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.

LCM-MVSNet		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 2

UA-Net		97.35 4	97.24 15	97.69 5	98.22 83	93.87 33	98.42 6	98.19 61	96.95 18	95.46 182	99.23 9	93.45 104	99.57 14	95.34 45	99.89 2	99.63 12

PS-CasMVS		96.69 27	97.43 9	94.49 144	99.13 6	84.09 226	96.61 37	97.97 104	97.91 8	98.64 16	98.13 45	95.24 44	99.65 4	93.39 95	99.84 3	99.72 4

WR-MVS_H		96.60 32	97.05 20	95.24 102	99.02 13	86.44 175	96.78 28	98.08 82	97.42 12	98.48 19	97.86 73	91.76 158	99.63 7	94.23 63	99.84 3	99.66 9

FC-MVSNet-test		95.32 93	95.88 74	93.62 182	98.49 65	81.77 272	95.90 81	98.32 39	93.93 69	97.53 50	97.56 95	88.48 233	99.40 51	92.91 113	99.83 5	99.68 7

PEN-MVS		96.69 27	97.39 12	94.61 133	99.16 4	84.50 215	96.54 39	98.05 89	98.06 7	98.64 16	98.25 42	95.01 59	99.65 4	92.95 112	99.83 5	99.68 7

DTE-MVSNet		96.74 24	97.43 9	94.67 130	99.13 6	84.68 214	96.51 41	97.94 112	98.14 6	98.67 15	98.32 39	95.04 56	99.69 3	93.27 100	99.82 7	99.62 13

CP-MVSNet		96.19 52	96.80 23	94.38 149	98.99 19	83.82 229	96.31 61	97.53 164	97.60 10	98.34 22	97.52 100	91.98 152	99.63 7	93.08 108	99.81 8	99.70 5

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 29	98.81 32	93.86 34	99.07 2	98.98 8	97.01 17	98.92 5	98.78 19	95.22 46	98.61 194	96.85 11	99.77 9	99.31 33
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
tt0320-xc		97.00 12	97.67 5	94.98 112	98.89 23	86.94 160	96.72 31	98.46 25	98.28 4	98.86 7	99.43 4	96.80 10	98.51 217	91.79 148	99.76 10	99.50 19

v7n		96.82 16	97.31 14	95.33 96	98.54 55	86.81 163	96.83 24	98.07 85	96.59 25	98.46 20	98.43 37	92.91 127	99.52 19	96.25 21	99.76 10	99.65 11

TranMVSNet+NR-MVSNet		96.07 57	96.26 48	95.50 87	98.26 80	87.69 141	93.75 177	97.86 122	95.96 41	97.48 54	97.14 145	95.33 40	99.44 33	90.79 179	99.76 10	99.38 28

Anonymous20231211		96.60 32	97.13 19	95.00 111	97.46 143	86.35 179	97.11 18	98.24 54	97.58 11	98.72 11	98.97 12	93.15 116	99.15 98	93.18 103	99.74 13	99.50 19

UniMVSNet_ETH3D		97.13 10	97.72 3	95.35 94	99.51 2	87.38 146	97.70 8	97.54 161	98.16 5	98.94 3	99.33 6	97.84 4	99.08 110	90.73 181	99.73 14	99.59 15

sc_t1		97.21 9	97.71 4	95.71 78	99.06 10	88.89 111	96.72 31	97.79 135	98.34 2	98.97 2	99.40 5	96.81 9	98.79 159	92.58 126	99.72 15	99.45 23

tt0320		96.97 13	97.64 6	94.96 114	98.89 23	86.86 162	96.85 23	98.45 26	98.29 3	98.88 6	99.45 3	96.48 13	98.54 211	91.73 151	99.72 15	99.47 21

pmmvs6		96.80 19	97.36 13	95.15 108	99.12 8	87.82 139	96.68 33	97.86 122	96.10 36	98.14 30	99.28 8	97.94 3	98.21 256	91.38 164	99.69 17	99.42 24

FIs		94.90 114	95.35 102	93.55 186	98.28 78	81.76 273	95.33 106	98.14 72	93.05 88	97.07 80	97.18 140	87.65 252	99.29 81	91.72 152	99.69 17	99.61 14

OurMVSNet-221017-0		96.80 19	96.75 24	96.96 38	99.03 12	91.85 60	97.98 7	98.01 99	94.15 64	98.93 4	99.07 10	88.07 242	99.57 14	95.86 27	99.69 17	99.46 22

Anonymous20240521		92.86 220	93.57 195	90.74 337	96.57 214	75.50 409	94.15 159	95.60 291	89.38 202	95.90 152	97.90 72	80.39 340	97.96 295	92.60 125	99.68 20	98.75 113

ANet_high		94.83 117	96.28 47	90.47 346	96.65 202	73.16 429	94.33 150	98.74 13	96.39 30	98.09 33	98.93 13	93.37 108	98.70 180	90.38 192	99.68 20	99.53 17

DeepC-MVS	91.39 4	95.43 86	95.33 105	95.71 78	97.67 129	90.17 87	93.86 174	98.02 98	87.35 263	96.22 133	97.99 58	94.48 83	99.05 117	92.73 120	99.68 20	97.93 221
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NR-MVSNet		95.28 97	95.28 108	95.26 100	97.75 119	87.21 150	95.08 120	97.37 176	93.92 71	97.65 42	95.90 251	90.10 211	99.33 76	90.11 210	99.66 23	99.26 35

Baseline_NR-MVSNet		94.47 140	95.09 122	92.60 242	98.50 64	80.82 293	92.08 262	96.68 244	93.82 72	96.29 127	98.56 29	90.10 211	97.75 321	90.10 212	99.66 23	99.24 39

