High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

CNVR-MVS		96.30 1	96.54 1	95.55 16	99.31 6	87.69 25	99.06 23	97.12 35	94.66 10	96.79 30	98.78 15	86.42 32	99.95 6	97.59 40	99.18 7	99.00 33

DPM-MVS		96.21 2	95.53 15	98.26 1	96.26 114	95.09 1	99.15 12	96.98 46	93.39 23	96.45 38	98.79 14	90.17 10	99.99 1	89.33 177	99.25 6	99.70 4

MCST-MVS		96.17 3	96.12 6	96.32 8	99.42 3	89.36 11	98.94 31	97.10 37	95.17 4	92.11 108	98.46 40	87.33 27	99.97 3	97.21 47	99.31 4	99.63 8

DVP-MVS++		96.05 4	96.41 3	94.96 25	99.05 14	85.34 66	98.13 71	96.77 73	88.38 92	97.70 14	98.77 16	92.06 3	99.84 19	97.47 41	99.37 1	99.70 4

SED-MVS		95.88 5	96.22 4	94.87 26	99.03 20	85.03 81	99.12 16	96.78 67	88.72 84	97.79 11	98.91 3	88.48 19	99.82 25	98.15 22	98.97 17	99.74 1

MM		95.85 6	95.74 11	96.15 9	96.34 111	89.50 10	99.18 9	98.10 8	95.68 1	96.64 34	97.92 80	80.72 77	99.80 33	99.16 2	97.96 62	99.15 28

NCCC		95.63 7	95.94 9	94.69 33	99.21 7	85.15 77	99.16 11	96.96 50	94.11 15	95.59 50	98.64 25	85.07 39	99.91 8	95.61 64	99.10 9	99.00 33

MSP-MVS		95.62 8	96.54 1	92.86 113	98.31 54	80.10 242	97.42 130	96.78 67	92.20 36	97.11 24	98.29 53	93.46 1	99.10 123	96.01 57	99.30 5	99.38 15
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
MED-MVS		95.59 9	96.05 8	94.21 47	99.06 11	83.70 108	98.35 57	97.14 31	87.65 117	97.03 27	98.83 10	89.87 13	99.96 4	97.78 36	98.71 31	98.97 36

DVP-MVS		95.58 10	95.91 10	94.57 36	99.05 14	85.18 72	99.06 23	96.46 122	88.75 82	96.69 31	98.76 18	87.69 25	99.76 46	97.90 30	98.85 21	98.77 47
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
MGCNet		95.58 10	95.44 17	96.01 11	97.63 78	89.26 13	99.27 5	96.59 102	94.71 9	97.08 25	97.99 74	78.69 110	99.86 15	99.15 3	97.85 66	98.91 41

DPE-MVS		95.32 12	95.55 14	94.64 34	98.79 29	84.87 86	97.77 97	96.74 78	86.11 167	96.54 37	98.89 9	88.39 21	99.74 54	97.67 39	99.05 12	99.31 21
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVS++		95.32 12	95.48 16	94.85 27	98.62 40	86.04 44	97.81 94	96.93 53	92.45 30	95.69 48	98.50 35	85.38 37	99.85 17	94.75 77	99.18 7	98.65 57

patch_mono-2		95.14 14	96.08 7	92.33 151	98.44 49	77.84 324	98.43 52	97.21 26	92.58 29	97.68 16	97.65 98	86.88 29	99.83 23	98.25 18	97.60 74	99.33 19

DELS-MVS		94.98 15	94.49 34	96.44 7	96.42 109	90.59 8	99.21 8	97.02 43	94.40 14	91.46 117	97.08 129	83.32 61	99.69 66	92.83 109	98.70 33	99.04 31
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
fmvsm_l_conf0.5_n_9		94.91 16	95.60 12	92.84 116	95.20 155	80.55 220	99.45 1	96.36 139	95.17 4	98.48 4	98.55 28	80.53 80	99.78 40	98.87 7	97.79 69	98.19 85

fmvsm_l_conf0.5_n_a		94.91 16	95.30 18	93.72 68	94.50 185	84.30 96	99.14 14	96.00 169	91.94 42	97.91 8	98.60 26	84.78 42	99.77 44	98.84 8	96.03 128	97.08 204

fmvsm_l_conf0.5_n		94.89 18	95.24 19	93.86 59	94.42 188	84.61 89	99.13 15	96.15 157	92.06 39	97.92 6	98.52 34	84.52 45	99.74 54	98.76 10	95.67 135	97.22 186

CANet		94.89 18	94.64 31	95.63 14	97.55 84	88.12 19	99.06 23	96.39 132	94.07 17	95.34 52	97.80 89	76.83 149	99.87 13	97.08 49	97.64 73	98.89 42

SD-MVS		94.84 20	95.02 25	94.29 43	97.87 70	84.61 89	97.76 99	96.19 155	89.59 74	96.66 33	98.17 61	84.33 47	99.60 77	96.09 56	98.50 42	98.66 56
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
ME-MVS		94.82 21	95.04 23	94.17 51	99.17 9	83.70 108	97.66 106	97.22 25	85.79 181	95.34 52	98.90 6	84.89 40	99.86 15	97.78 36	98.60 36	98.94 38

test_fmvsm_n_1920		94.81 22	95.60 12	92.45 140	95.29 151	80.96 204	99.29 4	97.21 26	94.50 13	97.29 23	98.44 41	82.15 69	99.78 40	98.56 12	97.68 72	96.61 231

TSAR-MVS + MP.		94.79 23	95.17 22	93.64 74	97.66 77	84.10 99	95.85 278	96.42 127	91.26 48	97.49 21	96.80 142	86.50 31	98.49 156	95.54 66	99.03 13	98.33 73
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SMA-MVS		94.70 24	94.68 30	94.76 30	98.02 65	85.94 48	97.47 123	96.77 73	85.32 194	97.92 6	98.70 23	83.09 64	99.84 19	95.79 61	99.08 10	98.49 64
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
fmvsm_l_conf0.5_n_3		94.61 25	94.92 26	93.68 72	94.52 180	82.80 131	99.33 2	96.37 137	95.08 6	97.59 20	98.48 38	77.40 133	99.79 37	98.28 16	97.21 89	98.44 68

DeepPCF-MVS	89.82 1	94.61 25	96.17 5	89.91 277	97.09 102	70.21 427	98.99 29	96.69 86	95.57 2	95.08 60	99.23 2	86.40 33	99.87 13	97.84 34	98.66 34	99.65 7

BridgeMVS		94.60 27	94.30 40	95.48 17	96.45 108	88.82 15	96.33 229	95.58 201	91.12 50	95.84 47	93.87 263	83.47 60	98.37 166	97.26 45	98.81 24	99.24 24

APDe-MVS		94.56 28	94.75 27	93.96 57	98.84 28	83.40 117	98.04 79	96.41 128	85.79 181	95.00 62	98.28 54	84.32 50	99.18 116	97.35 44	98.77 28	99.28 22
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_9		94.52 29	95.22 20	92.41 145	95.79 134	78.61 294	98.73 38	96.00 169	94.91 8	97.73 13	98.73 21	79.09 102	99.79 37	99.14 4	96.86 106	98.83 44

fmvsm_s_conf0.5_n_8		94.52 29	95.04 23	92.96 108	95.15 160	81.14 192	99.09 20	96.66 91	95.53 3	97.84 10	98.71 22	76.33 160	99.81 29	99.24 1	96.85 108	97.92 112

DeepC-MVS_fast	89.06 2	94.48 31	94.30 40	95.02 23	98.86 27	85.68 56	98.06 77	96.64 95	93.64 21	91.74 115	98.54 30	80.17 86	99.90 9	92.28 118	98.75 29	99.49 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_11		94.41 32	95.19 21	92.09 169	95.65 138	80.91 207	99.23 7	94.85 247	94.92 7	97.68 16	98.82 12	79.31 96	99.78 40	98.83 9	97.38 83	95.60 264

fmvsm_s_conf0.5_n_10		94.36 33	94.73 28	93.23 94	95.19 156	82.87 129	99.18 9	96.39 132	93.97 18	97.91 8	98.53 32	75.88 173	99.82 25	98.58 11	96.95 101	97.00 207

TSAR-MVS + GP.		94.35 34	94.50 33	93.89 58	97.38 96	83.04 125	98.10 73	95.29 226	91.57 44	93.81 79	97.45 107	86.64 30	99.43 94	96.28 55	94.01 156	99.20 26

train_agg		94.28 35	94.45 35	93.74 65	98.64 37	83.71 106	97.82 92	96.65 92	84.50 225	95.16 56	98.09 67	84.33 47	99.36 99	95.91 60	98.96 19	98.16 88

MSLP-MVS++		94.28 35	94.39 37	93.97 56	98.30 55	84.06 100	98.64 44	96.93 53	90.71 57	93.08 90	98.70 23	79.98 90	99.21 109	94.12 86	99.07 11	98.63 58

MG-MVS		94.25 37	93.72 49	95.85 13	99.38 4	89.35 12	97.98 81	98.09 9	89.99 68	92.34 102	96.97 134	81.30 75	98.99 129	88.54 194	98.88 20	99.20 26

TestfortrainingZip a		94.24 38	94.19 43	94.40 40	99.06 11	84.33 94	98.35 57	96.81 66	87.65 117	95.97 46	98.83 10	84.06 53	99.89 11	91.98 126	95.03 142	98.97 36

fmvsm_s_conf0.5_n_6		94.17 39	94.70 29	92.58 134	93.50 223	81.20 190	99.08 21	96.48 121	92.24 35	98.62 3	98.39 46	78.58 112	99.72 59	98.08 26	97.36 84	96.81 221

SF-MVS		94.17 39	94.05 46	94.55 37	97.56 83	85.95 46	97.73 101	96.43 126	84.02 242	95.07 61	98.74 20	82.93 65	99.38 96	95.42 68	98.51 40	98.32 74

PS-MVSNAJ		94.17 39	93.52 56	96.10 10	95.65 138	92.35 2	98.21 66	95.79 190	92.42 31	96.24 40	98.18 58	71.04 261	99.17 117	96.77 52	97.39 82	96.79 222

SteuartSystems-ACMMP		94.13 42	94.44 36	93.20 96	95.41 146	81.35 188	99.02 27	96.59 102	89.50 76	94.18 75	98.36 50	83.68 59	99.45 93	94.77 76	98.45 45	98.81 46
Skip Steuart: Steuart Systems R&D Blog.
EPNet		94.06 43	94.15 44	93.76 63	97.27 99	84.35 93	98.29 63	97.64 14	94.57 11	95.36 51	96.88 137	79.96 91	99.12 122	91.30 132	96.11 125	97.82 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		93.99 44	94.36 38	92.86 113	92.82 253	81.12 193	99.26 6	96.37 137	93.47 22	95.16 56	98.21 56	79.00 103	99.64 72	98.21 20	96.73 112	97.83 121

fmvsm_s_conf0.5_n_3		93.95 45	94.53 32	92.20 163	94.41 189	80.04 244	98.90 33	95.96 174	94.53 12	97.63 19	98.58 27	75.95 170	99.79 37	98.25 18	96.60 114	96.77 224

xiu_mvs_v2_base		93.92 46	93.26 62	95.91 12	95.07 163	92.02 6	98.19 67	95.68 196	92.06 39	96.01 45	98.14 63	70.83 266	98.96 131	96.74 54	96.57 115	96.76 226

lupinMVS		93.87 47	93.58 54	94.75 31	93.00 240	88.08 20	99.15 12	95.50 208	91.03 53	94.90 63	97.66 94	78.84 106	97.56 215	94.64 80	97.46 77	98.62 59

fmvsm_s_conf0.5_n		93.69 48	94.13 45	92.34 149	94.56 177	82.01 157	99.07 22	97.13 33	92.09 37	96.25 39	98.53 32	76.47 155	99.80 33	98.39 14	94.71 146	95.22 278

APD-MVS		93.61 49	93.59 53	93.69 71	98.76 30	83.26 120	97.21 142	96.09 161	82.41 289	94.65 69	98.21 56	81.96 72	98.81 141	94.65 79	98.36 51	99.01 32
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
fmvsm_s_conf0.5_n_4		93.59 50	94.32 39	91.41 217	93.89 207	79.24 267	98.89 34	96.53 113	92.82 27	97.37 22	98.47 39	77.21 141	99.78 40	98.11 25	95.59 137	95.21 279

PHI-MVS		93.59 50	93.63 52	93.48 85	98.05 64	81.76 173	98.64 44	97.13 33	82.60 285	94.09 76	98.49 36	80.35 81	99.85 17	94.74 78	98.62 35	98.83 44

fmvsm_s_conf0.5_n_5		93.57 52	93.75 48	93.01 105	92.87 252	82.73 132	98.93 32	95.90 182	90.96 55	95.61 49	98.39 46	76.57 153	99.63 74	98.32 15	96.24 120	96.68 230

BP-MVS1		93.55 53	93.50 57	93.71 69	92.64 264	85.39 65	97.78 96	96.84 61	89.52 75	92.00 109	97.06 131	88.21 22	98.03 181	91.45 131	96.00 130	97.70 135

ACMMP_NAP		93.46 54	93.23 63	94.17 51	97.16 100	84.28 97	96.82 186	96.65 92	86.24 164	94.27 73	97.99 74	77.94 122	99.83 23	93.39 95	98.57 38	98.39 71

MVS_111021_HR		93.41 55	93.39 60	93.47 87	97.34 97	82.83 130	97.56 115	98.27 6	89.16 80	89.71 145	97.14 124	79.77 92	99.56 84	93.65 93	97.94 63	98.02 99

fmvsm_s_conf0.5_n_a		93.34 56	93.71 50	92.22 160	93.38 226	81.71 176	98.86 35	96.98 46	91.64 43	96.85 29	98.55 28	75.58 179	99.77 44	97.88 32	93.68 165	95.18 280

lecture		93.17 57	93.57 55	91.96 181	97.80 71	78.79 289	98.50 50	96.98 46	86.61 157	94.75 68	98.16 62	78.36 116	99.35 101	93.89 88	97.12 94	97.75 129

PVSNet_Blended		93.13 58	92.98 68	93.57 79	97.47 85	83.86 102	99.32 3	96.73 80	91.02 54	89.53 151	96.21 155	76.42 157	99.57 82	94.29 83	95.81 134	97.29 184

CDPH-MVS		93.12 59	92.91 70	93.74 65	98.65 36	83.88 101	97.67 105	96.26 147	83.00 275	93.22 87	98.24 55	81.31 74	99.21 109	89.12 178	98.74 30	98.14 90

dcpmvs_2		93.10 60	93.46 59	92.02 179	97.77 73	79.73 255	94.82 329	93.86 335	86.91 145	91.33 121	96.76 143	85.20 38	98.06 179	96.90 51	97.60 74	98.27 80

test_fmvsmconf0.1_n		93.08 61	93.22 64	92.65 126	88.45 390	80.81 210	99.00 28	95.11 232	93.21 24	94.00 77	97.91 82	76.84 147	99.59 78	97.91 29	96.55 116	97.54 151

SPE-MVS-test		92.98 62	93.67 51	90.90 241	96.52 107	76.87 347	98.68 41	94.73 254	90.36 65	94.84 65	97.89 84	77.94 122	97.15 271	94.28 85	97.80 68	98.70 55

fmvsm_s_conf0.5_n_2		92.97 63	93.38 61	91.73 199	94.10 201	80.64 215	98.96 30	95.89 183	94.09 16	97.05 26	98.40 45	68.92 285	99.80 33	98.53 13	94.50 150	94.74 291

alignmvs		92.97 63	92.26 89	95.12 22	95.54 143	87.77 23	98.67 42	96.38 134	88.04 103	93.01 91	97.45 107	79.20 100	98.60 147	93.25 101	88.76 242	98.99 35

fmvsm_s_conf0.1_n		92.93 65	93.16 65	92.24 157	90.52 346	81.92 163	98.42 54	96.24 149	91.17 49	96.02 44	98.35 51	75.34 190	99.74 54	97.84 34	94.58 148	95.05 283

HFP-MVS		92.89 66	92.86 73	92.98 107	98.71 31	81.12 193	97.58 113	96.70 84	85.20 199	91.75 114	97.97 79	78.47 113	99.71 62	90.95 138	98.41 47	98.12 93

NormalMVS		92.88 67	92.97 69	92.59 133	97.80 71	82.02 155	97.94 84	94.70 255	92.34 32	92.15 106	96.53 150	77.03 142	98.57 149	91.13 136	97.12 94	97.19 193

fmvsm_s_conf0.5_n_7		92.88 67	93.82 47	90.08 268	92.79 256	76.45 355	98.54 48	96.74 78	92.28 34	95.22 55	98.49 36	74.91 197	98.15 177	98.28 16	97.13 93	95.63 262

