaime Spencer, Chris Russell, Simon Hadfield, Richard Bowden · University of Surrey, Oxford Internet Institute, University of Surrey, University of SurreyPaper ↗Code ↗2023-08-08	0.420	0.635	19.4%	34.4%	165.1	0.295	11.1%	2.238	37.82	100.0%
KBR & LoFTR (PnP)Jaime Spencer, Chris Russell, Simon Hadfield, Richard Bowden · University of Surrey, Oxford Internet Institute, University of Surrey, University of SurreyPaper ↗Code ↗2023-08-08	0.420	0.631	19.5%	34.0%	167.5	0.278	10.5%	2.326	39.52	100.0%
DPT-KITTI & ASpanFormer (Ess.Mat. + D.Scale)Hongkai Chen, Zixin Luo, Lei Zhou, Yurun Tian, Mingmin Zhen, Tian Fang, David McKinnon, Yanghai Tsin, Long Quan · Apple Inc, HKUSTPaper ↗2024-07-03	0.414	0.641	20.8%	37.0%	161.8	0.361	16.3%	1.901	29.25	100.0%
DPT-KITTI & LoFTR (PnP)2022-09-27	0.397	0.618	18.2%	33.5%	166.8	0.269	9.8%	2.312	39.42	100.0%
DPT-KITTI & LoFTR (Ess.Mat. + D.Scale)2022-09-27	0.387	0.615	18.8%	34.7%	167.6	0.346	15.5%	1.987	30.58	100.0%
DPT-KITTI & SuperGlue (Ess.Mat. + D.Scale)2022-09-27	0.385	0.602	20.6%	36.2%	160.3	0.346	16.8%	1.881	25.44	100.0%
DPT-KITTI & SuperGlue (PnP)2022-09-27	0.378	0.599	19.6%	36.0%	157.0	0.253	10.7%	2.108	32.58	100.0%
EpiFineShigeki Kobayashi · GO Drive Inc.2026-09-28	0.373	0.541	37.3%	54.1%	106.2	0.220	22.0%	1.360	18.20	100.0%
EpiFineShigeki Kobayashi · GO Drive Inc.2026-09-26	0.369	0.533	36.9%	53.3%	108.1	0.213	21.3%	1.400	19.30	100.0%
EpiFineShigeki Kobayashi · GO Drive Inc.2026-09-25	0.360	0.526	36.0%	52.6%	113.1	0.187	18.7%	1.470	20.70	100.0%
DPT-KITTI & LightGlue w/ DISK (Ess.Mat. + D.Scale)Map-free leaderboard baseline · Niantic - Map-free leaderboard baseline2024-07-03	0.355	0.527	19.5%	33.2%	138.8	0.314	15.9%	1.445	18.52	78.0%
DPT-KITTI & DISK (Ess.Mat. + D.Scale)Michał J. Tyszkiewicz, Pascal Fua, Eduard Trulls · École Polytechnique Fédérale de Lausanne (EPFL), Google Research, Zurich2024-07-02	0.346	0.536	15.1%	26.9%	208.2	0.264	10.2%	2.593	51.96	100.0%
EpiFineShigeki Kobayashi · GO Drive Inc.2026-09-24	0.327	0.502	32.7%	50.2%	118.9	0.153	15.3%	1.630	23.00	100.0%
DPT-KITTI & DeDoDe (v1) (Ess.Mat. + D.Scale)Johan Edstedt, Georg Bökman, Mårten Wadenbäck, Michael Felsberg · Linköping University, Chalmers University of TechnologyPaper ↗Code ↗2024-07-03	0.325	0.531	16.9%	31.2%	167.4	0.265	12.5%	2.021	30.30	88.0%
DPT-KITTI & SIFT (Ess.Mat. + D.Scale)2022-09-27	0.313	0.505	13.7%	25.0%	222.9	0.253	10.3%	2.930	61.43	100.0%
DPT-KITTI & SIFT (PnP)2022-09-27	0.299	0.468	14.3%	25.1%	192.0	0.191	7.8%	3.354	63.74	100.0%
DPT-KITTI & SiLK (Ess.Mat. + D.Scale)Pierre Gleize, Weiyao Wang, Matt Feiszli · Meta AIPaper ↗Code ↗2024-07-03	0.192	0.312	9.8%	18.1%	176.4	0.157	7.3%	2.208	33.81	52.2%
RPR [R(α, β, γ) + s · t(θ, φ)]2022-09-27	0.181	0.355	18.1%	35.5%	166.4	0.105	10.5%	1.831	23.27	100.0%
RPR [3D-3D]2022-09-27	0.181	0.387	18.1%	38.7%	148.7	0.061	6.1%	1.698	22.94	100.0%
RPR [R(6D) +t]2022-09-27	0.156	0.402	15.6%	40.2%	147.1	0.061	6.1%	1.682	22.52	100.0%

Methods evaluated using sequences of multiple query frames (9-frame window).

Method	AUC (VCRE < 45px)	AUC (VCRE < 90px)	Precision (VCRE < 45px)	Precision (VCRE < 90px)	Median Reproj. Error (px)	AUC (Err < 25cm, 5°)	Precision (Err < 25cm, 5°)	Median Trans. Error (m)	Median Rot. Error (°)	Estimates for % of frames
[Baseline] MicKey (using only a single frame)BaselineAxel Barroso-Laguna, Sowmya Munukutla, Victor Adrian Prisacariu, Eric Brachmann · NianticPaper ↗Code ↗2024-05-20	0.572	0.752	31.3%	49.6%	128.9	0.326	13.6%	1.656	26.92	100.0%
Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth KnowledgeMingyu Xiao ; Runze Chen ; Haiyong Luo ; Fang Zhao · Beijing University of Posts and TelecommunicationsPaper ↗2024-08-22	0.387	0.721	25.2%	53.1%	105.8	0.190	10.8%	0.951	15.10	99.1%

Live results from the benchmark server.