UniMVSNet (Re)		95.32 93	95.15 112	95.80 74	97.79 117	88.91 110	92.91 217	98.07 85	93.46 80	96.31 125	95.97 250	90.14 208	99.34 71	92.11 135	99.64 25	99.16 45

WR-MVS		93.49 188	93.72 185	92.80 227	97.57 136	80.03 307	90.14 341	95.68 289	93.70 74	96.62 107	95.39 286	87.21 260	99.04 120	87.50 286	99.64 25	99.33 31

MIMVSNet1		95.52 82	95.45 94	95.72 77	99.14 5	89.02 108	96.23 68	96.87 226	93.73 73	97.87 35	98.49 33	90.73 195	99.05 117	86.43 309	99.60 27	99.10 56

ACMH+	88.43 11	96.48 37	96.82 22	95.47 89	98.54 55	89.06 107	95.65 91	98.61 15	96.10 36	98.16 29	97.52 100	96.90 7	98.62 193	90.30 199	99.60 27	98.72 118

VPA-MVSNet		95.14 104	95.67 86	93.58 185	97.76 118	83.15 244	94.58 141	97.58 157	93.39 81	97.05 83	98.04 52	93.25 112	98.51 217	89.75 222	99.59 29	99.08 57

LPG-MVS_test		96.38 46	96.23 49	96.84 41	98.36 75	92.13 55	95.33 106	98.25 46	91.78 128	97.07 80	97.22 136	96.38 16	99.28 85	92.07 138	99.59 29	99.11 53

LGP-MVS_train				96.84 41	98.36 75	92.13 55		98.25 46	91.78 128	97.07 80	97.22 136	96.38 16	99.28 85	92.07 138	99.59 29	99.11 53

ACMH	88.36 12	96.59 34	97.43 9	94.07 160	98.56 49	85.33 206	96.33 54	98.30 42	94.66 54	98.72 11	98.30 40	97.51 5	98.00 291	94.87 50	99.59 29	98.86 93
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_NR-MVSNet		95.35 91	95.21 110	95.76 75	97.69 127	88.59 120	92.26 258	97.84 126	94.91 52	96.80 97	95.78 261	90.42 200	99.41 43	91.60 156	99.58 33	99.29 34

DU-MVS		95.28 97	95.12 118	95.75 76	97.75 119	88.59 120	92.58 235	97.81 131	93.99 66	96.80 97	95.90 251	90.10 211	99.41 43	91.60 156	99.58 33	99.26 35

MM		94.41 143	94.14 169	95.22 105	95.84 289	87.21 150	94.31 152	90.92 405	94.48 58	92.80 309	97.52 100	85.27 290	99.49 29	96.58 17	99.57 35	98.97 72

ACMP	88.15 13	95.71 74	95.43 97	96.54 48	98.17 86	91.73 63	94.24 154	98.08 82	89.46 200	96.61 108	96.47 202	95.85 22	99.12 104	90.45 189	99.56 36	98.77 112
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v10		94.68 125	95.27 109	92.90 221	96.57 214	80.15 301	94.65 138	97.57 158	90.68 168	97.43 56	98.00 55	88.18 239	99.15 98	94.84 51	99.55 37	99.41 26

MGCNet		92.88 217	92.27 243	94.69 128	92.35 407	86.03 188	92.88 219	89.68 413	90.53 174	91.52 346	96.43 205	82.52 320	99.32 77	95.01 48	99.54 38	98.71 121

PS-MVSNAJss		96.01 58	96.04 63	95.89 71	98.82 30	88.51 123	95.57 97	97.88 119	88.72 220	98.81 9	98.86 15	90.77 191	99.60 9	95.43 40	99.53 39	99.57 16

TDRefinement		97.68 3	97.60 8	97.93 2	99.02 13	95.95 8	98.61 3	98.81 10	97.41 13	97.28 70	98.46 35	94.62 76	98.84 148	94.64 53	99.53 39	98.99 65

IS-MVSNet		94.49 139	94.35 160	94.92 115	98.25 82	86.46 174	97.13 17	94.31 335	96.24 34	96.28 129	96.36 216	82.88 312	99.35 68	88.19 269	99.52 41	98.96 76

SSC-MVS3.2		89.88 314	91.06 277	86.31 431	95.90 285	63.76 479	82.68 472	92.43 382	91.42 147	92.37 328	94.58 320	86.34 276	96.60 391	84.35 341	99.50 42	98.57 144

nrg030		96.32 47	96.55 32	95.62 81	97.83 113	88.55 122	95.77 86	98.29 45	92.68 90	98.03 34	97.91 70	95.13 49	98.95 134	93.85 72	99.49 43	99.36 30

MP-MVS-pluss		96.08 56	95.92 71	96.57 47	99.06 10	91.21 68	93.25 197	98.32 39	87.89 250	96.86 92	97.38 114	95.55 30	99.39 54	95.47 38	99.47 44	99.11 53
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mvs_tets		96.83 15	96.71 25	97.17 30	98.83 29	92.51 51	96.58 38	97.61 152	87.57 260	98.80 10	98.90 14	96.50 12	99.59 13	96.15 22	99.47 44	99.40 27

v8		94.65 126	95.29 107	92.74 230	96.65 202	79.77 317	94.59 139	97.17 197	91.86 120	97.47 55	97.93 62	88.16 240	99.08 110	94.32 60	99.47 44	99.38 28