PAPM		92.87 69	92.40 83	94.30 42	92.25 287	87.85 22	96.40 222	96.38 134	91.07 52	88.72 169	96.90 135	82.11 70	97.37 253	90.05 164	97.70 71	97.67 137

GDP-MVS		92.85 70	92.55 80	93.75 64	92.82 253	85.76 52	97.63 107	95.05 236	88.34 94	93.15 88	97.10 128	86.92 28	98.01 184	87.95 202	94.00 157	97.47 162

ZNCC-MVS		92.75 71	92.60 78	93.23 94	98.24 57	81.82 171	97.63 107	96.50 117	85.00 209	91.05 126	97.74 91	78.38 114	99.80 33	90.48 151	98.34 52	98.07 96

PAPR		92.74 72	92.17 93	94.45 38	98.89 26	84.87 86	97.20 144	96.20 153	87.73 112	88.40 174	98.12 64	78.71 109	99.76 46	87.99 201	96.28 119	98.74 49

CS-MVS		92.73 73	93.48 58	90.48 254	96.27 113	75.93 368	98.55 47	94.93 240	89.32 77	94.54 71	97.67 93	78.91 105	97.02 276	93.80 89	97.32 86	98.49 64

jason		92.73 73	92.23 90	94.21 47	90.50 347	87.30 31	98.65 43	95.09 233	90.61 59	92.76 96	97.13 125	75.28 191	97.30 257	93.32 99	96.75 111	98.02 99
jason: jason.
myMVS_eth3d28		92.72 75	92.23 90	94.21 47	96.16 117	87.46 30	97.37 134	96.99 45	88.13 101	88.18 181	95.47 187	84.12 52	98.04 180	92.46 117	91.17 208	97.14 196

ETV-MVS		92.72 75	92.87 71	92.28 155	94.54 179	81.89 166	97.98 81	95.21 230	89.77 72	93.11 89	96.83 139	77.23 139	97.50 228	95.74 62	95.38 139	97.44 168

region2R		92.72 75	92.70 75	92.79 118	98.68 32	80.53 225	97.53 118	96.51 115	85.22 197	91.94 112	97.98 77	77.26 135	99.67 70	90.83 145	98.37 50	98.18 86

reproduce-ours		92.70 78	93.02 66	91.75 196	97.45 87	77.77 328	96.16 245	95.94 178	84.12 238	92.45 97	98.43 42	80.06 88	99.24 105	95.35 69	97.18 90	98.24 82

our_new_method		92.70 78	93.02 66	91.75 196	97.45 87	77.77 328	96.16 245	95.94 178	84.12 238	92.45 97	98.43 42	80.06 88	99.24 105	95.35 69	97.18 90	98.24 82

XVS		92.69 80	92.71 74	92.63 129	98.52 43	80.29 231	97.37 134	96.44 124	87.04 142	91.38 118	97.83 88	77.24 137	99.59 78	90.46 153	98.07 58	98.02 99

ACMMPR		92.69 80	92.67 76	92.75 120	98.66 34	80.57 219	97.58 113	96.69 86	85.20 199	91.57 116	97.92 80	77.01 144	99.67 70	90.95 138	98.41 47	98.00 105

UBG		92.68 82	92.35 84	93.70 70	95.61 140	85.65 59	97.25 140	97.06 40	87.92 106	89.28 155	95.03 213	86.06 36	98.07 178	92.24 119	90.69 216	97.37 174

WTY-MVS		92.65 83	91.68 102	95.56 15	96.00 122	88.90 14	98.23 65	97.65 13	88.57 87	89.82 144	97.22 122	79.29 97	99.06 126	89.57 172	88.73 243	98.73 53

MP-MVS		92.61 84	92.67 76	92.42 144	98.13 62	79.73 255	97.33 137	96.20 153	85.63 184	90.53 133	97.66 94	78.14 120	99.70 65	92.12 122	98.30 54	97.85 119
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		92.58 85	92.35 84	93.29 91	97.30 98	82.53 136	96.44 217	96.04 167	84.68 217	89.12 159	98.37 49	77.48 132	99.74 54	93.31 100	98.38 49	97.59 147
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		92.54 86	92.60 78	92.34 149	98.50 46	79.90 247	98.40 55	96.40 130	84.75 213	90.48 135	98.09 67	77.40 133	99.21 109	91.15 135	98.23 56	97.92 112

reproduce_model		92.53 87	92.87 71	91.50 212	97.41 91	77.14 345	96.02 255	95.91 181	83.65 260	92.45 97	98.39 46	79.75 93	99.21 109	95.27 72	96.98 99	98.14 90

testing11		92.48 88	92.04 97	93.78 62	95.94 126	86.00 45	97.56 115	97.08 38	87.52 121	89.32 154	95.40 190	84.60 43	98.02 182	91.93 128	89.04 238	97.32 179

SymmetryMVS		92.45 89	92.33 86	92.82 117	95.19 156	82.02 155	97.94 84	97.43 17	92.34 32	92.15 106	96.53 150	77.03 142	98.57 149	91.13 136	91.19 206	97.87 116

MTAPA		92.45 89	92.31 87	92.86 113	97.90 67	80.85 209	92.88 384	96.33 141	87.92 106	90.20 140	98.18 58	76.71 152	99.76 46	92.57 115	98.09 57	97.96 111

GST-MVS		92.43 91	92.22 92	93.04 104	98.17 60	81.64 179	97.40 132	96.38 134	84.71 216	90.90 129	97.40 112	77.55 131	99.76 46	89.75 169	97.74 70	97.72 132

fmvsm_s_conf0.1_n_a		92.38 92	92.49 81	92.06 173	88.08 395	81.62 181	97.97 83	96.01 168	90.62 58	96.58 35	98.33 52	74.09 210	99.71 62	97.23 46	93.46 170	94.86 287

MVSMamba_PlusPlus		92.37 93	91.55 105	94.83 28	95.37 148	87.69 25	95.60 292	95.42 217	74.65 408	93.95 78	92.81 283	83.11 63	97.70 201	94.49 81	98.53 39	99.11 29

sasdasda		92.27 94	91.22 111	95.41 18	95.80 132	88.31 16	97.09 160	94.64 266	88.49 89	92.99 92	97.31 114	72.68 230	98.57 149	93.38 97	88.58 250	99.36 17

canonicalmvs		92.27 94	91.22 111	95.41 18	95.80 132	88.31 16	97.09 160	94.64 266	88.49 89	92.99 92	97.31 114	72.68 230	98.57 149	93.38 97	88.58 250	99.36 17

fmvsm_s_conf0.1_n_2		92.26 96	92.48 82	91.60 207	92.29 283	80.55 220	98.73 38	94.33 296	93.80 20	96.18 41	98.11 65	66.93 304	99.75 51	98.19 21	93.74 164	94.50 298

SR-MVS		92.16 97	92.27 88	91.83 194	98.37 51	78.41 300	96.67 201	95.76 191	82.19 293	91.97 110	98.07 71	76.44 156	98.64 145	93.71 92	97.27 87	98.45 67

test_fmvsmvis_n_1920		92.12 98	92.10 95	92.17 165	90.87 338	81.04 196	98.34 61	93.90 332	92.71 28	87.24 199	97.90 83	74.83 198	99.72 59	96.96 50	96.20 121	95.76 260

VNet		92.11 99	91.22 111	94.79 29	96.91 103	86.98 32	97.91 87	97.96 10	86.38 161	93.65 81	95.74 166	70.16 273	98.95 133	93.39 95	88.87 241	98.43 69

CSCG		92.02 100	91.65 103	93.12 100	98.53 42	80.59 216	97.47 123	97.18 29	77.06 386	84.64 244	97.98 77	83.98 55	99.52 87	90.72 147	97.33 85	99.23 25

balanced_ft_v1		92.00 101	91.12 116	94.64 34	96.35 110	86.78 34	94.96 324	94.70 255	87.65 117	90.20 140	93.01 281	69.71 276	98.02 182	97.40 43	96.13 124	99.11 29

MGCFI-Net		91.95 102	91.03 118	94.72 32	95.68 137	86.38 38	96.93 176	94.48 276	88.25 97	92.78 95	97.24 120	72.34 237	98.46 159	93.13 106	88.43 258	99.32 20

PGM-MVS		91.93 103	91.80 100	92.32 153	98.27 56	79.74 254	95.28 303	97.27 22	83.83 252	90.89 130	97.78 90	76.12 167	99.56 84	88.82 187	97.93 65	97.66 138

testing99		91.91 104	91.35 108	93.60 77	95.98 124	85.70 54	97.31 138	96.92 55	86.82 149	88.91 163	95.25 195	84.26 51	97.89 194	88.80 188	87.94 264	97.21 189

testing91		91.90 105	91.31 110	93.66 73	95.99 123	85.68 56	97.39 133	96.89 56	86.75 153	88.85 165	95.23 199	83.93 56	97.90 193	88.91 181	87.89 265	97.41 170

mPP-MVS		91.88 106	91.82 99	92.07 172	98.38 50	78.63 293	97.29 139	96.09 161	85.12 205	88.45 173	97.66 94	75.53 180	99.68 68	89.83 165	98.02 61	97.88 114

EI-MVSNet-Vis-set		91.84 107	91.77 101	92.04 178	97.60 80	81.17 191	96.61 202	96.87 58	88.20 99	89.19 157	97.55 106	78.69 110	99.14 119	90.29 160	90.94 212	95.80 254

EIA-MVS		91.73 108	92.05 96	90.78 246	94.52 180	76.40 357	98.06 77	95.34 222	89.19 79	88.90 164	97.28 119	77.56 130	97.73 200	90.77 146	96.86 106	98.20 84

EC-MVSNet		91.73 108	92.11 94	90.58 250	93.54 217	77.77 328	98.07 76	94.40 288	87.44 125	92.99 92	97.11 127	74.59 204	96.87 293	93.75 91	97.08 96	97.11 197

DP-MVS Recon		91.72 110	90.85 120	94.34 41	99.50 1	85.00 83	98.51 49	95.96 174	80.57 321	88.08 184	97.63 100	76.84 147	99.89 11	85.67 225	94.88 143	98.13 92

CHOSEN 280x420		91.71 111	91.85 98	91.29 222	94.94 167	82.69 133	87.89 443	96.17 156	85.94 177	87.27 198	94.31 244	90.27 9	95.65 352	94.04 87	95.86 132	95.53 268

HY-MVS	84.06 6	91.63 112	90.37 134	95.39 20	96.12 119	88.25 18	90.22 419	97.58 15	88.33 95	90.50 134	91.96 300	79.26 98	99.06 126	90.29 160	89.07 237	98.88 43

HPM-MVS		91.62 113	91.53 106	91.89 185	97.88 69	79.22 269	96.99 166	95.73 194	82.07 295	89.50 153	97.19 123	75.59 178	98.93 136	90.91 140	97.94 63	97.54 151
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_LR		91.60 114	91.64 104	91.47 215	95.74 135	78.79 289	96.15 247	96.77 73	88.49 89	88.64 170	97.07 130	72.33 238	99.19 115	93.13 106	96.48 118	96.43 236

DeepC-MVS	86.58 3	91.53 115	91.06 117	92.94 110	94.52 180	81.89 166	95.95 259	95.98 172	90.76 56	83.76 260	96.76 143	73.24 222	99.71 62	91.67 130	96.96 100	97.22 186
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_yl		91.46 116	90.53 127	94.24 45	97.41 91	85.18 72	98.08 74	97.72 11	80.94 311	89.85 142	96.14 156	75.61 176	98.81 141	90.42 156	88.56 252	98.74 49

DCV-MVSNet		91.46 116	90.53 127	94.24 45	97.41 91	85.18 72	98.08 74	97.72 11	80.94 311	89.85 142	96.14 156	75.61 176	98.81 141	90.42 156	88.56 252	98.74 49

PAPM_NR		91.46 116	90.82 121	93.37 90	98.50 46	81.81 172	95.03 323	96.13 158	84.65 218	86.10 223	97.65 98	79.24 99	99.75 51	83.20 253	96.88 104	98.56 61

testing3-2		91.37 119	91.01 119	92.44 142	95.93 127	83.77 105	98.83 36	97.45 16	86.88 146	86.63 213	94.69 233	84.57 44	97.75 199	89.65 170	84.44 298	95.80 254

MVSFormer		91.36 120	90.57 126	93.73 67	93.00 240	88.08 20	94.80 331	94.48 276	80.74 317	94.90 63	97.13 125	78.84 106	95.10 385	83.77 242	97.46 77	98.02 99

EI-MVSNet-UG-set		91.35 121	91.22 111	91.73 199	97.39 94	80.68 213	96.47 214	96.83 62	87.92 106	88.30 178	97.36 113	77.84 125	99.13 121	89.43 176	89.45 228	95.37 272

SR-MVS-dyc-post		91.29 122	91.45 107	90.80 244	97.76 75	76.03 363	96.20 242	95.44 213	80.56 322	90.72 131	97.84 86	75.76 175	98.61 146	91.99 124	96.79 109	97.75 129

PVSNet_Blended_VisFu		91.24 123	90.77 122	92.66 125	95.09 161	82.40 144	97.77 97	95.87 187	88.26 96	86.39 218	93.94 261	76.77 150	99.27 103	88.80 188	94.00 157	96.31 242

APD-MVS_3200maxsize		91.23 124	91.35 108	90.89 242	97.89 68	76.35 358	96.30 232	95.52 206	79.82 344	91.03 127	97.88 85	74.70 200	98.54 153	92.11 123	96.89 103	97.77 127

diffmvs		91.17 125	90.74 123	92.44 142	93.11 238	82.50 141	96.25 236	93.62 364	87.79 110	90.40 137	95.93 160	73.44 220	97.42 241	93.62 94	92.55 181	97.41 170
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs_mvg		91.13 126	90.45 130	93.17 98	92.99 243	83.58 113	97.46 125	94.56 272	87.69 114	87.19 201	94.98 218	74.50 205	97.60 209	91.88 129	92.79 178	98.34 72
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
testing222		91.09 127	90.49 129	92.87 112	95.82 130	85.04 80	96.51 212	97.28 21	86.05 170	89.13 158	95.34 192	80.16 87	96.62 306	85.82 223	88.31 260	96.96 211

test_fmvsmconf0.01_n		91.08 128	90.68 124	92.29 154	82.43 455	80.12 241	97.94 84	93.93 328	92.07 38	91.97 110	97.60 101	67.56 295	99.53 86	97.09 48	95.56 138	97.21 189

CHOSEN 1792x2688		91.07 129	90.21 140	93.64 74	95.18 158	83.53 114	96.26 235	96.13 158	88.92 81	84.90 237	93.10 279	72.86 226	99.62 76	88.86 182	95.67 135	97.79 126

ETVMVS		90.99 130	90.26 137	93.19 97	95.81 131	85.64 60	96.97 171	97.18 29	85.43 191	88.77 168	94.86 225	82.00 71	96.37 313	82.70 258	88.60 248	97.57 148

CANet_DTU		90.98 131	90.04 147	93.83 60	94.76 173	86.23 42	96.32 230	93.12 391	93.11 25	93.71 80	96.82 141	63.08 335	99.48 91	84.29 235	95.12 141	95.77 259

test2506		90.96 132	90.39 132	92.65 126	93.54 217	82.46 142	96.37 223	97.35 19	86.78 151	87.55 191	95.25 195	77.83 126	97.50 228	84.07 237	94.80 144	97.98 107

thisisatest0515		90.95 133	90.26 137	93.01 105	94.03 206	84.27 98	97.91 87	96.67 88	83.18 268	86.87 211	95.51 184	88.66 17	97.85 195	80.46 278	89.01 239	96.92 215

casdiffmvs		90.95 133	90.39 132	92.63 129	92.82 253	82.53 136	96.83 183	94.47 279	87.69 114	88.47 172	95.56 181	74.04 211	97.54 222	90.90 141	92.74 179	97.83 121
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E3new		90.90 135	90.35 136	92.55 135	93.63 213	82.40 144	96.79 189	94.49 275	87.07 141	88.54 171	95.70 169	73.85 213	97.60 209	91.23 134	91.86 196	97.64 140

sss		90.87 136	89.96 152	93.60 77	94.15 197	83.84 104	97.14 153	98.13 7	85.93 178	89.68 146	96.09 158	71.67 252	99.30 102	87.69 207	89.16 236	97.66 138

diffmvs_AUTHOR		90.86 137	90.41 131	92.24 157	92.01 306	82.22 151	96.18 244	93.64 362	87.28 130	90.46 136	95.64 174	72.82 228	97.39 247	93.17 103	92.46 184	97.11 197

baseline		90.76 138	90.10 143	92.74 121	92.90 251	82.56 135	94.60 334	94.56 272	87.69 114	89.06 161	95.67 172	73.76 215	97.51 227	90.43 155	92.23 192	98.16 88

viewmanbaseed2359cas		90.74 139	90.07 145	92.76 119	92.98 244	82.93 128	96.53 209	94.28 299	87.08 140	88.96 162	95.64 174	72.03 249	97.58 213	90.85 143	92.26 190	97.76 128