CLD-MVS		91.82 257	91.41 268	93.04 211	96.37 235	83.65 231	86.82 422	97.29 188	84.65 331	92.27 333	89.67 430	92.20 148	97.85 308	83.95 346	99.47 44	97.62 261
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SPE-MVS-test		95.32 93	95.10 121	95.96 62	96.86 184	90.75 80	96.33 54	99.20 4	93.99 66	91.03 358	93.73 353	93.52 101	99.55 18	91.81 147	99.45 48	97.58 265

jajsoiax		96.59 34	96.42 36	97.12 32	98.76 35	92.49 52	96.44 48	97.42 173	86.96 275	98.71 13	98.72 22	95.36 38	99.56 17	95.92 25	99.45 48	99.32 32

test_djsdf		96.62 30	96.49 33	97.01 35	98.55 53	91.77 62	97.15 15	97.37 176	88.98 212	98.26 26	98.86 15	93.35 109	99.60 9	96.41 18	99.45 48	99.66 9

FE-MVSNET2		94.07 166	94.47 153	92.90 221	97.45 145	81.26 284	93.58 185	97.54 161	88.28 238	96.46 114	97.92 67	91.41 170	98.74 170	88.12 273	99.44 51	98.69 125

CP-MVS		96.44 41	96.08 60	97.54 14	98.29 77	94.62 17	96.80 26	98.08 82	92.67 92	95.08 213	96.39 213	94.77 72	99.42 37	93.17 104	99.44 51	98.58 143

COLMAP_ROB	91.06 5	96.75 23	96.62 29	97.13 31	98.38 70	94.31 20	96.79 27	98.32 39	96.69 21	96.86 92	97.56 95	95.48 31	98.77 166	90.11 210	99.44 51	98.31 174
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
NormalMVS		94.10 163	93.36 203	96.31 55	99.01 15	90.84 76	94.70 134	97.90 114	90.98 157	93.22 289	95.73 264	78.94 350	99.12 104	90.38 192	99.42 54	98.97 72

lecture		97.32 6	97.64 6	96.33 54	99.01 15	90.77 79	96.90 21	98.60 16	96.30 33	97.74 40	98.00 55	96.87 8	99.39 54	95.95 24	99.42 54	98.84 97

test_0728_THIRD									93.26 84	97.40 61	97.35 121	94.69 73	99.34 71	93.88 70	99.42 54	98.89 90

MTAPA		96.65 29	96.38 40	97.47 18	98.95 21	94.05 26	95.88 82	97.62 150	94.46 59	96.29 127	96.94 162	93.56 99	99.37 66	94.29 62	99.42 54	98.99 65

pm-mvs1		95.43 86	95.94 68	93.93 167	98.38 70	85.08 210	95.46 102	97.12 203	91.84 124	97.28 70	98.46 35	95.30 42	97.71 325	90.17 208	99.42 54	98.99 65

XVG-ACMP-BASELINE		95.68 75	95.34 103	96.69 44	98.40 68	93.04 44	94.54 146	98.05 89	90.45 177	96.31 125	96.76 179	92.91 127	98.72 173	91.19 167	99.42 54	98.32 172

wuyk23d		87.83 361	90.79 287	78.96 470	90.46 453	88.63 116	92.72 225	90.67 408	91.65 136	98.68 14	97.64 89	96.06 19	77.53 492	59.84 485	99.41 60	70.73 490

anonymousdsp		96.74 24	96.42 36	97.68 7	98.00 102	94.03 28	96.97 19	97.61 152	87.68 258	98.45 21	98.77 20	94.20 88	99.50 23	96.70 13	99.40 61	99.53 17

SixPastTwentyTwo		94.91 112	95.21 110	93.98 162	98.52 57	83.19 243	95.93 79	94.84 320	94.86 53	98.49 18	98.74 21	81.45 330	99.60 9	94.69 52	99.39 62	99.15 47

Elysia		96.00 59	96.36 41	94.91 116	98.01 100	85.96 190	95.29 110	97.90 114	95.31 45	98.14 30	97.28 128	88.82 228	99.51 20	97.08 7	99.38 63	99.26 35

StellarMVS		96.00 59	96.36 41	94.91 116	98.01 100	85.96 190	95.29 110	97.90 114	95.31 45	98.14 30	97.28 128	88.82 228	99.51 20	97.08 7	99.38 63	99.26 35

HPM-MVS_fast		97.01 11	96.89 21	97.39 24	99.12 8	93.92 31	97.16 14	98.17 67	93.11 86	96.48 112	97.36 118	96.92 6	99.34 71	94.31 61	99.38 63	98.92 87

HPM-MVS		96.81 18	96.62 29	97.36 26	98.89 23	93.53 41	97.51 10	98.44 27	92.35 101	95.95 148	96.41 208	96.71 11	99.42 37	93.99 69	99.36 66	99.13 49
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		97.35 4	97.24 15	97.70 4	98.44 67	95.08 11	95.88 82	98.50 22	96.62 24	98.27 23	97.93 62	94.57 78	99.50 23	95.57 35	99.35 67	98.52 148

SDMVSNet		94.43 142	95.02 123	92.69 233	97.93 107	82.88 252	91.92 272	95.99 282	93.65 78	95.51 177	98.63 25	94.60 77	96.48 395	87.57 285	99.35 67	98.70 122

sd_testset		93.94 172	94.39 155	92.61 241	97.93 107	83.24 239	93.17 201	95.04 314	93.65 78	95.51 177	98.63 25	94.49 82	95.89 415	81.72 371	99.35 67	98.70 122

KD-MVS_self_test		94.10 163	94.73 137	92.19 262	97.66 130	79.49 329	94.86 128	97.12 203	89.59 199	96.87 91	97.65 88	90.40 202	98.34 241	89.08 243	99.35 67	98.75 113

ACMMP_NAP		96.21 51	96.12 57	96.49 51	98.90 22	91.42 66	94.57 142	98.03 96	90.42 178	96.37 120	97.35 121	95.68 25	99.25 88	94.44 58	99.34 71	98.80 102