Effi-MVS+		90.70 140	89.90 155	93.09 102	93.61 214	83.48 115	95.20 311	92.79 396	83.22 267	91.82 113	95.70 169	71.82 251	97.48 231	91.25 133	93.67 166	98.32 74

viewcassd2359sk11		90.66 141	90.06 146	92.47 138	93.22 230	82.21 152	96.70 199	94.47 279	86.94 144	88.22 180	95.50 185	73.15 223	97.59 211	90.86 142	91.48 200	97.60 146

MAR-MVS		90.63 142	90.22 139	91.86 187	98.47 48	78.20 312	97.18 146	96.61 98	83.87 249	88.18 181	98.18 58	68.71 286	99.75 51	83.66 247	97.15 92	97.63 142
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
MVS		90.60 143	88.64 183	96.50 6	94.25 193	90.53 9	93.33 372	97.21 26	77.59 377	78.88 319	97.31 114	71.52 256	99.69 66	89.60 171	98.03 60	99.27 23

onestephybrid01		90.58 144	90.37 134	91.20 229	92.69 258	78.81 283	96.04 254	93.94 327	86.55 159	90.40 137	95.64 174	72.84 227	97.43 240	93.77 90	91.46 201	97.36 175

xiu_mvs_v1_base_debu		90.54 145	89.54 163	93.55 80	92.31 275	87.58 27	96.99 166	94.87 244	87.23 133	93.27 84	97.56 103	57.43 388	98.32 168	92.72 111	93.46 170	94.74 291

xiu_mvs_v1_base		90.54 145	89.54 163	93.55 80	92.31 275	87.58 27	96.99 166	94.87 244	87.23 133	93.27 84	97.56 103	57.43 388	98.32 168	92.72 111	93.46 170	94.74 291

xiu_mvs_v1_base_debi		90.54 145	89.54 163	93.55 80	92.31 275	87.58 27	96.99 166	94.87 244	87.23 133	93.27 84	97.56 103	57.43 388	98.32 168	92.72 111	93.46 170	94.74 291

hybridnocas07		90.53 148	90.02 148	92.05 177	92.36 272	81.48 184	96.27 233	93.57 369	86.86 148	89.28 155	95.48 186	72.17 242	97.47 232	92.77 110	91.41 203	97.21 189

mvsmamba		90.53 148	90.08 144	91.88 186	94.81 171	80.93 205	93.94 355	94.45 282	88.24 98	87.02 205	92.35 290	68.04 288	95.80 340	94.86 75	97.03 98	98.92 40

Casviewmamba		90.52 150	90.00 150	92.06 173	92.72 257	80.42 229	96.87 180	94.28 299	87.45 123	87.30 196	95.73 167	73.10 224	97.67 205	90.27 163	92.29 189	98.10 95

hybrid		90.42 151	89.87 157	92.06 173	92.20 289	81.45 185	96.09 251	93.61 365	85.80 180	89.55 150	95.52 183	72.14 246	97.39 247	92.60 114	91.36 204	97.34 178

hybridcas		90.40 152	89.67 160	92.60 132	92.39 270	82.32 148	96.83 183	94.25 303	87.19 136	86.59 215	95.43 189	72.54 232	97.65 206	88.77 190	93.02 176	97.82 123

baseline2		90.39 153	90.21 140	90.93 238	90.86 339	80.99 198	95.20 311	97.41 18	86.03 172	80.07 310	94.61 234	90.58 7	97.47 232	87.29 211	89.86 225	94.35 299

ACMMP		90.39 153	89.97 151	91.64 204	97.58 82	78.21 311	96.78 191	96.72 82	84.73 215	84.72 241	97.23 121	71.22 258	99.63 74	88.37 199	92.41 187	97.08 204
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
HPM-MVS_fast		90.38 155	90.17 142	91.03 234	97.61 79	77.35 339	97.15 152	95.48 209	79.51 350	88.79 166	96.90 135	71.64 254	98.81 141	87.01 215	97.44 79	96.94 212

E2		90.33 156	89.65 161	92.37 147	92.66 260	81.99 158	96.58 204	94.39 289	86.71 155	87.88 186	95.25 195	72.18 241	97.56 215	90.37 158	90.88 213	97.57 148

E3		90.33 156	89.65 161	92.37 147	92.64 264	81.99 158	96.58 204	94.39 289	86.71 155	87.87 187	95.27 194	72.17 242	97.56 215	90.37 158	90.88 213	97.57 148

viewmamba		90.30 158	89.90 155	91.48 214	92.14 296	79.76 250	95.92 262	93.50 371	87.73 112	88.32 176	95.82 163	72.39 235	97.36 254	92.19 121	91.12 209	97.30 182

MVS_Test		90.29 159	89.18 170	93.62 76	95.23 152	84.93 84	94.41 337	94.66 263	84.31 231	90.37 139	91.02 314	75.13 193	97.82 196	83.11 255	94.42 151	98.12 93

API-MVS		90.18 160	88.97 176	93.80 61	98.66 34	82.95 127	97.50 122	95.63 200	75.16 403	86.31 219	97.69 92	72.49 234	99.90 9	81.26 274	96.07 126	98.56 61

viewdifsd2359ckpt13		90.08 161	89.36 166	92.26 156	93.03 239	81.90 165	96.37 223	94.34 293	86.16 165	87.44 192	95.30 193	70.93 265	97.55 219	89.05 179	91.59 199	97.35 177

PVSNet_BlendedMVS		90.05 162	89.96 152	90.33 261	97.47 85	83.86 102	98.02 80	96.73 80	87.98 104	89.53 151	89.61 337	76.42 157	99.57 82	94.29 83	79.59 333	87.57 417

ET-MVSNet_ETH3D		90.01 163	89.03 172	92.95 109	94.38 190	86.77 35	98.14 68	96.31 144	89.30 78	63.33 452	96.72 146	90.09 11	93.63 427	90.70 149	82.29 320	98.46 66

viewdifsd2359ckpt09		90.00 164	89.28 169	92.15 167	93.31 228	81.38 186	96.37 223	93.64 362	86.34 162	86.62 214	95.64 174	71.58 255	97.52 225	88.93 180	91.06 210	97.54 151

test_vis1_n_1920		89.95 165	90.59 125	88.03 327	92.36 272	68.98 437	99.12 16	94.34 293	93.86 19	93.64 82	97.01 133	51.54 421	99.59 78	96.76 53	96.71 113	95.53 268

test_cas_vis1_n_1920		89.90 166	90.02 148	89.54 287	90.14 358	74.63 380	98.71 40	94.43 285	93.04 26	92.40 100	96.35 153	53.41 417	99.08 125	95.59 65	96.16 122	94.90 285

viewmacassd2359aftdt		89.89 167	89.01 175	92.52 137	91.56 320	82.46 142	96.32 230	94.06 322	86.41 160	88.11 183	95.01 215	69.68 277	97.47 232	88.73 192	91.19 206	97.63 142

E4		89.85 168	89.06 171	92.22 160	91.88 311	81.63 180	96.43 219	94.27 301	86.32 163	87.29 197	94.97 219	70.81 267	97.52 225	89.57 172	90.00 222	97.51 158

guyue		89.85 168	89.33 168	91.40 218	92.53 269	80.15 240	96.82 186	95.68 196	89.66 73	86.43 217	94.23 247	67.00 302	97.16 267	91.96 127	89.65 226	96.89 216

TESTMET0.1,1		89.83 170	89.34 167	91.31 220	92.54 268	80.19 238	97.11 156	96.57 105	86.15 166	86.85 212	91.83 305	79.32 95	96.95 284	81.30 272	92.35 188	96.77 224

EPP-MVSNet		89.76 171	89.72 159	89.87 278	93.78 209	76.02 365	97.22 141	96.51 115	79.35 352	85.11 233	95.01 215	84.82 41	97.10 274	87.46 210	88.21 262	96.50 234

CPTT-MVS		89.72 172	89.87 157	89.29 290	98.33 53	73.30 392	97.70 103	95.35 221	75.68 398	87.40 193	97.44 110	70.43 270	98.25 171	89.56 174	96.90 102	96.33 241

RRT-MVS		89.67 173	88.67 182	92.67 124	94.44 187	81.08 195	94.34 341	94.45 282	86.05 170	85.79 225	92.39 289	63.39 333	98.16 176	93.22 102	93.95 160	98.76 48

thisisatest0530		89.65 174	89.02 173	91.53 209	93.46 224	80.78 211	96.52 210	96.67 88	81.69 302	83.79 259	94.90 222	88.85 16	97.68 203	77.80 310	87.49 272	96.14 245

3Dnovator+	82.88 8	89.63 175	87.85 204	94.99 24	94.49 186	86.76 36	97.84 91	95.74 193	86.10 168	75.47 367	96.02 159	65.00 320	99.51 89	82.91 257	97.07 97	98.72 54

viewmambaseed2359dif		89.52 176	89.02 173	91.03 234	92.24 288	78.83 280	95.89 272	93.77 350	83.04 272	88.28 179	95.80 165	72.08 247	97.40 245	89.76 168	90.32 218	96.87 219

CDS-MVSNet		89.50 177	88.96 177	91.14 231	91.94 310	80.93 205	97.09 160	95.81 189	84.26 236	84.72 241	94.20 250	80.31 82	95.64 353	83.37 252	88.96 240	96.85 220
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PMMVS		89.46 178	89.92 154	88.06 325	94.64 174	69.57 434	96.22 240	94.95 239	87.27 132	91.37 120	96.54 149	65.88 312	97.39 247	88.54 194	93.89 161	97.23 185

E5new		89.38 179	88.55 187	91.85 189	91.77 316	80.97 199	95.90 268	94.22 307	86.03 172	86.88 207	94.90 222	69.05 281	97.47 232	88.86 182	89.35 229	97.10 199

E5		89.38 179	88.55 187	91.85 189	91.77 316	80.97 199	95.90 268	94.22 307	86.03 172	86.88 207	94.90 222	69.05 281	97.47 232	88.86 182	89.35 229	97.10 199

E6new		89.37 181	88.55 187	91.85 189	91.75 318	80.97 199	95.90 268	94.22 307	86.03 172	86.88 207	94.91 220	69.05 281	97.47 232	88.86 182	89.34 231	97.10 199

E6		89.37 181	88.55 187	91.85 189	91.75 318	80.97 199	95.90 268	94.22 307	86.03 172	86.88 207	94.91 220	69.05 281	97.47 232	88.86 182	89.34 231	97.10 199

HyFIR lowres test		89.36 183	88.60 184	91.63 206	94.91 169	80.76 212	95.60 292	95.53 204	82.56 286	84.03 253	91.24 311	78.03 121	96.81 297	87.07 214	88.41 259	97.32 179

3Dnovator	82.32 10	89.33 184	87.64 209	94.42 39	93.73 212	85.70 54	97.73 101	96.75 77	86.73 154	76.21 356	95.93 160	62.17 340	99.68 68	81.67 267	97.81 67	97.88 114

h-mvs33		89.30 185	88.95 178	90.36 260	95.07 163	76.04 362	96.96 173	97.11 36	90.39 63	92.22 104	95.10 210	74.70 200	98.86 138	93.14 104	65.89 436	96.16 244

LFMVS		89.27 186	87.64 209	94.16 54	97.16 100	85.52 63	97.18 146	94.66 263	79.17 358	89.63 148	96.57 148	55.35 406	98.22 172	89.52 175	89.54 227	98.74 49

MVSTER		89.25 187	88.92 179	90.24 264	95.98 124	84.66 88	96.79 189	95.36 219	87.19 136	80.33 305	90.61 321	90.02 12	95.97 329	85.38 228	78.64 342	90.09 348

dtuplus		89.18 188	88.59 186	90.96 237	91.84 315	78.40 303	95.89 272	93.81 344	83.26 266	87.77 190	95.53 182	70.57 269	97.49 230	88.57 193	90.08 220	96.99 208

KinetiMVS		89.13 189	87.95 202	92.65 126	92.16 294	82.39 146	97.04 164	96.05 165	86.59 158	88.08 184	94.85 226	61.54 352	98.38 165	81.28 273	93.99 159	97.19 193

CostFormer		89.08 190	88.39 193	91.15 230	93.13 236	79.15 272	88.61 435	96.11 160	83.14 269	89.58 149	86.93 381	83.83 58	96.87 293	88.22 200	85.92 287	97.42 169

viewdifsd2359ckpt07		89.04 191	88.30 195	91.27 223	92.32 274	78.90 278	95.89 272	93.77 350	84.48 227	85.18 232	95.16 205	69.83 274	97.70 201	88.75 191	89.29 234	97.22 186

PVSNet	82.34 9	89.02 192	87.79 206	92.71 123	95.49 144	81.50 183	97.70 103	97.29 20	87.76 111	85.47 230	95.12 209	56.90 394	98.90 137	80.33 279	94.02 155	97.71 134

AstraMVS		88.99 193	88.35 194	90.92 239	90.81 342	78.29 304	96.73 194	94.24 304	89.96 69	86.13 222	95.04 212	62.12 345	97.41 243	92.54 116	87.57 271	97.06 206

test-mter		88.95 194	88.60 184	89.98 273	92.26 285	77.23 341	97.11 156	95.96 174	85.32 194	86.30 220	91.38 308	76.37 159	96.78 300	80.82 275	91.92 194	95.94 250

1314		88.94 195	87.20 223	94.17 51	93.21 231	85.73 53	93.33 372	96.64 95	82.89 277	75.98 359	96.36 152	66.83 306	99.39 95	83.52 251	96.02 129	97.39 173

UA-Net		88.92 196	88.48 192	90.24 264	94.06 203	77.18 343	93.04 380	94.66 263	87.39 127	91.09 125	93.89 262	74.92 196	98.18 175	75.83 339	91.43 202	95.35 273

thres200		88.92 196	87.65 208	92.73 122	96.30 112	85.62 61	97.85 90	98.86 1	84.38 230	84.82 238	93.99 259	75.12 194	98.01 184	70.86 385	86.67 276	94.56 297

Vis-MVSNet (Re-imp)		88.88 198	88.87 181	88.91 298	93.89 207	74.43 383	96.93 176	94.19 313	84.39 229	83.22 270	95.67 172	78.24 117	94.70 403	78.88 301	94.40 152	97.61 145

baseline1		88.85 199	87.49 216	92.93 111	95.21 154	86.85 33	95.47 297	94.61 269	87.29 129	83.11 272	94.99 217	80.70 78	96.89 290	82.28 263	73.72 370	95.05 283

AdaColmap		88.81 200	87.61 212	92.39 146	99.33 5	79.95 245	96.70 199	95.58 201	77.51 378	83.05 273	96.69 147	61.90 350	99.72 59	84.29 235	93.47 169	97.50 159

OMC-MVS		88.80 201	88.16 199	90.72 247	95.30 150	77.92 321	94.81 330	94.51 274	86.80 150	84.97 236	96.85 138	67.53 296	98.60 147	85.08 229	87.62 268	95.63 262

114514_t		88.79 202	87.57 214	92.45 140	98.21 59	81.74 174	96.99 166	95.45 212	75.16 403	82.48 276	95.69 171	68.59 287	98.50 155	80.33 279	95.18 140	97.10 199

mvs_anonymous		88.68 203	87.62 211	91.86 187	94.80 172	81.69 177	93.53 367	94.92 241	82.03 296	78.87 320	90.43 324	75.77 174	95.34 366	85.04 230	93.16 174	98.55 63

Vis-MVSNet		88.67 204	87.82 205	91.24 225	92.68 259	78.82 281	96.95 174	93.85 336	87.55 120	87.07 204	95.13 208	63.43 332	97.21 264	77.58 317	96.15 123	97.70 135
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		88.67 204	88.16 199	90.20 266	93.61 214	76.86 348	96.77 193	93.07 392	84.02 242	83.62 263	95.60 179	74.69 203	96.24 320	78.43 305	93.66 167	97.49 160

IB-MVS	85.34 4	88.67 204	87.14 226	93.26 92	93.12 237	84.32 95	98.76 37	97.27 22	87.19 136	79.36 316	90.45 323	83.92 57	98.53 154	84.41 234	69.79 399	96.93 213
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
1112_ss		88.60 207	87.47 218	92.00 180	93.21 231	80.97 199	96.47 214	92.46 399	83.64 261	80.86 298	97.30 117	80.24 84	97.62 208	77.60 316	85.49 292	97.40 172

tttt0517		88.57 208	88.19 198	89.71 284	93.00 240	75.99 366	95.67 287	96.67 88	80.78 316	81.82 289	94.40 243	88.97 15	97.58 213	76.05 337	86.31 280	95.57 266

UWE-MVS		88.56 209	88.91 180	87.50 342	94.17 196	72.19 404	95.82 280	97.05 41	84.96 210	84.78 239	93.51 273	81.33 73	94.75 401	79.43 291	89.17 235	95.57 266

tfpn200view9		88.48 210	87.15 224	92.47 138	96.21 115	85.30 70	97.44 126	98.85 2	83.37 264	83.99 254	93.82 265	75.36 187	97.93 187	69.04 393	86.24 283	94.17 301

test-LLR		88.48 210	87.98 201	89.98 273	92.26 285	77.23 341	97.11 156	95.96 174	83.76 255	86.30 220	91.38 308	72.30 239	96.78 300	80.82 275	91.92 194	95.94 250

TAMVS		88.48 210	87.79 206	90.56 251	91.09 333	79.18 270	96.45 216	95.88 185	83.64 261	83.12 271	93.33 274	75.94 171	95.74 348	82.40 260	88.27 261	96.75 227

thres400		88.42 213	87.15 224	92.23 159	96.21 115	85.30 70	97.44 126	98.85 2	83.37 264	83.99 254	93.82 265	75.36 187	97.93 187	69.04 393	86.24 283	93.45 317

tpmrst		88.36 214	87.38 220	91.31 220	94.36 191	79.92 246	87.32 447	95.26 228	85.32 194	88.34 175	86.13 398	80.60 79	96.70 302	83.78 241	85.34 295	97.30 182

ECVR-MVS		88.35 215	87.25 222	91.65 203	93.54 217	79.40 263	96.56 208	90.78 436	86.78 151	85.57 228	95.25 195	57.25 392	97.56 215	84.73 233	94.80 144	97.98 107

thres100view900		88.30 216	86.95 231	92.33 151	96.10 120	84.90 85	97.14 153	98.85 2	82.69 283	83.41 267	93.66 269	75.43 184	97.93 187	69.04 393	86.24 283	94.17 301

VDD-MVS		88.28 217	87.02 229	92.06 173	95.09 161	80.18 239	97.55 117	94.45 282	83.09 270	89.10 160	95.92 162	47.97 439	98.49 156	93.08 108	86.91 275	97.52 157

BH-w/o		88.24 218	87.47 218	90.54 253	95.03 166	78.54 295	97.41 131	93.82 341	84.08 240	78.23 326	94.51 237	69.34 280	97.21 264	80.21 283	94.58 148	95.87 253

casdiffseed414692147		88.22 219	86.93 233	92.08 170	92.04 304	81.84 169	96.08 253	94.08 320	84.56 221	85.59 227	93.98 260	67.37 298	97.42 241	80.12 285	88.52 254	96.99 208

hse-mvs2		88.22 219	88.21 197	88.25 317	93.54 217	73.41 389	95.41 300	95.89 183	90.39 63	92.22 104	94.22 248	74.70 200	96.66 305	93.14 104	64.37 441	94.69 296

test1111		88.11 221	87.04 228	91.35 219	93.15 234	78.79 289	96.57 206	90.78 436	86.88 146	85.04 234	95.20 202	57.23 393	97.39 247	83.88 239	94.59 147	97.87 116

IMVS_0403		88.07 222	87.02 229	91.24 225	92.30 278	78.81 283	93.62 363	93.84 337	85.14 201	84.36 246	94.49 239	69.49 278	97.46 239	81.33 268	88.61 244	97.46 163

thres600view7		88.06 223	86.70 239	92.15 167	96.10 120	85.17 76	97.14 153	98.85 2	82.70 282	83.41 267	93.66 269	75.43 184	97.82 196	67.13 402	85.88 288	93.45 317

Test_1112_low_res		88.03 224	86.73 236	91.94 184	93.15 234	80.88 208	96.44 217	92.41 403	83.59 263	80.74 300	91.16 312	80.18 85	97.59 211	77.48 319	85.40 293	97.36 175

LuminaMVS		88.02 225	86.89 234	91.43 216	88.65 388	83.16 122	94.84 328	94.41 287	83.67 259	86.56 216	91.95 302	62.04 346	96.88 292	89.78 167	90.06 221	94.24 300

PLC	83.97 7	88.00 226	87.38 220	89.83 280	98.02 65	76.46 354	97.16 150	94.43 285	79.26 357	81.98 286	96.28 154	69.36 279	99.27 103	77.71 314	92.25 191	93.77 311
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CLD-MVS		87.97 227	87.48 217	89.44 288	92.16 294	80.54 224	98.14 68	94.92 241	91.41 46	79.43 315	95.40 190	62.34 339	97.27 260	90.60 150	82.90 312	90.50 338
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Fast-Effi-MVS+		87.93 228	86.94 232	90.92 239	94.04 204	79.16 271	98.26 64	93.72 357	81.29 305	83.94 257	92.90 282	69.83 274	96.68 303	76.70 327	91.74 197	96.93 213

HQP-MVS		87.91 229	87.55 215	88.98 297	92.08 300	78.48 296	97.63 107	94.80 250	90.52 60	82.30 279	94.56 235	65.40 316	97.32 255	87.67 208	83.01 309	91.13 330

IMVS_0407		87.82 230	86.72 237	91.14 231	92.30 278	78.81 283	93.34 371	93.84 337	85.14 201	83.68 261	94.49 239	67.75 291	97.14 272	81.33 268	88.61 244	97.46 163

reproduce_monomvs		87.80 231	87.60 213	88.40 309	96.56 106	80.26 234	95.80 281	96.32 143	91.56 45	73.60 379	88.36 356	88.53 18	96.25 319	90.47 152	67.23 425	88.67 392

0.3-1-1-0.015		87.79 232	85.93 248	93.38 89	89.87 362	85.09 79	98.43 52	96.55 108	81.13 308	87.21 200	89.75 333	77.23 139	97.02 276	86.87 217	66.38 433	98.02 99

test_fmvs1		87.79 232	88.52 191	85.62 379	92.98 244	64.31 459	97.88 89	92.42 402	87.95 105	92.24 103	95.82 163	47.94 440	98.44 163	95.31 71	94.09 153	94.09 305

0.4-1-1-0.2		87.73 234	85.82 251	93.46 88	89.97 361	85.31 69	98.49 51	96.55 108	81.24 306	87.14 202	89.63 336	76.16 165	97.02 276	86.84 218	66.38 433	98.05 97

WBMVS		87.73 234	86.79 235	90.56 251	95.61 140	85.68 56	97.63 107	95.52 206	83.77 254	78.30 325	88.44 355	86.14 35	95.78 342	82.54 259	73.15 377	90.21 343

UGNet		87.73 234	86.55 241	91.27 223	95.16 159	79.11 273	96.35 227	96.23 150	88.14 100	87.83 189	90.48 322	50.65 426	99.09 124	80.13 284	94.03 154	95.60 264
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
FA-MVS(test-final)		87.71 237	86.23 245	92.17 165	94.19 195	80.55 220	87.16 449	96.07 164	82.12 294	85.98 224	88.35 357	72.04 248	98.49 156	80.26 281	89.87 224	97.48 161

SSM_0404		87.69 238	86.26 243	91.95 182	92.94 246	83.02 126	94.69 333	92.33 405	80.11 337	84.65 243	94.18 251	64.68 325	96.90 288	82.34 261	90.44 217	95.94 250

EPNet_dtu		87.65 239	87.89 203	86.93 355	94.57 176	71.37 419	96.72 195	96.50 117	88.56 88	87.12 203	95.02 214	75.91 172	94.01 419	66.62 406	90.00 222	95.42 271
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test1		87.58 240	88.22 196	85.67 377	89.78 364	67.18 445	95.25 308	87.93 459	83.96 245	88.79 166	97.06 131	72.52 233	94.53 409	92.21 120	86.45 279	95.30 275

icg_test_0407_2		87.55 241	86.59 240	90.43 255	92.30 278	78.81 283	92.17 395	93.84 337	85.14 201	83.68 261	94.49 239	67.75 291	95.02 393	81.33 268	88.61 244	97.46 163

0.4-1-1-0.1		87.53 242	85.67 253	93.13 99	89.70 369	84.41 92	98.30 62	96.55 108	80.85 313	86.94 206	89.53 338	76.18 163	96.99 281	86.62 221	66.36 435	97.98 107

HQP_MVS		87.50 243	87.09 227	88.74 302	91.86 312	77.96 318	97.18 146	94.69 259	89.89 70	81.33 292	94.15 253	64.77 323	97.30 257	87.08 212	82.82 313	90.96 332

EPMVS		87.47 244	85.90 249	92.18 164	95.41 146	82.26 150	87.00 450	96.28 145	85.88 179	84.23 249	85.57 405	75.07 195	96.26 317	71.14 383	92.50 182	98.03 98

tpm2		87.35 245	86.26 243	90.62 249	92.93 250	78.67 292	88.06 442	95.99 171	79.33 353	87.40 193	86.43 392	80.28 83	96.40 311	80.23 282	85.73 291	96.79 222

SSM_0407		87.33 246	85.87 250	91.71 202	92.94 246	82.53 136	94.30 344	92.33 405	80.11 337	83.50 264	94.18 251	64.68 325	96.80 299	82.34 261	88.51 255	95.79 256

ab-mvs		87.08 247	84.94 270	93.48 85	93.34 227	83.67 111	88.82 432	95.70 195	81.18 307	84.55 245	90.14 330	62.72 336	98.94 135	85.49 227	82.54 317	97.85 119

SDMVSNet		87.02 248	85.61 254	91.24 225	94.14 198	83.30 119	93.88 357	95.98 172	84.30 233	79.63 313	92.01 296	58.23 373	97.68 203	90.28 162	82.02 321	92.75 321

CNLPA		86.96 249	85.37 259	91.72 201	97.59 81	79.34 266	97.21 142	91.05 431	74.22 410	78.90 318	96.75 145	67.21 301	98.95 133	74.68 353	90.77 215	96.88 218

BH-untuned		86.95 250	85.94 247	89.99 272	94.52 180	77.46 336	96.78 191	93.37 380	81.80 299	76.62 346	93.81 267	66.64 307	97.02 276	76.06 336	93.88 162	95.48 270

QAPM		86.88 251	84.51 274	93.98 55	94.04 204	85.89 49	97.19 145	96.05 165	73.62 415	75.12 370	95.62 178	62.02 347	99.74 54	70.88 384	96.06 127	96.30 243

BH-RMVSNet		86.84 252	85.28 262	91.49 213	95.35 149	80.26 234	96.95 174	92.21 407	82.86 279	81.77 291	95.46 188	59.34 365	97.64 207	69.79 391	93.81 163	96.57 233

PatchmatchNet		86.83 253	85.12 267	91.95 182	94.12 200	82.27 149	86.55 454	95.64 199	84.59 220	82.98 274	84.99 417	77.26 135	95.96 332	68.61 396	91.34 205	97.64 140
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
nrg030		86.79 254	85.43 257	90.87 243	88.76 381	85.34 66	97.06 163	94.33 296	84.31 231	80.45 303	91.98 299	72.36 236	96.36 314	88.48 197	71.13 386	90.93 334

PCF-MVS	84.09 5	86.77 255	85.00 269	92.08 170	92.06 303	83.07 124	92.14 396	94.47 279	79.63 348	76.90 342	94.78 228	71.15 259	99.20 114	72.87 369	91.05 211	93.98 307
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FIs		86.73 256	86.10 246	88.61 305	90.05 359	80.21 236	96.14 248	96.95 51	85.56 188	78.37 324	92.30 291	76.73 151	95.28 370	79.51 289	79.27 336	90.35 340

cascas		86.50 257	84.48 276	92.55 135	92.64 264	85.95 46	97.04 164	95.07 235	75.32 401	80.50 301	91.02 314	54.33 414	97.98 186	86.79 219	87.62 268	93.71 312

VDDNet		86.44 258	84.51 274	92.22 160	91.56 320	81.83 170	97.10 159	94.64 266	69.50 448	87.84 188	95.19 203	48.01 438	97.92 192	89.82 166	86.92 274	96.89 216

viewdifsd2359ckpt11		86.38 259	85.29 260	89.66 286	90.42 349	75.65 372	95.27 306	92.45 400	85.54 189	84.27 248	94.73 229	62.16 341	97.39 247	87.78 204	74.97 364	95.96 247

viewmsd2359difaftdt		86.38 259	85.29 260	89.67 285	90.42 349	75.65 372	95.27 306	92.45 400	85.54 189	84.28 247	94.73 229	62.16 341	97.39 247	87.78 204	74.97 364	95.96 247

GeoE		86.36 261	85.20 263	89.83 280	93.17 233	76.13 360	97.53 118	92.11 408	79.58 349	80.99 295	94.01 256	66.60 308	96.17 324	73.48 365	89.30 233	97.20 192

test_fmvs1_n		86.34 262	86.72 237	85.17 387	87.54 402	63.64 464	96.91 178	92.37 404	87.49 122	91.33 121	95.58 180	40.81 469	98.46 159	95.00 74	93.49 168	93.41 319

TR-MVS		86.30 263	84.93 271	90.42 256	94.63 175	77.58 334	96.57 206	93.82 341	80.30 332	82.42 278	95.16 205	58.74 369	97.55 219	74.88 351	87.82 266	96.13 246

X-MVStestdata		86.26 264	84.14 285	92.63 129	98.52 43	80.29 231	97.37 134	96.44 124	87.04 142	91.38 118	20.73 530	77.24 137	99.59 78	90.46 153	98.07 58	98.02 99

AUN-MVS		86.25 265	85.57 255	88.26 315	93.57 216	73.38 390	95.45 298	95.88 185	83.94 246	85.47 230	94.21 249	73.70 218	96.67 304	83.54 249	64.41 440	94.73 295

OpenMVS	79.58 14	86.09 266	83.62 296	93.50 83	90.95 335	86.71 37	97.44 126	95.83 188	75.35 400	72.64 393	95.72 168	57.42 391	99.64 72	71.41 378	95.85 133	94.13 304

FE-MVS		86.06 267	84.15 284	91.78 195	94.33 192	79.81 248	84.58 467	96.61 98	76.69 392	85.00 235	87.38 372	70.71 268	98.37 166	70.39 388	91.70 198	97.17 195

FC-MVSNet-test		85.96 268	85.39 258	87.66 335	89.38 378	78.02 315	95.65 289	96.87 58	85.12 205	77.34 333	91.94 303	76.28 162	94.74 402	77.09 322	78.82 340	90.21 343

miper_enhance_ethall		85.95 269	85.20 263	88.19 322	94.85 170	79.76 250	96.00 256	94.06 322	82.98 276	77.74 331	88.76 346	79.42 94	95.46 362	80.58 277	72.42 379	89.36 364

OPM-MVS		85.84 270	85.10 268	88.06 325	88.34 392	77.83 325	95.72 283	94.20 312	87.89 109	80.45 303	94.05 255	58.57 370	97.26 261	83.88 239	82.76 315	89.09 372
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		85.80 271	85.20 263	87.59 338	91.55 322	77.41 337	95.13 317	95.36 219	80.43 327	80.33 305	94.71 231	73.72 216	95.97 329	76.96 325	78.64 342	89.39 358

GA-MVS		85.79 272	84.04 287	91.02 236	89.47 376	80.27 233	96.90 179	94.84 248	85.57 186	80.88 296	89.08 341	56.56 398	96.47 310	77.72 313	85.35 294	96.34 239

XVG-OURS-SEG-HR		85.74 273	85.16 266	87.49 344	90.22 353	71.45 417	91.29 408	94.09 319	81.37 304	83.90 258	95.22 200	60.30 358	97.53 224	85.58 226	84.42 300	93.50 315

MonoMVSNet		85.68 274	84.22 282	90.03 270	88.43 391	77.83 325	92.95 383	91.46 421	87.28 130	78.11 327	85.96 400	66.31 311	94.81 399	90.71 148	76.81 353	97.46 163

SCA		85.63 275	83.64 295	91.60 207	92.30 278	81.86 168	92.88 384	95.56 203	84.85 211	82.52 275	85.12 415	58.04 376	95.39 363	73.89 361	87.58 270	97.54 151

Elysia		85.62 276	83.66 292	91.51 210	88.76 381	82.21 152	95.15 315	94.70 255	76.96 388	84.13 250	92.20 293	50.81 424	97.26 261	77.81 308	92.42 185	95.06 281