SteuartSystems-ACMMP		96.40 44	96.30 46	96.71 43	98.63 42	91.96 58	95.70 88	98.01 99	93.34 83	96.64 106	96.57 197	94.99 60	99.36 67	93.48 87	99.34 71	98.82 98
Skip Steuart: Steuart Systems R&D Blog.
reproduce-ours		97.28 7	97.19 17	97.57 11	98.37 72	94.84 12	95.57 97	98.40 31	96.36 31	98.18 27	97.78 75	95.47 32	99.50 23	95.26 46	99.33 73	98.36 167

our_new_method		97.28 7	97.19 17	97.57 11	98.37 72	94.84 12	95.57 97	98.40 31	96.36 31	98.18 27	97.78 75	95.47 32	99.50 23	95.26 46	99.33 73	98.36 167

ACMMP		96.61 31	96.34 43	97.43 21	98.61 45	93.88 32	96.95 20	98.18 63	92.26 104	96.33 122	96.84 174	95.10 54	99.40 51	93.47 88	99.33 73	99.02 62
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
ACMM	88.83 9	96.30 49	96.07 61	96.97 37	98.39 69	92.95 47	94.74 131	98.03 96	90.82 163	97.15 76	96.85 171	96.25 18	99.00 124	93.10 106	99.33 73	98.95 80
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1111		90.39 294	90.61 291	89.74 366	98.04 97	71.50 442	95.59 93	79.72 484	89.41 201	95.94 149	98.14 44	70.79 410	98.81 155	88.52 262	99.32 77	98.90 89

DVP-MVS		95.82 69	96.18 52	94.72 126	98.51 58	86.69 167	95.20 116	97.00 210	91.85 121	97.40 61	97.35 121	95.58 28	99.34 71	93.44 91	99.31 78	98.13 196
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
test_0728_SECOND				94.88 119	98.55 53	86.72 166	95.20 116	98.22 58					99.38 64	93.44 91	99.31 78	98.53 147

MSC_two_6792asdad				95.90 69	96.54 217	89.57 94		96.87 226					99.41 43	94.06 66	99.30 80	98.72 118

No_MVS				95.90 69	96.54 217	89.57 94		96.87 226					99.41 43	94.06 66	99.30 80	98.72 118

APDe-MVS		96.46 38	96.64 28	95.93 66	97.68 128	89.38 101	96.90 21	98.41 30	92.52 94	97.43 56	97.92 67	95.11 51	99.50 23	94.45 57	99.30 80	98.92 87
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SED-MVS		96.00 59	96.41 39	94.76 124	98.51 58	86.97 157	95.21 114	98.10 79	91.95 114	97.63 43	97.25 131	96.48 13	99.35 68	93.29 98	99.29 83	97.95 218

IU-MVS					98.51 58	86.66 169		96.83 230	72.74 450	95.83 155				93.00 110	99.29 83	98.64 135

SMA-MVS		95.77 71	95.54 91	96.47 52	98.27 79	91.19 69	95.09 119	97.79 135	86.48 282	97.42 59	97.51 104	94.47 84	99.29 81	93.55 82	99.29 83	98.93 83
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
MP-MVS		96.14 53	95.68 85	97.51 16	98.81 32	94.06 24	96.10 72	97.78 137	92.73 89	93.48 273	96.72 185	94.23 87	99.42 37	91.99 141	99.29 83	99.05 60
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test_0402		95.73 73	96.22 50	94.26 152	98.19 85	85.77 196	93.24 198	97.24 193	96.88 20	97.69 41	97.77 79	94.12 90	99.13 103	91.54 160	99.29 83	97.88 232

ZNCC-MVS		96.42 42	96.20 51	97.07 33	98.80 34	92.79 49	96.08 73	98.16 70	91.74 132	95.34 189	96.36 216	95.68 25	99.44 33	94.41 59	99.28 88	98.97 72

DPE-MVS		95.89 66	95.88 74	95.92 68	97.93 107	89.83 91	93.46 190	98.30 42	92.37 99	97.75 39	96.95 161	95.14 48	99.51 20	91.74 150	99.28 88	98.41 161
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
mPP-MVS		96.46 38	96.05 62	97.69 5	98.62 43	94.65 16	96.45 46	97.74 139	92.59 93	95.47 180	96.68 188	94.50 81	99.42 37	93.10 106	99.26 90	98.99 65

test_241102_TWO								98.10 79	91.95 114	97.54 48	97.25 131	95.37 36	99.35 68	93.29 98	99.25 91	98.49 152

ACMMP++															99.25 91

CSCG		94.69 124	94.75 134	94.52 141	97.55 137	87.87 137	95.01 124	97.57 158	92.68 90	96.20 135	93.44 361	91.92 153	98.78 163	89.11 242	99.24 93	96.92 311

testf1		96.77 21	96.49 33	97.60 9	99.01 15	96.70 3	96.31 61	98.33 37	94.96 50	97.30 67	97.93 62	96.05 20	97.90 298	89.32 229	99.23 94	98.19 188

APD_test2		96.77 21	96.49 33	97.60 9	99.01 15	96.70 3	96.31 61	98.33 37	94.96 50	97.30 67	97.93 62	96.05 20	97.90 298	89.32 229	99.23 94	98.19 188

TransMVSNet (Re)		95.27 100	96.04 63	92.97 214	98.37 72	81.92 271	95.07 121	96.76 236	93.97 68	97.77 38	98.57 28	95.72 24	97.90 298	88.89 248	99.23 94	99.08 57

EC-MVSNet		95.44 85	95.62 88	94.89 118	96.93 179	87.69 141	96.48 45	99.14 6	93.93 69	92.77 311	94.52 322	93.95 94	99.49 29	93.62 79	99.22 97	97.51 271