StellarMVS		85.62 276	83.66 292	91.51 210	88.76 381	82.21 152	95.15 315	94.70 255	76.96 388	84.13 250	92.20 293	50.81 424	97.26 261	77.81 308	92.42 185	95.06 281

test_vis1_n		85.60 278	85.70 252	85.33 384	84.79 436	64.98 456	96.83 183	91.61 420	87.36 128	91.00 128	94.84 227	36.14 476	97.18 266	95.66 63	93.03 175	93.82 310

tpm		85.55 279	84.47 277	88.80 301	90.19 355	75.39 375	88.79 433	94.69 259	84.83 212	83.96 256	85.21 411	78.22 118	94.68 405	76.32 335	78.02 350	96.34 239

UniMVSNet_NR-MVSNet		85.49 280	84.59 273	88.21 321	89.44 377	79.36 264	96.71 197	96.41 128	85.22 197	78.11 327	90.98 316	76.97 146	95.14 382	79.14 297	68.30 413	90.12 346

gg-mvs-nofinetune		85.48 281	82.90 311	93.24 93	94.51 184	85.82 51	79.22 482	96.97 49	61.19 475	87.33 195	53.01 510	90.58 7	96.07 325	86.07 222	97.23 88	97.81 125

VortexMVS		85.45 282	84.40 278	88.63 304	93.25 229	81.66 178	95.39 302	94.34 293	87.15 139	75.10 371	87.65 368	66.58 309	95.19 376	86.89 216	73.21 376	89.03 380

UWE-MVS-28		85.41 283	86.36 242	82.59 422	91.12 332	66.81 450	93.88 357	97.03 42	83.86 251	78.55 321	93.84 264	77.76 128	88.55 470	73.47 366	87.69 267	92.41 325

IMVS_0404		85.34 284	83.69 289	90.29 262	92.30 278	78.81 283	90.62 416	93.84 337	85.14 201	72.51 396	94.49 239	54.36 413	94.61 406	81.33 268	88.61 244	97.46 163

VPA-MVSNet		85.32 285	83.83 288	89.77 283	90.25 352	82.63 134	96.36 226	97.07 39	83.03 274	81.21 294	89.02 343	61.58 351	96.31 316	85.02 231	70.95 388	90.36 339

UniMVSNet (Re)		85.31 286	84.23 281	88.55 306	89.75 366	80.55 220	96.72 195	96.89 56	85.42 192	78.40 323	88.93 344	75.38 186	95.52 360	78.58 303	68.02 416	89.57 357

mamba_0408		85.26 287	83.10 307	91.74 198	92.94 246	82.53 136	72.52 497	91.77 414	80.36 329	83.50 264	94.01 256	64.97 321	96.90 288	79.37 292	88.51 255	95.79 256

XVG-OURS		85.18 288	84.38 279	87.59 338	90.42 349	71.73 414	91.06 412	94.07 321	82.00 297	83.29 269	95.08 211	56.42 399	97.55 219	83.70 246	83.42 305	93.49 316

cl22		85.11 289	84.17 283	87.92 328	95.06 165	78.82 281	95.51 295	94.22 307	79.74 346	76.77 343	87.92 364	75.96 169	95.68 349	79.93 287	72.42 379	89.27 366

usedtu_dtu_shiyan1		85.03 290	83.24 303	90.37 258	86.62 409	86.24 40	96.23 238	95.30 224	84.55 222	77.22 336	88.47 353	67.85 289	95.27 371	76.59 328	76.35 354	89.61 355

FE-MVSNET3		85.03 290	83.24 303	90.37 258	86.62 409	86.24 40	96.23 238	95.30 224	84.55 222	77.22 336	88.47 353	67.85 289	95.27 371	76.59 328	76.35 354	89.61 355

TAPA-MVS	81.61 12	85.02 292	83.67 291	89.06 294	96.79 104	73.27 395	95.92 262	94.79 252	74.81 406	80.47 302	96.83 139	71.07 260	98.19 174	49.82 479	92.57 180	95.71 261
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		85.00 293	83.66 292	89.02 296	95.86 129	74.55 382	92.49 389	93.60 366	79.30 355	79.29 317	91.47 306	58.53 371	98.45 161	70.22 389	92.17 193	94.07 306

PS-MVSNAJss		84.91 294	84.30 280	86.74 356	85.89 424	74.40 384	94.95 325	94.16 315	83.93 247	76.45 349	90.11 331	71.04 261	95.77 343	83.16 254	79.02 339	90.06 350

CVMVSNet		84.83 295	85.57 255	82.63 421	91.55 322	60.38 477	95.13 317	95.03 237	80.60 320	82.10 285	94.71 231	66.40 310	90.19 463	74.30 358	90.32 218	97.31 181

FMVSNet3		84.71 296	82.71 315	90.70 248	94.55 178	87.71 24	95.92 262	94.67 262	81.73 301	75.82 362	88.08 362	66.99 303	94.47 410	71.23 380	75.38 361	89.91 352

VPNet		84.69 297	82.92 310	90.01 271	89.01 380	83.45 116	96.71 197	95.46 211	85.71 183	79.65 312	92.18 295	56.66 397	96.01 328	83.05 256	67.84 419	90.56 337

SSM_04072		84.64 298	83.10 307	89.25 291	92.94 246	82.53 136	72.52 497	91.77 414	80.36 329	83.50 264	94.01 256	64.97 321	89.41 466	79.37 292	88.51 255	95.79 256

dtuonly		84.63 299	84.08 286	86.30 367	86.14 419	69.59 432	92.71 387	90.28 440	82.00 297	80.87 297	94.51 237	62.61 337	96.18 322	79.00 299	88.60 248	93.14 320

sd_testset		84.62 300	83.11 306	89.17 292	94.14 198	77.78 327	91.54 407	94.38 291	84.30 233	79.63 313	92.01 296	52.28 419	96.98 282	77.67 315	82.02 321	92.75 321

Effi-MVS+-dtu		84.61 301	84.90 272	83.72 409	91.96 308	63.14 467	94.95 325	93.34 381	85.57 186	79.79 311	87.12 378	61.99 348	95.61 356	83.55 248	85.83 289	92.41 325

miper_ehance_all_eth		84.57 302	83.60 297	87.50 342	92.64 264	78.25 307	95.40 301	93.47 372	79.28 356	76.41 350	87.64 369	76.53 154	95.24 374	78.58 303	72.42 379	89.01 384

DU-MVS		84.57 302	83.33 302	88.28 314	88.76 381	79.36 264	96.43 219	95.41 218	85.42 192	78.11 327	90.82 317	67.61 293	95.14 382	79.14 297	68.30 413	90.33 341

F-COLMAP		84.50 304	83.44 301	87.67 334	95.22 153	72.22 402	95.95 259	93.78 347	75.74 397	76.30 353	95.18 204	59.50 363	98.45 161	72.67 371	86.59 278	92.35 327

Anonymous202405211		84.41 305	81.93 326	91.85 189	96.78 105	78.41 300	97.44 126	91.34 425	70.29 443	84.06 252	94.26 246	41.09 466	98.96 131	79.46 290	82.65 316	98.17 87

WR-MVS		84.32 306	82.96 309	88.41 308	89.38 378	80.32 230	96.59 203	96.25 148	83.97 244	76.63 345	90.36 325	67.53 296	94.86 397	75.82 340	70.09 397	90.06 350

dp		84.30 307	82.31 320	90.28 263	94.24 194	77.97 317	86.57 453	95.53 204	79.94 343	80.75 299	85.16 413	71.49 257	96.39 312	63.73 423	83.36 306	96.48 235

LPG-MVS_test		84.20 308	83.49 300	86.33 362	90.88 336	73.06 396	95.28 303	94.13 316	82.20 291	76.31 351	93.20 275	54.83 411	96.95 284	83.72 244	80.83 326	88.98 385

dmvs_re		84.10 309	82.90 311	87.70 332	91.41 326	73.28 393	90.59 417	93.19 385	85.02 207	77.96 330	93.68 268	57.92 381	96.18 322	75.50 345	80.87 325	93.63 313

WB-MVSnew		84.08 310	83.51 299	85.80 372	91.34 327	76.69 352	95.62 291	96.27 146	81.77 300	81.81 290	92.81 283	58.23 373	94.70 403	66.66 405	87.06 273	85.99 442

ACMP	81.66 11	84.00 311	83.22 305	86.33 362	91.53 324	72.95 400	95.91 267	93.79 346	83.70 258	73.79 378	92.22 292	54.31 415	96.89 290	83.98 238	79.74 331	89.16 370
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 312	82.80 314	87.31 348	91.46 325	77.39 338	95.66 288	93.43 375	80.44 325	75.51 366	87.26 375	73.72 216	95.16 379	76.99 323	70.72 390	89.39 358
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		83.84 313	82.00 325	89.35 289	87.13 404	81.38 186	95.72 283	94.26 302	80.15 336	75.92 361	90.63 320	61.96 349	96.52 308	78.98 300	73.28 375	90.14 345

c3_l		83.80 314	82.65 316	87.25 350	92.10 299	77.74 332	95.25 308	93.04 393	78.58 367	76.01 358	87.21 377	75.25 192	95.11 384	77.54 318	68.89 407	88.91 390

LCM-MVSNet-Re		83.75 315	83.54 298	84.39 402	93.54 217	64.14 461	92.51 388	84.03 483	83.90 248	66.14 440	86.59 386	67.36 299	92.68 434	84.89 232	92.87 177	96.35 238

ACMM	80.70 13	83.72 316	82.85 313	86.31 365	91.19 329	72.12 406	95.88 275	94.29 298	80.44 325	77.02 340	91.96 300	55.24 407	97.14 272	79.30 295	80.38 328	89.67 354
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm cat1		83.63 317	81.38 334	90.39 257	93.53 222	78.19 313	85.56 461	95.09 233	70.78 441	78.51 322	83.28 433	74.80 199	97.03 275	66.77 404	84.05 301	95.95 249

CR-MVSNet		83.53 318	81.36 335	90.06 269	90.16 356	79.75 252	79.02 484	91.12 428	84.24 237	82.27 283	80.35 457	75.45 182	93.67 426	63.37 427	86.25 281	96.75 227

v2v482		83.46 319	81.86 327	88.25 317	86.19 417	79.65 257	96.34 228	94.02 325	81.56 303	77.32 334	88.23 359	65.62 313	96.03 326	77.77 311	69.72 401	89.09 372

NR-MVSNet		83.35 320	81.52 333	88.84 299	88.76 381	81.31 189	94.45 336	95.16 231	84.65 218	67.81 429	90.82 317	70.36 271	94.87 396	74.75 352	66.89 429	90.33 341

Fast-Effi-MVS+-dtu		83.33 321	82.60 317	85.50 381	89.55 374	69.38 435	96.09 251	91.38 422	82.30 290	75.96 360	91.41 307	56.71 395	95.58 358	75.13 350	84.90 297	91.54 328

cl____		83.27 322	82.12 322	86.74 356	92.20 289	75.95 367	95.11 319	93.27 383	78.44 370	74.82 373	87.02 380	74.19 208	95.19 376	74.67 354	69.32 403	89.09 372

DIV-MVS_self_test		83.27 322	82.12 322	86.74 356	92.19 291	75.92 369	95.11 319	93.26 384	78.44 370	74.81 374	87.08 379	74.19 208	95.19 376	74.66 355	69.30 404	89.11 371

TranMVSNet+NR-MVSNet		83.24 324	81.71 329	87.83 329	87.71 399	78.81 283	96.13 250	94.82 249	84.52 224	76.18 357	90.78 319	64.07 328	94.60 407	74.60 356	66.59 432	90.09 348

Anonymous20240529		83.15 325	80.60 346	90.80 244	95.74 135	78.27 306	96.81 188	94.92 241	60.10 480	81.89 288	92.54 287	45.82 448	98.82 140	79.25 296	78.32 348	95.31 274

eth_miper_zixun_eth		83.12 326	82.01 324	86.47 361	91.85 314	74.80 378	94.33 342	93.18 387	79.11 359	75.74 365	87.25 376	72.71 229	95.32 368	76.78 326	67.13 426	89.27 366

MS-PatchMatch		83.05 327	81.82 328	86.72 360	89.64 371	79.10 274	94.88 327	94.59 271	79.70 347	70.67 411	89.65 335	50.43 428	96.82 296	70.82 387	95.99 131	84.25 457

V42		83.04 328	81.53 332	87.57 340	86.27 416	79.09 275	95.87 276	94.11 318	80.35 331	77.22 336	86.79 384	65.32 318	96.02 327	77.74 312	70.14 393	87.61 416

tpmvs		83.04 328	80.77 342	89.84 279	95.43 145	77.96 318	85.59 460	95.32 223	75.31 402	76.27 354	83.70 428	73.89 212	97.41 243	59.53 443	81.93 323	94.14 303

test_djsdf		83.00 330	82.45 319	84.64 395	84.07 445	69.78 430	94.80 331	94.48 276	80.74 317	75.41 368	87.70 367	61.32 355	95.10 385	83.77 242	79.76 329	89.04 378

v1144		82.90 331	81.27 336	87.78 331	86.29 415	79.07 276	96.14 248	93.93 328	80.05 340	77.38 332	86.80 383	65.50 314	95.93 334	75.21 349	70.13 394	88.33 403

test0.0.03 1		82.79 332	82.48 318	83.74 408	86.81 407	72.22 402	96.52 210	95.03 237	83.76 255	73.00 389	93.20 275	72.30 239	88.88 468	64.15 421	77.52 351	90.12 346

FMVSNet2		82.79 332	80.44 348	89.83 280	92.66 260	85.43 64	95.42 299	94.35 292	79.06 361	74.46 375	87.28 373	56.38 400	94.31 414	69.72 392	74.68 367	89.76 353

D2MVS		82.67 334	81.55 331	86.04 370	87.77 398	76.47 353	95.21 310	96.58 104	82.66 284	70.26 417	85.46 408	60.39 357	95.80 340	76.40 333	79.18 337	85.83 445

MVP-Stereo		82.65 335	81.67 330	85.59 380	86.10 421	78.29 304	93.33 372	92.82 395	77.75 375	69.17 426	87.98 363	59.28 366	95.76 344	71.77 375	96.88 104	82.73 466
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs4		82.54 336	80.79 341	87.79 330	86.11 420	80.49 228	93.55 366	93.18 387	77.29 381	73.35 385	89.40 340	65.26 319	95.05 392	75.32 348	73.61 371	87.83 411

v144192		82.43 337	80.73 343	87.54 341	85.81 425	78.22 308	95.98 257	93.78 347	79.09 360	77.11 339	86.49 388	64.66 327	95.91 335	74.20 359	69.42 402	88.49 397

GBi-Net		82.42 338	80.43 349	88.39 310	92.66 260	81.95 160	94.30 344	93.38 377	79.06 361	75.82 362	85.66 401	56.38 400	93.84 422	71.23 380	75.38 361	89.38 360

test1		82.42 338	80.43 349	88.39 310	92.66 260	81.95 160	94.30 344	93.38 377	79.06 361	75.82 362	85.66 401	56.38 400	93.84 422	71.23 380	75.38 361	89.38 360

v148		82.41 340	80.89 340	86.99 354	86.18 418	76.81 349	96.27 233	93.82 341	80.49 324	75.28 369	86.11 399	67.32 300	95.75 345	75.48 346	67.03 428	88.42 401

v1192		82.31 341	80.55 347	87.60 337	85.94 422	78.47 299	95.85 278	93.80 345	79.33 353	76.97 341	86.51 387	63.33 334	95.87 336	73.11 368	70.13 394	88.46 399

LS3D		82.22 342	79.94 357	89.06 294	97.43 90	74.06 387	93.20 378	92.05 409	61.90 470	73.33 386	95.21 201	59.35 364	99.21 109	54.54 465	92.48 183	93.90 309

jajsoiax		82.12 343	81.15 338	85.03 389	84.19 443	70.70 422	94.22 349	93.95 326	83.07 271	73.48 381	89.75 333	49.66 432	95.37 365	82.24 264	79.76 329	89.02 382

v1921920		82.02 344	80.23 351	87.41 345	85.62 426	77.92 321	95.79 282	93.69 359	78.86 364	76.67 344	86.44 390	62.50 338	95.83 338	72.69 370	69.77 400	88.47 398

myMVS_eth3d		81.93 345	82.18 321	81.18 433	92.13 297	67.18 445	93.97 353	94.23 305	82.43 287	73.39 382	93.57 271	76.98 145	87.86 475	50.53 477	82.34 318	88.51 395

v8		81.88 346	80.06 355	87.32 347	86.63 408	79.04 277	94.41 337	93.65 361	78.77 365	73.19 388	85.57 405	66.87 305	95.81 339	73.84 363	67.61 421	87.11 425

blend_shiyan4		81.76 347	79.58 360	88.31 313	80.00 465	80.59 216	95.95 259	93.73 355	72.26 433	71.14 407	82.52 437	76.13 166	95.15 380	77.83 306	66.62 431	89.19 368

mvs_tets		81.74 348	80.71 344	84.84 390	84.22 442	70.29 426	93.91 356	93.78 347	82.77 281	73.37 384	89.46 339	47.36 444	95.31 369	81.99 265	79.55 335	88.92 389

v1240		81.70 349	79.83 359	87.30 349	85.50 427	77.70 333	95.48 296	93.44 373	78.46 369	76.53 348	86.44 390	60.85 356	95.84 337	71.59 377	70.17 392	88.35 402