EGC-MVSNET		80.97 438	75.73 456	96.67 45	98.85 28	94.55 18	96.83 24	96.60 249	2.44 497	5.32 498	98.25 42	92.24 145	98.02 288	91.85 146	99.21 98	97.45 275

PGM-MVS		96.32 47	95.94 68	97.43 21	98.59 48	93.84 35	95.33 106	98.30 42	91.40 148	95.76 160	96.87 170	95.26 43	99.45 32	92.77 117	99.21 98	99.00 63

SD-MVS		95.19 102	95.73 83	93.55 186	96.62 211	88.88 113	94.67 136	98.05 89	91.26 151	97.25 72	96.40 209	95.42 34	94.36 445	92.72 121	99.19 100	97.40 282
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
Vis-MVSNet (Re-imp)		90.42 291	90.16 300	91.20 314	97.66 130	77.32 378	94.33 150	87.66 430	91.20 153	92.99 301	95.13 292	75.40 387	98.28 244	77.86 409	99.19 100	97.99 211

test2506		85.42 397	84.57 400	87.96 402	97.81 115	66.53 465	96.14 70	56.35 498	89.04 210	93.55 270	98.10 47	42.88 492	98.68 184	88.09 275	99.18 102	98.67 127

ECVR-MVS		90.12 305	90.16 300	90.00 362	97.81 115	72.68 435	95.76 87	78.54 487	89.04 210	95.36 188	98.10 47	70.51 412	98.64 190	87.10 293	99.18 102	98.67 127

tfpnnormal		94.27 151	94.87 128	92.48 251	97.71 124	80.88 292	94.55 145	95.41 304	93.70 74	96.67 103	97.72 81	91.40 171	98.18 260	87.45 287	99.18 102	98.36 167

FMVSNet1		94.84 116	95.13 117	93.97 163	97.60 133	84.29 219	95.99 75	96.56 253	92.38 98	97.03 84	98.53 30	90.12 209	98.98 126	88.78 253	99.16 105	98.65 129

ACMMPR		96.46 38	96.14 56	97.41 23	98.60 46	93.82 36	96.30 65	97.96 106	92.35 101	95.57 175	96.61 194	94.93 64	99.41 43	93.78 74	99.15 106	99.00 63

HFP-MVS		96.39 45	96.17 55	97.04 34	98.51 58	93.37 42	96.30 65	97.98 102	92.35 101	95.63 172	96.47 202	95.37 36	99.27 87	93.78 74	99.14 107	98.48 153

VDD-MVS		94.37 146	94.37 157	94.40 148	97.49 140	86.07 187	93.97 169	93.28 363	94.49 57	96.24 131	97.78 75	87.99 246	98.79 159	88.92 246	99.14 107	98.34 171

region2R		96.41 43	96.09 58	97.38 25	98.62 43	93.81 38	96.32 56	97.96 106	92.26 104	95.28 194	96.57 197	95.02 58	99.41 43	93.63 78	99.11 109	98.94 81

MED-MVS test				95.52 85	98.69 37	88.21 129	96.32 56	98.58 18	88.79 217	97.38 63	96.22 228		99.39 54	92.89 114	99.10 110	98.96 76

MED-MVS		96.11 54	96.31 45	95.52 85	98.69 37	88.21 129	96.32 56	98.58 18	92.48 95	97.38 63	96.22 228	95.11 51	99.39 54	92.89 114	99.10 110	98.96 76

TestfortrainingZip a		95.98 62	96.18 52	95.38 92	98.69 37	87.60 143	96.32 56	98.58 18	88.79 217	97.38 63	96.22 228	95.11 51	99.39 54	95.41 42	99.10 110	99.16 45

ME-MVS		95.61 77	95.65 87	95.49 88	97.62 132	88.21 129	94.21 157	97.87 121	92.48 95	96.38 118	96.22 228	94.06 92	99.32 77	92.89 114	99.10 110	98.96 76

fmvsm_s_conf0.5_n_6		94.14 162	94.54 150	92.95 216	96.51 221	82.74 258	92.71 227	98.13 73	86.56 281	96.44 115	96.85 171	88.51 232	98.05 282	96.03 23	99.09 114	98.06 199

Gipuma		95.31 96	95.80 81	93.81 174	97.99 105	90.91 73	96.42 49	97.95 109	96.69 21	91.78 343	98.85 17	91.77 156	95.49 422	91.72 152	99.08 115	95.02 400
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
GST-MVS		96.24 50	95.99 66	97.00 36	98.65 41	92.71 50	95.69 90	98.01 99	92.08 112	95.74 165	96.28 222	95.22 46	99.42 37	93.17 104	99.06 116	98.88 92

OPM-MVS		95.61 77	95.45 94	96.08 58	98.49 65	91.00 71	92.65 231	97.33 184	90.05 188	96.77 99	96.85 171	95.04 56	98.56 208	92.77 117	99.06 116	98.70 122
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VPNet		93.08 209	93.76 184	91.03 320	98.60 46	75.83 407	91.51 290	95.62 290	91.84 124	95.74 165	97.10 150	89.31 222	98.32 242	85.07 330	99.06 116	98.93 83

SF-MVS		95.88 67	95.88 74	95.87 72	98.12 88	89.65 93	95.58 96	98.56 21	91.84 124	96.36 121	96.68 188	94.37 85	99.32 77	92.41 131	99.05 119	98.64 135

CS-MVS		95.77 71	95.58 90	96.37 53	96.84 186	91.72 64	96.73 30	99.06 7	94.23 62	92.48 320	94.79 309	93.56 99	99.49 29	93.47 88	99.05 119	97.89 231

XVS		96.49 36	96.18 52	97.44 19	98.56 49	93.99 29	96.50 42	97.95 109	94.58 55	94.38 239	96.49 201	94.56 79	99.39 54	93.57 80	99.05 119	98.93 83

X-MVStestdata		90.70 282	88.45 335	97.44 19	98.56 49	93.99 29	96.50 42	97.95 109	94.58 55	94.38 239	26.89 495	94.56 79	99.39 54	93.57 80	99.05 119	98.93 83

test20.03		90.80 278	90.85 283	90.63 342	95.63 307	79.24 338	89.81 353	92.87 369	89.90 190	94.39 238	96.40 209	85.77 282	95.27 430	73.86 443	99.05 119	97.39 283

Anonymous20240529		95.50 83	95.83 78	94.50 142	97.33 151	85.93 192	95.19 118	96.77 235	96.64 23	97.61 46	98.05 50	93.23 113	98.79 159	88.60 259	99.04 124	98.78 109

IterMVS-LS		93.78 177	94.28 163	92.27 256	96.27 251	79.21 340	91.87 277	96.78 233	91.77 130	96.57 111	97.07 152	87.15 261	98.74 170	91.99 141	99.03 125	98.86 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mmtdpeth		95.82 69	96.02 65	95.23 103	96.91 180	88.62 117	96.49 44	99.26 3	95.07 49	93.41 275	99.29 7	90.25 204	97.27 356	94.49 55	99.01 126	99.80 3

fmvsm_s_conf0.5_n_9		95.58 80	95.91 72	94.59 137	97.25 154	86.26 181	92.96 210	97.86 122	91.88 119	97.52 51	98.13 45	91.45 169	98.54 211	97.17 4	98.99 127	98.98 69

test_fmvsmconf0.01_n		95.90 65	96.09 58	95.31 99	97.30 153	89.21 103	94.24 154	98.76 12	86.25 287	97.56 47	98.66 23	95.73 23	98.44 230	97.35 3	98.99 127	98.27 179

test_fmvsmconf0.1_n		95.61 77	95.72 84	95.26 100	96.85 185	89.20 104	93.51 188	98.60 16	85.68 306	97.42 59	98.30 40	95.34 39	98.39 231	96.85 11	98.98 129	98.19 188

cl____		90.65 285	90.56 293	90.91 331	91.85 425	76.98 386	86.75 423	95.36 306	85.53 311	94.06 250	94.89 302	77.36 370	97.98 294	90.27 201	98.98 129	97.76 250

AllTest		94.88 115	94.51 152	96.00 59	98.02 98	92.17 53	95.26 112	98.43 28	90.48 175	95.04 215	96.74 182	92.54 136	97.86 306	85.11 328	98.98 129	97.98 212

TestCases				96.00 59	98.02 98	92.17 53		98.43 28	90.48 175	95.04 215	96.74 182	92.54 136	97.86 306	85.11 328	98.98 129	97.98 212

Patchmtry		90.11 306	89.92 306	90.66 340	90.35 454	77.00 384	92.96 210	92.81 370	90.25 181	94.74 228	96.93 164	67.11 423	97.52 338	85.17 323	98.98 129	97.46 274

DIV-MVS_self_test		90.65 285	90.56 293	90.91 331	91.85 425	76.99 385	86.75 423	95.36 306	85.52 313	94.06 250	94.89 302	77.37 369	97.99 293	90.28 200	98.97 134	97.76 250

9.14			94.81 129		97.49 140		94.11 162	98.37 35	87.56 261	95.38 185	96.03 245	94.66 74	99.08 110	90.70 182	98.97 134

D2MVS		89.93 312	89.60 314	90.92 329	94.03 370	78.40 355	88.69 388	94.85 319	78.96 406	93.08 297	95.09 295	74.57 389	96.94 377	88.19 269	98.96 136	97.41 278

PHI-MVS		94.34 149	93.80 182	95.95 63	95.65 305	91.67 65	94.82 129	97.86 122	87.86 251	93.04 300	94.16 338	91.58 161	98.78 163	90.27 201	98.96 136	97.41 278

fmvsm_s_conf0.5_n_11		94.91 112	95.44 96	93.33 199	96.45 226	83.11 247	93.56 186	98.64 14	89.76 194	95.70 169	97.97 59	92.32 142	98.08 275	95.62 31	98.95 138	98.79 104

test_fmvsmconf_n		95.43 86	95.50 92	95.22 105	96.48 225	89.19 105	93.23 199	98.36 36	85.61 309	96.92 90	98.02 54	95.23 45	98.38 234	96.69 14	98.95 138	98.09 198

fmvsm_s_conf0.5_n_7		93.61 182	93.94 177	92.63 238	96.11 267	82.76 257	90.81 314	97.55 160	86.57 280	93.14 295	97.69 83	90.17 207	96.83 384	94.46 56	98.93 140	98.31 174

mvs5depth		95.28 97	95.82 80	93.66 180	96.42 230	83.08 248	97.35 12	99.28 2	96.44 28	96.20 135	99.65 2	84.10 300	98.01 289	94.06 66	98.93 140	99.87 1

ambc				92.98 213	96.88 182	83.01 250	95.92 80	96.38 263		96.41 117	97.48 106	88.26 238	97.80 312	89.96 216	98.93 140	98.12 197

balanced_conf03		93.45 190	94.17 168	91.28 308	95.81 293	78.40 355	96.20 69	97.48 170	88.56 230	95.29 193	97.20 139	85.56 289	99.21 91	92.52 128	98.91 143	96.24 350

fmvsm_s_conf0.5_n_10		94.63 127	95.11 119	93.18 208	96.28 248	83.51 233	93.00 207	98.25 46	88.37 236	97.43 56	97.70 82	88.90 226	98.63 192	97.15 5	98.90 144	97.41 278