PVSNet_0	77.72 15	81.70 349	78.95 368	89.94 276	90.77 343	76.72 351	95.96 258	96.95 51	85.01 208	70.24 419	88.53 351	52.32 418	98.20 173	86.68 220	44.08 495	94.89 286

miper_lstm_enhance		81.66 351	80.66 345	84.67 394	91.19 329	71.97 409	91.94 398	93.19 385	77.86 374	72.27 397	85.26 409	73.46 219	93.42 430	73.71 364	67.05 427	88.61 393

DP-MVS		81.47 352	78.28 371	91.04 233	98.14 61	78.48 296	95.09 322	86.97 464	61.14 476	71.12 408	92.78 286	59.59 361	99.38 96	53.11 469	86.61 277	95.27 277

v10		81.43 353	79.53 362	87.11 352	86.38 412	78.87 279	94.31 343	93.43 375	77.88 373	73.24 387	85.26 409	65.44 315	95.75 345	72.14 374	67.71 420	86.72 429

pmmvs5		81.34 354	79.54 361	86.73 359	85.02 434	76.91 346	96.22 240	91.65 418	77.65 376	73.55 380	88.61 348	55.70 404	94.43 412	74.12 360	73.35 374	88.86 391

SD_0403		81.29 355	81.13 339	81.78 430	90.20 354	60.43 476	89.97 421	91.31 427	83.87 249	71.78 400	93.08 280	63.86 329	89.61 465	60.00 442	86.07 286	95.30 275

ADS-MVSNet		81.26 356	78.36 370	89.96 275	93.78 209	79.78 249	79.48 480	93.60 366	73.09 421	80.14 307	79.99 460	62.15 343	95.24 374	59.49 444	83.52 303	94.85 288

Baseline_NR-MVSNet		81.22 357	80.07 354	84.68 393	85.32 432	75.12 377	96.48 213	88.80 454	76.24 396	77.28 335	86.40 393	67.61 293	94.39 413	75.73 341	66.73 430	84.54 454

tt0805		81.20 358	79.06 367	87.61 336	86.50 411	72.97 399	93.66 361	95.48 209	74.11 411	76.23 355	91.99 298	41.36 465	97.40 245	77.44 320	74.78 366	92.45 324

SSC-MVS3.2		81.06 359	79.49 363	85.75 375	89.78 364	73.00 398	94.40 340	95.23 229	83.76 255	76.61 347	87.82 366	49.48 433	94.88 395	66.80 403	71.56 384	89.38 360

WR-MVS_H		81.02 360	80.09 352	83.79 406	88.08 395	71.26 420	94.46 335	96.54 111	80.08 339	72.81 392	86.82 382	70.36 271	92.65 435	64.18 420	67.50 422	87.46 422

CP-MVSNet		81.01 361	80.08 353	83.79 406	87.91 397	70.51 423	94.29 348	95.65 198	80.83 314	72.54 395	88.84 345	63.71 330	92.32 440	68.58 397	68.36 412	88.55 394

anonymousdsp		80.98 362	79.97 356	84.01 403	81.73 457	70.44 425	92.49 389	93.58 368	77.10 385	72.98 390	86.31 394	57.58 387	94.90 394	79.32 294	78.63 344	86.69 430

UniMVSNet_ETH3D		80.86 363	78.75 369	87.22 351	86.31 414	72.02 407	91.95 397	93.76 352	73.51 416	75.06 372	90.16 329	43.04 457	95.66 350	76.37 334	78.55 345	93.98 307

testing3		80.74 364	81.17 337	79.44 443	91.15 331	63.48 465	97.16 150	95.76 191	80.83 314	71.36 404	93.15 278	78.22 118	87.30 480	43.19 491	79.67 332	87.55 420

IterMVS		80.67 365	79.16 365	85.20 386	89.79 363	76.08 361	92.97 382	91.86 411	80.28 333	71.20 406	85.14 414	57.93 380	91.34 452	72.52 372	70.74 389	88.18 406
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		80.62 366	77.77 376	89.14 293	93.43 225	77.24 340	91.89 399	90.18 441	69.86 447	68.02 428	91.94 303	52.21 420	98.84 139	59.32 446	83.12 307	91.35 329

IterMVS-SCA-FT		80.51 367	79.10 366	84.73 392	89.63 372	74.66 379	92.98 381	91.81 413	80.05 340	71.06 409	85.18 412	58.04 376	91.40 451	72.48 373	70.70 391	88.12 407

PS-CasMVS		80.27 368	79.18 364	83.52 412	87.56 401	69.88 429	94.08 351	95.29 226	80.27 334	72.08 398	88.51 352	59.22 367	92.23 442	67.49 399	68.15 415	88.45 400

pm-mvs1		80.05 369	78.02 374	86.15 368	85.42 428	75.81 370	95.11 319	92.69 398	77.13 383	70.36 413	87.43 371	58.44 372	95.27 371	71.36 379	64.25 442	87.36 423

RPMNet		79.85 370	75.92 390	91.64 204	90.16 356	79.75 252	79.02 484	95.44 213	58.43 487	82.27 283	72.55 488	73.03 225	98.41 164	46.10 486	86.25 281	96.75 227

PatchT		79.75 371	76.85 383	88.42 307	89.55 374	75.49 374	77.37 488	94.61 269	63.07 465	82.46 277	73.32 485	75.52 181	93.41 431	51.36 473	84.43 299	96.36 237

Anonymous20231211		79.72 372	77.19 380	87.33 346	95.59 142	77.16 344	95.18 314	94.18 314	59.31 484	72.57 394	86.20 397	47.89 441	95.66 350	74.53 357	69.24 405	89.18 369

test_fmvs2		79.59 373	79.90 358	78.67 448	82.86 454	55.82 489	95.20 311	89.55 446	81.09 309	80.12 309	89.80 332	34.31 481	93.51 429	87.82 203	78.36 347	86.69 430

ADS-MVSNet2		79.57 374	77.53 377	85.71 376	93.78 209	72.13 405	79.48 480	86.11 472	73.09 421	80.14 307	79.99 460	62.15 343	90.14 464	59.49 444	83.52 303	94.85 288

FMVSNet1		79.50 375	76.54 386	88.39 310	88.47 389	81.95 160	94.30 344	93.38 377	73.14 420	72.04 399	85.66 401	43.86 451	93.84 422	65.48 413	72.53 378	89.38 360

PEN-MVS		79.47 376	78.26 372	83.08 415	86.36 413	68.58 438	93.85 359	94.77 253	79.76 345	71.37 403	88.55 349	59.79 359	92.46 436	64.50 418	65.40 437	88.19 405

XVG-ACMP-BASELINE		79.38 377	77.90 375	83.81 405	84.98 435	67.14 449	89.03 431	93.18 387	80.26 335	72.87 391	88.15 361	38.55 471	96.26 317	76.05 337	78.05 349	88.02 408

v7n		79.32 378	77.34 378	85.28 385	84.05 446	72.89 401	93.38 369	93.87 334	75.02 405	70.68 410	84.37 421	59.58 362	95.62 355	67.60 398	67.50 422	87.32 424

MIMVSNet		79.18 379	75.99 389	88.72 303	87.37 403	80.66 214	79.96 478	91.82 412	77.38 380	74.33 376	81.87 446	41.78 461	90.74 458	66.36 411	83.10 308	94.76 290

JIA-IIPM		79.00 380	77.20 379	84.40 401	89.74 368	64.06 462	75.30 492	95.44 213	62.15 469	81.90 287	59.08 504	78.92 104	95.59 357	66.51 409	85.78 290	93.54 314

wanda-best-256-512		78.87 381	75.75 391	88.22 319	79.74 466	80.51 226	95.92 262	93.75 353	72.60 426	70.34 414	82.14 438	57.91 382	95.09 387	75.61 342	53.77 469	89.05 375

FE-blended-shiyan7		78.87 381	75.75 391	88.22 319	79.74 466	80.51 226	95.92 262	93.75 353	72.60 426	70.34 414	82.14 438	57.91 382	95.09 387	75.61 342	53.77 469	89.05 375

blended_shiyan8		78.76 383	75.65 395	88.10 323	79.58 471	80.20 237	95.70 286	93.71 358	72.43 431	70.26 417	82.12 441	57.66 386	95.08 389	75.57 344	53.80 468	89.02 382

blended_shiyan6		78.74 384	75.63 396	88.07 324	79.63 470	80.10 242	95.72 283	93.73 355	72.43 431	70.17 420	82.09 443	57.69 385	95.07 390	75.47 347	53.77 469	89.03 380

gbinet_0.2-2-1-0.02		78.67 385	75.67 394	87.70 332	80.38 463	79.60 259	96.25 236	94.03 324	72.51 429	71.41 402	83.33 432	55.97 403	94.45 411	73.37 367	53.73 473	89.04 378

USDC		78.65 386	76.25 387	85.85 371	87.58 400	74.60 381	89.58 425	90.58 439	84.05 241	63.13 453	88.23 359	40.69 470	96.86 295	66.57 408	75.81 359	86.09 439

DTE-MVSNet		78.37 387	77.06 381	82.32 426	85.22 433	67.17 448	93.40 368	93.66 360	78.71 366	70.53 412	88.29 358	59.06 368	92.23 442	61.38 434	63.28 446	87.56 418

Patchmatch-test		78.25 388	74.72 403	88.83 300	91.20 328	74.10 386	73.91 495	88.70 457	59.89 481	66.82 435	85.12 415	78.38 114	94.54 408	48.84 482	79.58 334	97.86 118

tfpnnormal		78.14 389	75.42 397	86.31 365	88.33 393	79.24 267	94.41 337	96.22 151	73.51 416	69.81 422	85.52 407	55.43 405	95.75 345	47.65 484	67.86 418	83.95 460

mmtdpeth		78.04 390	76.76 384	81.86 429	89.60 373	66.12 453	92.34 394	87.18 463	76.83 390	85.55 229	76.49 476	46.77 445	97.02 276	90.85 143	45.24 492	82.43 470

ACMH	75.40 17	77.99 391	74.96 399	87.10 353	90.67 344	76.41 356	93.19 379	91.64 419	72.47 430	63.44 451	87.61 370	43.34 454	97.16 267	58.34 449	73.94 369	87.72 412
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	73.68 18	77.99 391	75.74 393	84.74 391	90.45 348	72.02 407	86.41 455	91.12 428	72.57 428	66.63 437	87.27 374	54.95 410	96.98 282	56.29 459	75.98 356	85.21 449
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
Syy-MVS		77.97 393	78.05 373	77.74 452	92.13 297	56.85 485	93.97 353	94.23 305	82.43 287	73.39 382	93.57 271	57.95 379	87.86 475	32.40 504	82.34 318	88.51 395

our_test_3		77.90 394	75.37 398	85.48 382	85.39 429	76.74 350	93.63 362	91.67 417	73.39 419	65.72 442	84.65 420	58.20 375	93.13 433	57.82 451	67.87 417	86.57 432

RPSCF		77.73 395	76.63 385	81.06 434	88.66 387	55.76 490	87.77 444	87.88 460	64.82 462	74.14 377	92.79 285	49.22 434	96.81 297	67.47 400	76.88 352	90.62 336

KD-MVS_2432*1600		77.63 396	74.92 401	85.77 373	90.86 339	79.44 261	88.08 440	93.92 330	76.26 394	67.05 433	82.78 435	72.15 244	91.92 445	61.53 431	41.62 498	85.94 443

miper_refine_blended		77.63 396	74.92 401	85.77 373	90.86 339	79.44 261	88.08 440	93.92 330	76.26 394	67.05 433	82.78 435	72.15 244	91.92 445	61.53 431	41.62 498	85.94 443

usedtu_blend_shiyan5		77.51 398	73.93 412	88.26 315	79.74 466	80.59 216	90.76 415	89.69 444	63.21 464	70.34 414	82.14 438	57.91 382	95.15 380	77.83 306	53.77 469	89.05 375

ACMH+	76.62 16	77.47 399	74.94 400	85.05 388	91.07 334	71.58 416	93.26 376	90.01 442	71.80 436	64.76 446	88.55 349	41.62 462	96.48 309	62.35 430	71.00 387	87.09 426

Patchmtry		77.36 400	74.59 404	85.67 377	89.75 366	75.75 371	77.85 487	91.12 428	60.28 478	71.23 405	80.35 457	75.45 182	93.56 428	57.94 450	67.34 424	87.68 414

ppachtmachnet_test		77.19 401	74.22 408	86.13 369	85.39 429	78.22 308	93.98 352	91.36 424	71.74 437	67.11 432	84.87 418	56.67 396	93.37 432	52.21 470	64.59 439	86.80 428

OurMVSNet-221017-0		77.18 402	76.06 388	80.55 437	83.78 449	60.00 479	90.35 418	91.05 431	77.01 387	66.62 438	87.92 364	47.73 442	94.03 418	71.63 376	68.44 411	87.62 415

TransMVSNet (Re)		76.94 403	74.38 406	84.62 396	85.92 423	75.25 376	95.28 303	89.18 451	73.88 414	67.22 430	86.46 389	59.64 360	94.10 417	59.24 447	52.57 478	84.50 455

EU-MVSNet		76.92 404	76.95 382	76.83 458	84.10 444	54.73 492	91.77 402	92.71 397	72.74 424	69.57 423	88.69 347	58.03 378	87.43 479	64.91 416	70.00 398	88.33 403

Patchmatch-RL test		76.65 405	74.01 411	84.55 397	77.37 480	64.23 460	78.49 486	82.84 488	78.48 368	64.63 447	73.40 484	76.05 168	91.70 450	76.99 323	57.84 456	97.72 132

FMVSNet5		76.46 406	74.16 409	83.35 414	90.05 359	76.17 359	89.58 425	89.85 443	71.39 439	65.29 445	80.42 456	50.61 427	87.70 478	61.05 437	69.24 405	86.18 437

SixPastTwentyTwo		76.04 407	74.32 407	81.22 432	84.54 438	61.43 474	91.16 410	89.30 450	77.89 372	64.04 448	86.31 394	48.23 436	94.29 415	63.54 426	63.84 444	87.93 410

AllTest		75.92 408	73.06 416	84.47 398	92.18 292	67.29 443	91.07 411	84.43 478	67.63 453	63.48 449	90.18 327	38.20 472	97.16 267	57.04 455	73.37 372	88.97 387

CL-MVSNet_self_test		75.81 409	74.14 410	80.83 436	78.33 476	67.79 442	94.22 349	93.52 370	77.28 382	69.82 421	81.54 449	61.47 354	89.22 467	57.59 453	53.51 474	85.48 447

COLMAP_ROB	73.24 19	75.74 410	73.00 417	83.94 404	92.38 271	69.08 436	91.85 401	86.93 465	61.48 473	65.32 444	90.27 326	42.27 459	96.93 287	50.91 475	75.63 360	85.80 446
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CMPMVS	54.94 21	75.71 411	74.56 405	79.17 445	79.69 469	55.98 487	89.59 424	93.30 382	60.28 478	53.85 486	89.07 342	47.68 443	96.33 315	76.55 330	81.02 324	85.22 448
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		75.29 412	73.64 413	80.22 439	80.75 459	63.38 466	93.36 370	90.71 438	73.09 421	67.12 431	83.70 428	50.33 429	90.85 457	53.63 468	70.10 396	86.44 433

EG-PatchMatch MVS		74.92 413	72.02 421	83.62 410	83.76 451	73.28 393	93.62 363	92.04 410	68.57 451	58.88 474	83.80 427	31.87 486	95.57 359	56.97 457	78.67 341	82.00 475

testgi		74.88 414	73.40 414	79.32 444	80.13 464	61.75 471	93.21 377	86.64 469	79.49 351	66.56 439	91.06 313	35.51 479	88.67 469	56.79 458	71.25 385	87.56 418

pmmvs6		74.65 415	71.67 422	83.60 411	79.13 473	69.94 428	93.31 375	90.88 435	61.05 477	65.83 441	84.15 424	43.43 453	94.83 398	66.62 406	60.63 451	86.02 441

test_vis1_rt		73.96 416	72.40 419	78.64 449	83.91 447	61.16 475	95.63 290	68.18 505	76.32 393	60.09 469	74.77 479	29.01 492	97.54 222	87.74 206	75.94 357	77.22 488

FE-MVSNET2		73.72 417	70.80 427	82.46 423	74.97 489	73.81 388	91.88 400	91.73 416	76.70 391	59.74 472	77.41 470	42.26 460	90.52 460	64.75 417	57.79 457	83.06 462

K. test v3		73.62 418	71.59 423	79.69 441	82.98 453	59.85 480	90.85 414	88.83 453	77.13 383	58.90 473	82.11 442	43.62 452	91.72 449	65.83 412	54.10 467	87.50 421

pmmvs-eth3d		73.59 419	70.66 428	82.38 424	76.40 484	73.38 390	89.39 429	89.43 448	72.69 425	60.34 468	77.79 467	46.43 447	91.26 454	66.42 410	57.06 458	82.51 467

kuosan		73.55 420	72.39 420	77.01 456	89.68 370	66.72 451	85.24 464	93.44 373	67.76 452	60.04 470	83.40 431	71.90 250	84.25 489	45.34 488	54.75 462	80.06 484

MDA-MVSNet_test_wron		73.54 421	70.43 430	82.86 417	84.55 437	71.85 411	91.74 403	91.32 426	67.63 453	46.73 494	81.09 453	55.11 408	90.42 462	55.91 461	59.76 452	86.31 435

YYNet1		73.53 422	70.43 430	82.85 418	84.52 439	71.73 414	91.69 404	91.37 423	67.63 453	46.79 493	81.21 452	55.04 409	90.43 461	55.93 460	59.70 453	86.38 434

UnsupCasMVSNet_eth		73.25 423	70.57 429	81.30 431	77.53 478	66.33 452	87.24 448	93.89 333	80.38 328	57.90 478	81.59 447	42.91 458	90.56 459	65.18 415	48.51 486	87.01 427

DSMNet-mixed		73.13 424	72.45 418	75.19 465	77.51 479	46.82 497	85.09 465	82.01 490	67.61 457	69.27 425	81.33 451	50.89 423	86.28 483	54.54 465	83.80 302	92.46 323

OpenMVS_ROB	68.52 20	73.02 425	69.57 433	83.37 413	80.54 462	71.82 412	93.60 365	88.22 458	62.37 468	61.98 460	83.15 434	35.31 480	95.47 361	45.08 489	75.88 358	82.82 464

test_0402		72.68 426	69.54 434	82.09 427	88.67 386	71.81 413	92.72 386	86.77 468	61.52 472	62.21 459	83.91 426	43.22 455	93.76 425	34.60 500	72.23 382	80.72 483

dtuonlycased		72.49 427	71.58 424	75.22 464	81.04 458	64.71 457	92.43 391	86.46 470	75.62 399	59.79 471	78.43 465	48.54 435	85.84 485	63.66 425	58.28 454	75.10 490

TinyColmap		72.41 428	68.99 437	82.68 419	88.11 394	69.59 432	88.41 436	85.20 474	65.55 459	57.91 477	84.82 419	30.80 488	95.94 333	51.38 472	68.70 408	82.49 469

sc_t1		72.37 429	68.03 440	85.39 383	83.78 449	70.51 423	91.27 409	83.70 485	52.46 493	68.29 427	82.02 444	30.58 489	94.81 399	64.50 418	55.69 460	90.85 335

test20.03		72.36 430	71.15 425	75.98 462	77.79 477	59.16 481	92.40 392	89.35 449	74.09 412	61.50 463	84.32 422	48.09 437	85.54 487	50.63 476	62.15 449	83.24 461

LF4IMVS		72.36 430	70.82 426	76.95 457	79.18 472	56.33 486	86.12 457	86.11 472	69.30 449	63.06 454	86.66 385	33.03 484	92.25 441	65.33 414	68.64 409	82.28 471

Anonymous20240521		72.06 432	69.91 432	78.50 450	77.11 481	61.67 473	91.62 406	90.97 433	65.52 460	62.37 458	79.05 463	36.32 475	90.96 456	57.75 452	68.52 410	82.87 463

dmvs_testset		72.00 433	73.36 415	67.91 472	83.83 448	31.90 517	85.30 463	77.12 497	82.80 280	63.05 455	92.46 288	61.54 352	82.55 494	42.22 494	71.89 383	89.29 365

MDA-MVSNet-bldmvs		71.45 434	67.94 441	81.98 428	85.33 431	68.50 439	92.35 393	88.76 455	70.40 442	42.99 497	81.96 445	46.57 446	91.31 453	48.75 483	54.39 466	86.11 438

mvs5depth		71.40 435	68.36 439	80.54 438	75.31 488	65.56 455	79.94 479	85.14 475	69.11 450	71.75 401	81.59 447	41.02 467	93.94 420	60.90 438	50.46 481	82.10 472

MVS-HIRNet		71.36 436	67.00 442	84.46 400	90.58 345	69.74 431	79.15 483	87.74 461	46.09 497	61.96 461	50.50 511	45.14 449	95.64 353	53.74 467	88.11 263	88.00 409

KD-MVS_self_test		70.97 437	69.31 435	75.95 463	76.24 486	55.39 491	87.45 445	90.94 434	70.20 445	62.96 456	77.48 469	44.01 450	88.09 473	61.25 435	53.26 475	84.37 456

tt0320		70.21 438	66.07 446	82.64 420	83.42 452	70.82 421	89.63 423	84.10 481	49.75 496	62.71 457	77.28 471	33.35 482	92.45 438	58.78 448	55.62 461	84.64 453

tt0320-xc		69.70 439	65.27 451	82.99 416	84.33 440	71.92 410	89.56 427	82.08 489	50.11 494	61.87 462	77.50 468	30.48 490	92.34 439	60.30 440	51.20 480	84.71 452

ttmdpeth		69.58 440	66.92 444	77.54 454	75.95 487	62.40 469	88.09 439	84.32 480	62.87 467	65.70 443	86.25 396	36.53 474	88.53 471	55.65 463	46.96 491	81.70 478

test_fmvs3		69.56 441	69.19 436	70.67 469	69.01 497	47.05 496	90.87 413	86.81 466	71.31 440	66.79 436	77.15 472	16.40 500	83.17 492	81.84 266	62.51 448	81.79 477

dongtai		69.47 442	68.98 438	70.93 468	86.87 406	58.45 482	88.19 438	93.18 387	63.98 463	56.04 482	80.17 459	70.97 264	79.24 496	33.46 502	47.94 488	75.09 491

MIMVSNet1		69.44 443	66.65 445	77.84 451	76.48 483	62.84 468	87.42 446	88.97 452	66.96 458	57.75 480	79.72 462	32.77 485	85.83 486	46.32 485	63.42 445	84.85 451

PM-MVS		69.32 444	66.93 443	76.49 459	73.60 492	55.84 488	85.91 458	79.32 495	74.72 407	61.09 465	78.18 466	21.76 496	91.10 455	70.86 385	56.90 459	82.51 467

FE-MVSNET		69.26 445	66.03 447	78.93 446	73.82 491	68.33 440	89.65 422	84.06 482	70.21 444	57.79 479	76.94 475	41.48 464	86.98 482	45.85 487	54.51 465	81.48 480

TDRefinement		69.20 446	65.78 449	79.48 442	66.04 502	62.21 470	88.21 437	86.12 471	62.92 466	61.03 466	85.61 404	33.23 483	94.16 416	55.82 462	53.02 476	82.08 473

new-patchmatchnet		68.85 447	65.93 448	77.61 453	73.57 493	63.94 463	90.11 420	88.73 456	71.62 438	55.08 484	73.60 483	40.84 468	87.22 481	51.35 474	48.49 487	81.67 479

UnsupCasMVSNet_bld		68.60 448	64.50 452	80.92 435	74.63 490	67.80 441	83.97 469	92.94 394	65.12 461	54.63 485	68.23 495	35.97 477	92.17 444	60.13 441	44.83 493	82.78 465

mvsany_test3		67.19 449	65.34 450	72.72 467	63.08 504	48.57 495	83.12 472	78.09 496	72.07 434	61.21 464	77.11 473	22.94 495	87.78 477	78.59 302	51.88 479	81.80 476

MVStest1		66.93 450	63.01 454	78.69 447	78.56 474	71.43 418	85.51 462	86.81 466	49.79 495	48.57 492	84.15 424	53.46 416	83.31 490	43.14 492	37.15 501	81.34 481

new_pmnet		66.18 451	63.18 453	75.18 466	76.27 485	61.74 472	83.79 470	84.66 477	56.64 489	51.57 489	71.85 491	31.29 487	87.93 474	49.98 478	62.55 447	75.86 489

pmmvs3		65.75 452	62.18 455	76.45 460	67.12 501	64.54 458	88.68 434	85.05 476	54.77 491	57.54 481	73.79 482	29.40 491	86.21 484	55.49 464	47.77 489	78.62 486

usedtu_dtu_shiyan2		64.65 453	60.40 457	77.38 455	64.24 503	57.84 484	89.16 430	87.60 462	52.95 492	53.43 487	71.31 494	23.41 494	88.27 472	51.95 471	49.58 483	86.03 440

test_f		64.01 454	62.13 456	69.65 470	63.00 505	45.30 503	83.66 471	80.68 492	61.30 474	55.70 483	72.62 487	14.23 502	84.64 488	69.84 390	58.11 455	79.00 485

N_pmnet		61.30 455	60.20 458	64.60 478	84.32 441	17.00 532	91.67 405	10.98 530	61.77 471	58.45 476	78.55 464	49.89 431	91.83 448	42.27 493	63.94 443	84.97 450

ArgMatch-SfM		60.14 456	57.35 459	68.50 471	71.14 495	45.17 504	80.16 477	63.06 509	59.74 483	51.33 490	80.81 454	11.74 507	78.30 497	61.13 436	37.05 502	82.04 474

ArgMatch-Sym		59.60 457	56.89 460	67.74 473	71.40 494	45.64 502	81.24 476	58.34 513	58.65 486	52.79 488	81.51 450	11.35 509	76.76 501	60.83 439	35.86 503	80.81 482

WB-MVS		57.26 458	56.22 461	60.39 485	69.29 496	35.91 513	86.39 456	70.06 503	59.84 482	46.46 495	72.71 486	51.18 422	78.11 498	15.19 520	34.89 504	67.14 498

test_method		56.77 459	54.53 463	63.49 480	76.49 482	40.70 507	75.68 491	74.24 499	19.47 517	48.73 491	71.89 490	19.31 497	65.80 512	57.46 454	47.51 490	83.97 459

APD_test1		56.56 460	53.58 464	65.50 475	67.93 500	46.51 499	77.24 490	72.95 500	38.09 499	42.75 498	75.17 478	13.38 503	82.78 493	40.19 496	54.53 464	67.23 497

SSC-MVS		56.01 461	54.96 462	59.17 486	68.42 498	34.13 514	84.98 466	69.23 504	58.08 488	45.36 496	71.67 492	50.30 430	77.46 499	14.28 521	32.33 505	65.91 500

FPMVS		55.09 462	52.93 465	61.57 482	55.98 509	40.51 508	83.11 473	83.41 487	37.61 500	34.95 502	71.95 489	14.40 501	76.95 500	29.81 505	65.16 438	67.25 496

test_vis3_rt		54.10 463	51.04 466	63.27 481	58.16 508	46.08 501	84.17 468	49.32 519	56.48 490	36.56 501	49.48 514	8.03 512	91.91 447	67.29 401	49.87 482	51.82 513

LCM-MVSNet		52.52 464	48.24 467	65.35 476	47.63 520	41.45 506	72.55 496	83.62 486	31.75 504	37.66 500	57.92 506	9.19 511	76.76 501	49.26 480	44.60 494	77.84 487

EGC-MVSNET		52.46 465	47.56 468	67.15 474	81.98 456	60.11 478	82.54 474	72.44 501	0.11 551	0.70 553	74.59 480	25.11 493	83.26 491	29.04 506	61.51 450	58.09 505

PMMVS2		50.90 466	46.31 469	64.67 477	55.53 510	46.67 498	77.30 489	71.02 502	40.89 498	34.16 503	59.32 503	9.83 510	76.14 504	40.09 497	28.63 507	71.21 493

ANet_high		46.22 467	41.28 474	61.04 483	39.91 526	46.25 500	70.59 499	76.18 498	58.87 485	23.09 517	48.00 516	12.58 505	66.54 511	28.65 508	13.62 518	70.35 494

testf1		45.70 468	42.41 470	55.58 488	53.29 513	40.02 509	68.96 500	62.67 510	27.45 508	29.85 507	61.58 501	5.98 514	73.83 507	28.49 509	43.46 496	52.90 509

APD_test2		45.70 468	42.41 470	55.58 488	53.29 513	40.02 509	68.96 500	62.67 510	27.45 508	29.85 507	61.58 501	5.98 514	73.83 507	28.49 509	43.46 496	52.90 509

LoFTR		45.13 470	39.91 475	60.78 484	58.50 507	33.07 515	59.69 507	57.64 514	30.48 506	25.92 513	63.30 498	4.30 516	74.96 505	28.23 512	31.12 506	74.31 492

Gipuma		45.11 471	42.05 472	54.30 490	80.69 460	51.30 494	35.80 516	83.81 484	28.13 507	27.94 510	34.53 519	11.41 508	76.70 503	21.45 515	54.65 463	34.90 519
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DenseAffine		43.98 472	39.51 476	57.39 487	60.41 506	37.29 511	67.44 502	34.50 520	35.36 502	31.38 505	65.55 497	4.21 517	67.77 510	35.59 499	21.11 510	67.10 499

tmp_tt		41.54 473	41.93 473	40.38 500	20.10 541	26.84 522	61.93 505	59.09 512	14.81 521	28.51 509	80.58 455	35.53 478	48.33 522	63.70 424	13.11 520	45.96 518

RoMa-SfM		40.68 474	36.49 477	53.24 492	52.27 516	33.01 516	62.88 504	23.78 525	32.85 503	31.33 506	67.39 496	3.87 518	64.89 513	33.77 501	20.24 512	61.82 503

MatchFormer		39.45 475	34.61 479	54.00 491	53.28 515	28.79 521	58.06 510	51.35 518	21.48 513	23.10 516	55.83 508	3.50 521	70.37 509	19.01 517	25.84 508	62.84 501

PMVS	34.80 23	39.19 476	35.53 478	50.18 494	29.72 529	30.30 519	59.60 508	66.20 508	26.06 510	17.91 521	49.53 513	3.12 522	74.09 506	18.19 519	49.40 484	46.14 516
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DKM		38.02 477	33.59 481	51.32 493	50.45 518	30.46 518	61.04 506	19.18 526	30.65 505	26.88 511	61.89 500	2.55 527	61.16 514	32.68 503	16.95 513	62.34 502

PDCNetPlus		37.10 478	34.54 480	44.76 496	50.06 519	29.19 520	58.72 509	23.89 524	37.05 501	24.11 515	58.95 505	6.11 513	55.29 516	40.76 495	11.21 529	49.81 514

MVE	35.65 22	33.85 479	29.49 486	46.92 495	41.86 523	36.28 512	50.45 513	56.52 515	18.75 518	18.28 519	37.84 518	2.41 530	58.41 515	18.71 518	20.62 511	46.06 517
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MASt3R-SfM		33.79 480	32.03 483	39.08 501	30.86 528	18.05 531	44.70 514	25.59 523	21.32 514	31.97 504	71.52 493	3.78 519	38.14 525	35.97 498	22.58 509	61.06 504

RoMa-HiRes		33.28 481	29.63 485	44.22 498	41.01 524	25.30 525	51.82 512	14.13 527	25.85 512	26.34 512	61.96 499	2.78 525	54.52 518	28.42 511	14.36 514	52.83 512

DKM-HiRes		32.92 482	29.13 487	44.31 497	42.93 521	25.35 524	53.22 511	13.26 528	25.92 511	24.31 514	57.58 507	1.88 536	50.95 521	28.87 507	14.19 515	56.63 508

E-PMN		32.70 483	32.39 482	33.65 504	53.35 512	25.70 523	74.07 494	53.33 516	21.08 515	17.17 522	33.63 521	11.85 506	54.84 517	12.98 523	14.04 516	20.42 524

EMVS		31.70 484	31.45 484	32.48 505	50.72 517	23.95 526	74.78 493	52.30 517	20.36 516	16.08 523	31.48 522	12.80 504	53.60 519	11.39 524	13.10 521	19.88 526