EPNet		89.80 317	88.25 344	94.45 146	83.91 492	86.18 184	93.87 173	87.07 436	91.16 155	80.64 479	94.72 311	78.83 352	98.89 140	85.17 323	98.89 145	98.28 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPP-MVSNet		93.91 173	93.68 190	94.59 137	98.08 91	85.55 202	97.44 11	94.03 342	94.22 63	94.94 219	96.19 233	82.07 324	99.57 14	87.28 291	98.89 145	98.65 129

v1192		93.49 188	93.78 183	92.62 240	96.16 261	79.62 321	91.83 280	97.22 195	86.07 293	96.10 142	96.38 214	87.22 259	99.02 122	94.14 65	98.88 147	99.22 40

v1144		93.50 187	93.81 180	92.57 243	96.28 248	79.61 322	91.86 279	96.96 213	86.95 276	95.91 151	96.32 218	87.65 252	98.96 132	93.51 84	98.88 147	99.13 49

APD-MVS_3200maxsize		96.82 16	96.65 27	97.32 28	97.95 106	93.82 36	96.31 61	98.25 46	95.51 44	96.99 87	97.05 155	95.63 27	99.39 54	93.31 97	98.88 147	98.75 113

APD-MVS		95.00 109	94.69 138	95.93 66	97.38 147	90.88 74	94.59 139	97.81 131	89.22 207	95.46 182	96.17 237	93.42 107	99.34 71	89.30 231	98.87 150	97.56 268
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OMC-MVS		94.22 158	93.69 189	95.81 73	97.25 154	91.27 67	92.27 257	97.40 175	87.10 273	94.56 234	95.42 281	93.74 96	98.11 270	86.62 302	98.85 151	98.06 199

SR-MVS-dyc-post		96.84 14	96.60 31	97.56 13	98.07 92	95.27 9	96.37 51	98.12 75	95.66 42	97.00 85	97.03 156	94.85 68	99.42 37	93.49 85	98.84 152	98.00 208

RE-MVS-def			96.66 26		98.07 92	95.27 9	96.37 51	98.12 75	95.66 42	97.00 85	97.03 156	95.40 35		93.49 85	98.84 152	98.00 208

v144192		93.20 206	93.54 197	92.16 266	96.05 273	78.26 362	91.95 268	97.14 199	84.98 326	95.96 147	96.11 241	87.08 263	99.04 120	93.79 73	98.84 152	99.17 44

v1921920		93.26 199	93.61 193	92.19 262	96.04 277	78.31 361	91.88 276	97.24 193	85.17 319	96.19 138	96.19 233	86.76 271	99.05 117	94.18 64	98.84 152	99.22 40

DP-MVS		95.62 76	95.84 77	94.97 113	97.16 161	88.62 117	94.54 146	97.64 148	96.94 19	96.58 110	97.32 125	93.07 121	98.72 173	90.45 189	98.84 152	97.57 266

VDDNet		94.03 167	94.27 165	93.31 200	98.87 26	82.36 264	95.51 101	91.78 396	97.19 15	96.32 124	98.60 27	84.24 298	98.75 167	87.09 294	98.83 157	98.81 100

CPTT-MVS		94.74 120	94.12 170	96.60 46	98.15 87	93.01 45	95.84 84	97.66 147	89.21 208	93.28 283	95.46 278	88.89 227	98.98 126	89.80 218	98.82 158	97.80 245

ACMMP++_ref															98.82 158

usedtu_dtu_shiyan2		93.15 208	92.40 239	95.41 91	98.56 49	90.53 83	94.71 133	94.14 340	92.10 111	93.73 264	96.94 162	89.66 219	97.77 317	72.97 449	98.81 160	97.92 226

KinetiMVS		95.09 106	95.40 99	94.15 155	97.42 146	84.35 218	93.91 172	96.69 241	94.41 60	96.67 103	97.25 131	87.67 251	99.14 100	95.78 29	98.81 160	98.97 72

fmvsm_s_conf0.1_n_2		94.38 144	94.78 133	93.19 207	97.07 167	81.72 275	91.97 267	97.51 167	87.05 274	97.31 66	97.92 67	88.29 237	98.15 266	97.10 6	98.81 160	99.70 5

v2v482		93.29 197	93.63 191	92.29 255	96.35 240	78.82 349	91.77 284	96.28 266	88.45 231	95.70 169	96.26 225	86.02 281	98.90 138	93.02 109	98.81 160	99.14 48

fmvsm_s_conf0.5_n_8		94.70 123	95.34 103	92.78 229	96.77 194	81.50 280	92.64 232	98.50 22	91.51 143	97.22 73	97.93 62	88.07 242	98.45 228	96.62 16	98.80 164	98.39 165

MVSMamba_PlusPlus		94.82 118	95.89 73	91.62 287	97.82 114	78.88 347	96.52 40	97.60 154	97.14 16	94.23 242	98.48 34	87.01 264	99.71 2	95.43 40	98.80 164	96.28 347

USDC		89.02 333	89.08 319	88.84 384	95.07 333	74.50 417	88.97 376	96.39 262	73.21 446	93.27 284	96.28 222	82.16 323	96.39 400	77.55 413	98.80 164	95.62 382

tttt0517		89.81 316	88.90 326	92.55 244	97.00 174	79.73 320	95.03 123	83.65 464	89.88 191	95.30 191	94.79 309	53.64 470	99.39 54	91.99 141	98.79 167	98.54 146

PMVS	87.21 14	94.97 110	95.33 105	93.91 168	98.97 20	97.16 2	95.54 100	95.85 285	96.47 27	93.40 278	97.46 107	95.31 41	95.47 423	86.18 313	98.78 168	89.11 473
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
fmvsm_s_conf0.5_n_2		94.25 156	94.63 145	93.10 210	96.65 202	81.75 274	91.72 285	97.25 191	86.93 278	97.20 74	97.67 86	88.44 235	98.14 269	97.06 9	98.77 169	99.42 24