ELoFTR		28.06 485	23.17 489	42.73 499	26.41 536	16.73 533	32.43 518	29.00 521	18.06 519	18.03 520	50.11 512	1.10 538	53.50 520	21.73 514	11.65 528	57.96 506

PMatch-SfM		26.26 486	22.21 490	38.43 503	28.29 533	16.65 534	37.61 515	8.91 534	18.02 520	18.64 518	53.32 509	0.55 550	41.01 524	24.74 513	9.79 531	57.63 507

GLUNet-SfM		23.82 487	18.93 491	38.50 502	29.22 530	15.72 535	24.44 525	26.94 522	12.76 523	13.93 525	40.99 517	2.01 535	46.93 523	13.88 522	6.19 541	52.85 511

PMatch-Up-SfM		21.53 488	18.34 492	31.10 506	23.05 537	12.66 536	29.81 521	5.63 541	13.87 522	16.04 524	48.08 515	0.39 554	31.11 526	21.09 516	7.09 538	49.53 515

cdsmvs_eth3d_5k		21.43 489	28.57 488	0.00 534	0.00 558	0.00 560	0.00 545	95.93 180	0.00 552	0.00 554	97.66 94	63.57 331	0.00 554	0.00 552	0.00 552	0.00 549

ALIKED-LG		17.53 490	16.82 493	19.64 507	42.07 522	19.09 528	31.53 519	11.93 529	7.76 524	10.68 527	26.90 525	3.52 520	22.14 527	3.10 533	13.89 517	17.68 527

ALIKED-MNN		16.35 491	15.48 495	18.95 508	40.20 525	19.09 528	30.16 520	10.63 532	6.03 525	9.48 529	24.90 527	2.59 526	21.29 528	2.88 535	12.46 523	16.48 528

ALIKED-NN		16.22 492	15.63 494	17.99 509	39.36 527	18.31 530	29.26 522	10.71 531	5.97 526	10.10 528	26.06 526	2.80 524	20.08 529	2.91 534	13.46 519	15.60 529

wuyk23d		14.10 493	13.89 496	14.72 510	55.23 511	22.91 527	33.83 517	3.56 547	4.94 527	4.11 536	2.28 551	2.06 534	19.66 530	10.23 525	8.74 533	1.59 548

SP-LightGlue		12.02 494	12.06 499	11.90 511	28.59 531	6.58 544	24.58 524	7.89 537	3.94 531	6.94 533	17.94 532	2.45 528	7.82 534	3.96 529	12.26 524	21.30 520

SP-SuperGlue		12.00 495	12.07 498	11.81 512	28.37 532	6.58 544	24.63 523	8.02 536	3.99 530	7.02 532	18.00 531	2.44 529	7.72 536	3.95 530	12.19 525	21.13 522

SP-DiffGlue		11.69 496	11.68 501	11.70 514	11.01 553	7.08 543	18.35 528	8.44 535	4.41 528	11.18 526	28.64 524	2.84 523	7.44 537	7.44 526	12.85 522	20.56 523

SP-MNN		11.64 497	11.60 502	11.74 513	27.48 534	6.11 550	24.23 526	7.72 538	3.40 534	6.22 535	17.81 534	2.13 532	7.94 533	3.69 532	11.73 527	21.18 521

SP-NN		11.53 498	11.59 503	11.38 515	27.20 535	6.14 549	24.02 527	7.42 540	3.57 532	6.38 534	17.94 532	2.17 531	7.78 535	3.71 531	11.86 526	20.23 525

XFeat-MNN		10.03 499	9.79 505	10.74 516	9.46 554	6.05 551	16.60 529	9.52 533	4.29 529	8.53 531	22.45 528	2.10 533	13.28 531	5.47 527	9.68 532	12.89 530

testmvs		9.92 500	12.94 497	0.84 533	0.65 556	0.29 559	93.78 360	0.39 557	0.42 549	2.85 542	15.84 535	0.17 556	0.30 553	2.18 536	0.21 550	1.91 547

XFeat-NN		9.17 501	9.18 506	9.14 517	8.78 555	5.26 553	15.30 530	7.57 539	3.56 533	8.63 530	22.05 529	1.87 537	11.03 532	4.95 528	9.92 530	11.13 531

test123		9.07 502	11.73 500	1.11 532	0.50 557	0.77 558	89.44 428	0.20 558	0.34 550	2.15 548	10.72 541	0.34 555	0.32 552	1.79 537	0.08 551	2.23 546

ab-mvs-re		8.11 503	10.81 504	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	97.30 117	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

SIFT-NN		7.34 504	7.57 508	6.67 518	22.83 538	8.78 537	12.92 531	4.04 543	2.52 535	3.88 537	11.56 536	0.86 539	6.16 538	0.95 538	8.56 534	5.09 532

SIFT-MNN		6.97 505	7.12 509	6.51 519	21.26 539	8.28 538	11.89 532	4.05 542	2.50 536	3.39 539	11.27 537	0.76 540	6.14 539	0.95 538	8.05 536	5.09 532

SIFT-NN-NCMNet		6.77 506	6.92 510	6.30 520	19.98 542	8.05 539	11.79 533	3.97 544	2.43 538	3.43 538	10.93 538	0.75 541	5.95 541	0.88 540	8.15 535	4.90 534

SIFT-NCM-Cal		6.46 507	6.58 511	6.10 521	20.43 540	7.62 540	11.15 535	3.59 545	2.40 541	2.33 547	10.33 544	0.68 545	6.03 540	0.77 546	7.51 537	4.64 538

SIFT-NN-CMatch		6.23 508	6.33 512	5.94 522	18.10 546	7.22 542	10.34 536	3.54 548	2.42 539	3.36 540	10.93 538	0.72 543	5.71 543	0.87 541	6.67 540	4.89 535

SIFT-NN-UMatch		6.11 509	6.25 513	5.68 524	17.01 548	6.50 546	11.20 534	3.58 546	2.44 537	2.68 543	10.88 540	0.74 542	5.70 544	0.87 541	6.85 539	4.82 536

SIFT-ConvMatch		6.05 510	6.14 514	5.78 523	19.43 543	7.31 541	9.58 539	3.30 549	2.42 539	2.67 544	10.54 542	0.65 546	5.73 542	0.83 544	5.84 543	4.29 539

pcd_1.5k_mvsjas		5.92 511	7.89 507	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	71.04 261	0.00 554	0.00 552	0.00 552	0.00 549

SIFT-UMatch		5.86 512	6.01 515	5.38 525	18.70 544	6.22 548	10.07 537	3.07 551	2.39 542	2.42 545	10.54 542	0.63 548	5.65 545	0.84 543	5.49 544	4.28 540

SIFT-NN-PointCN		5.63 513	5.80 516	5.10 527	16.00 549	5.22 554	10.00 538	3.21 550	2.26 545	2.92 541	10.15 545	0.72 543	5.35 547	0.81 545	6.14 542	4.74 537

SIFT-CM-Cal		5.56 514	5.66 517	5.26 526	18.45 545	6.34 547	8.44 541	2.81 552	2.36 543	2.42 545	9.99 547	0.64 547	5.41 546	0.74 548	5.05 545	4.02 541

SIFT-UM-Cal		5.40 515	5.58 518	4.87 528	18.00 547	5.37 552	9.03 540	2.49 554	2.33 544	2.14 549	10.11 546	0.60 549	5.27 548	0.77 546	4.78 547	3.95 542

SIFT-PointCN		4.77 516	4.97 519	4.17 530	15.53 551	3.97 555	8.20 542	2.62 553	2.10 546	1.91 551	8.44 549	0.47 552	4.70 550	0.67 550	4.79 546	3.85 544

SIFT-PCN-Cal		4.71 517	4.89 520	4.18 529	15.70 550	3.90 556	7.58 543	2.37 555	2.09 547	1.95 550	8.68 548	0.51 551	4.71 549	0.68 549	4.45 548	3.93 543

SIFT-NCMNet		4.03 518	4.21 521	3.50 531	14.53 552	3.56 557	6.14 544	1.51 556	2.08 548	1.72 552	7.39 550	0.42 553	4.00 551	0.57 551	3.56 549	2.93 545

mmdepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

monomultidepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

test_blank		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet_test		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

DCPMVS		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

sosnet-low-res		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

sosnet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uncertanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

Regformer		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

test-260524					99.01 23	85.87 50		96.82 65		95.25 54		86.23 34	99.92 7	97.87 33	98.71 31

MED-MVS test				94.20 50	99.06 11	83.70 108	98.35 57	97.14 31	87.45 123	97.03 27	98.90 6		99.96 4	97.78 36	98.60 36	98.94 38

TestfortrainingZip				97.22 3	99.48 2	91.93 7	98.35 57	97.26 24	85.61 185	99.54 1	99.26 1	91.36 5	99.98 2		96.55 116	99.73 3

WAC-MVS						67.18 445								49.00 481

FOURS1					98.51 45	78.01 316	98.13 71	96.21 152	83.04 272	94.39 72

MSC_two_6792asdad				97.14 4	99.05 14	92.19 4		96.83 62					99.81 29	98.08 26	98.81 24	99.43 12

PC_three_1452									91.12 50	98.33 5	98.42 44	92.51 2	99.81 29	98.96 6	99.37 1	99.70 4

No_MVS				97.14 4	99.05 14	92.19 4		96.83 62					99.81 29	98.08 26	98.81 24	99.43 12

test_one_0601					98.91 24	84.56 91		96.70 84	88.06 102	96.57 36	98.77 16	88.04 23

eth-test2					0.00 558

eth-test					0.00 558

ZD-MVS					99.09 10	83.22 121		96.60 101	82.88 278	93.61 83	98.06 72	82.93 65	99.14 119	95.51 67	98.49 43

RE-MVS-def			91.18 115		97.76 75	76.03 363	96.20 242	95.44 213	80.56 322	90.72 131	97.84 86	73.36 221		91.99 124	96.79 109	97.75 129

IU-MVS					99.03 20	85.34 66		96.86 60	92.05 41	98.74 2				98.15 22	98.97 17	99.42 14

OPU-MVS				97.30 2	99.19 8	92.31 3	99.12 16				98.54 30	92.06 3	99.84 19	99.11 5	99.37 1	99.74 1

test_241102_TWO								96.78 67	88.72 84	97.70 14	98.91 3	87.86 24	99.82 25	98.15 22	99.00 15	99.47 10

test_241102_ONE					99.03 20	85.03 81		96.78 67	88.72 84	97.79 11	98.90 6	88.48 19	99.82 25

9.14			94.26 42		98.10 63		98.14 68	96.52 114	84.74 214	94.83 66	98.80 13	82.80 67	99.37 98	95.95 59	98.42 46

save fliter					98.24 57	83.34 118	98.61 46	96.57 105	91.32 47

test_0728_THIRD									88.38 92	96.69 31	98.76 18	89.64 14	99.76 46	97.47 41	98.84 23	99.38 15

test_0728_SECOND				95.14 21	99.04 19	86.14 43	99.06 23	96.77 73					99.84 19	97.90 30	98.85 21	99.45 11

test0726					99.05 14	85.18 72	99.11 19	96.78 67	88.75 82	97.65 18	98.91 3	87.69 25

GSMVS																97.54 151

test_part2					98.90 25	85.14 78				96.07 43

sam_mvs1												77.59 129				97.54 151

sam_mvs												75.35 189

ambc				76.02 461	68.11 499	51.43 493	64.97 503	89.59 445		60.49 467	74.49 481	17.17 499	92.46 436	61.50 433	52.85 477	84.17 458

MTGPA								96.33 141

test_post1							85.88 459				30.24 523	73.77 214	95.07 390	73.89 361

test_post											33.80 520	76.17 164	95.97 329

patchmatchnet-post											77.09 474	77.78 127	95.39 363

GG-mvs-BLEND				93.49 84	94.94 167	86.26 39	81.62 475	97.00 44		88.32 176	94.30 245	91.23 6	96.21 321	88.49 196	97.43 80	98.00 105

MTMP							97.53 118	68.16 506

gm-plane-assit					92.27 284	79.64 258			84.47 228		95.15 207		97.93 187	85.81 224

test9_res														96.00 58	99.03 13	98.31 76

TEST9					98.64 37	83.71 106	97.82 92	96.65 92	84.29 235	95.16 56	98.09 67	84.39 46	99.36 99

test_8					98.63 39	83.64 112	97.81 94	96.63 97	84.50 225	95.10 59	98.11 65	84.33 47	99.23 107

agg_prior2														94.30 82	99.00 15	98.57 60

agg_prior					98.59 41	83.13 123		96.56 107		94.19 74			99.16 118

TestCases				84.47 398	92.18 292	67.29 443		84.43 478	67.63 453	63.48 449	90.18 327	38.20 472	97.16 267	57.04 455	73.37 372	88.97 387

test_prior4						82.34 147	97.75 100

test_prior2							98.37 56		86.08 169	94.57 70	98.02 73	83.14 62		95.05 73	98.79 27

test_prior				93.09 102	98.68 32	81.91 164		96.40 130					99.06 126			98.29 78

旧先验2							96.97 171		74.06 413	96.10 42			97.76 198	88.38 198

新几何2							96.42 221

新几何1				93.12 100	97.44 89	81.60 182		96.71 83	74.54 409	91.22 124	97.57 102	79.13 101	99.51 89	77.40 321	98.46 44	98.26 81

旧先验1					97.39 94	79.58 260		96.54 111			98.08 70	84.00 54			97.42 81	97.62 144

无先验							96.87 180	96.78 67	77.39 379				99.52 87	79.95 286		98.43 69

原ACMM2							96.84 182

原ACMM1				91.22 228	97.77 73	78.10 314		96.61 98	81.05 310	91.28 123	97.42 111	77.92 124	98.98 130	79.85 288	98.51 40	96.59 232

test222					96.15 118	78.41 300	95.87 276	96.46 122	71.97 435	89.66 147	97.45 107	76.33 160			98.24 55	98.30 77

testdata2													99.48 91	76.45 332

segment_acmp												82.69 68

testdata				90.13 267	95.92 128	74.17 385		96.49 120	73.49 418	94.82 67	97.99 74	78.80 108	97.93 187	83.53 250	97.52 76	98.29 78

testdata1							95.57 294		87.44 125

test12				94.25 44	98.34 52	85.55 62		96.35 140		92.36 101		80.84 76	99.22 108		98.31 53	97.98 107

plane_prior7					91.86 312	77.55 335

plane_prior6					91.98 307	77.92 321						64.77 323

plane_prior5								94.69 259					97.30 257	87.08 212	82.82 313	90.96 332

plane_prior4											94.15 253

plane_prior3						77.75 331			90.17 67	81.33 292

plane_prior2							97.18 146		89.89 70

plane_prior1					91.95 309

plane_prior						77.96 318	97.52 121		90.36 65						82.96 311

n2								0.00 559

nn								0.00 559

door-mid								79.75 494

lessismore_v0				79.98 440	80.59 461	58.34 483		80.87 491		58.49 475	83.46 430	43.10 456	93.89 421	63.11 428	48.68 485	87.72 412

LGP-MVS_train				86.33 362	90.88 336	73.06 396		94.13 316	82.20 291	76.31 351	93.20 275	54.83 411	96.95 284	83.72 244	80.83 326	88.98 385

test11								96.50 117

door								80.13 493

HQP5-MVS						78.48 296

HQP-NCC					92.08 300		97.63 107		90.52 60	82.30 279

ACMP_Plane					92.08 300		97.63 107		90.52 60	82.30 279

BP-MVS														87.67 208

HQP4-MVS										82.30 279			97.32 255			91.13 330

HQP3-MVS								94.80 250							83.01 309

HQP2-MVS												65.40 316

NP-MVS					92.04 304	78.22 308					94.56 235

MDTV_nov1_ep13_2view						81.74 174	86.80 451		80.65 319	85.65 226		74.26 207		76.52 331		96.98 210

MDTV_nov1_ep13			83.69 289		94.09 202	81.01 197	86.78 452	96.09 161	83.81 253	84.75 240	84.32 422	74.44 206	96.54 307	63.88 422	85.07 296

ACMMP++_ref															78.45 346

ACMMP++															79.05 338

Test By Simon												71.65 253

ITE_SJBPF				82.38 424	87.00 405	65.59 454		89.55 446	79.99 342	69.37 424	91.30 310	41.60 463	95.33 367	62.86 429	74.63 368	86.24 436

DeepMVS_CX				64.06 479	78.53 475	43.26 505		68.11 507	69.94 446	38.55 499	76.14 477	18.53 498	79.34 495	43.72 490	41.62 498	69.57 495

High-resolution multi-view benchmark