TinyColmap		92.00 256	92.76 221	89.71 367	95.62 308	77.02 383	90.72 319	96.17 275	87.70 257	95.26 196	96.29 220	92.54 136	96.45 398	81.77 369	98.77 169	95.66 379

LuminaMVS		93.43 191	93.18 209	94.16 154	97.32 152	85.29 207	93.36 195	93.94 347	88.09 245	97.12 78	96.43 205	80.11 341	98.98 126	93.53 83	98.76 171	98.21 184

VortexMVS		92.13 252	92.56 233	90.85 333	94.54 355	76.17 400	92.30 255	96.63 248	86.20 289	96.66 105	96.79 176	79.87 343	98.16 264	91.27 166	98.76 171	98.24 181

v1240		93.29 197	93.71 188	92.06 270	96.01 278	77.89 367	91.81 281	97.37 176	85.12 321	96.69 102	96.40 209	86.67 272	99.07 116	94.51 54	98.76 171	99.22 40

DeepPCF-MVS	90.46 6	94.20 159	93.56 196	96.14 56	95.96 280	92.96 46	89.48 362	97.46 171	85.14 320	96.23 132	95.42 281	93.19 114	98.08 275	90.37 195	98.76 171	97.38 285

FE-MVSNET		92.02 255	92.22 245	91.41 298	96.63 210	79.08 342	91.53 289	96.84 229	85.52 313	95.16 206	96.14 238	83.97 301	97.50 339	85.48 320	98.75 175	97.64 260

Anonymous20231206		88.77 343	88.29 341	90.20 356	96.31 245	78.81 350	89.56 360	93.49 360	74.26 439	92.38 326	95.58 274	82.21 321	95.43 425	72.07 453	98.75 175	96.34 340

fmvsm_l_conf0.5_n_9		94.51 133	95.11 119	92.72 231	96.70 198	83.14 245	91.91 273	97.89 118	88.44 232	97.30 67	97.57 93	91.60 160	97.54 336	95.82 28	98.74 177	97.47 273

BP-MVS1		91.77 259	91.10 276	93.75 176	96.42 230	83.40 235	94.10 163	91.89 393	91.27 150	93.36 279	94.85 304	64.43 441	99.29 81	94.88 49	98.74 177	98.56 145

E5new		94.50 134	95.15 112	92.55 244	97.04 168	80.27 297	92.96 210	98.25 46	90.18 182	95.77 157	97.45 108	94.85 68	98.59 198	91.16 168	98.73 179	98.79 104

E6new		94.50 134	95.15 112	92.55 244	97.04 168	80.28 295	92.96 210	98.25 46	90.18 182	95.76 160	97.45 108	94.86 66	98.59 198	91.16 168	98.73 179	98.79 104

E6		94.50 134	95.15 112	92.55 244	97.04 168	80.28 295	92.96 210	98.25 46	90.18 182	95.76 160	97.45 108	94.86 66	98.59 198	91.16 168	98.73 179	98.79 104

E5		94.50 134	95.15 112	92.55 244	97.04 168	80.27 297	92.96 210	98.25 46	90.18 182	95.77 157	97.45 108	94.85 68	98.59 198	91.16 168	98.73 179	98.79 104

test_fmvsmvis_n_1920		95.08 107	95.40 99	94.13 158	96.66 201	87.75 140	93.44 192	98.49 24	85.57 310	98.27 23	97.11 148	94.11 91	97.75 321	96.26 20	98.72 183	96.89 313

casdiffmvs_mvg		95.10 105	95.62 88	93.53 189	96.25 254	83.23 240	92.66 230	98.19 61	93.06 87	97.49 53	97.15 144	94.78 71	98.71 179	92.27 133	98.72 183	98.65 129
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SR-MVS		96.70 26	96.42 36	97.54 14	98.05 94	94.69 14	96.13 71	98.07 85	95.17 48	96.82 96	96.73 184	95.09 55	99.43 36	92.99 111	98.71 185	98.50 150

UGNet		93.08 209	92.50 235	94.79 123	93.87 374	87.99 135	95.07 121	94.26 338	90.64 169	87.33 432	97.67 86	86.89 269	98.49 219	88.10 274	98.71 185	97.91 228
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
LFMVS		91.33 271	91.16 275	91.82 278	96.27 251	79.36 334	95.01 124	85.61 451	96.04 39	94.82 224	97.06 154	72.03 406	98.46 227	84.96 331	98.70 187	97.65 259

HPM-MVS++		95.02 108	94.39 155	96.91 40	97.88 110	93.58 40	94.09 164	96.99 212	91.05 156	92.40 325	95.22 289	91.03 186	99.25 88	92.11 135	98.69 188	97.90 229

DVP-MVS++		95.93 63	96.34 43	94.70 127	96.54 217	86.66 169	98.45 4	98.22 58	93.26 84	97.54 48	97.36 118	93.12 117	99.38 64	93.88 70	98.68 189	98.04 203

PC_three_1452									75.31 432	95.87 154	95.75 263	92.93 126	96.34 405	87.18 292	98.68 189	98.04 203

miper_lstm_enhance		89.90 313	89.80 309	90.19 357	91.37 436	77.50 375	83.82 467	95.00 315	84.84 329	93.05 299	94.96 300	76.53 383	95.20 431	89.96 216	98.67 191	97.86 236

FMVSNet2		92.78 223	92.73 224	92.95 216	95.40 320	81.98 270	94.18 158	95.53 299	88.63 222	96.05 143	97.37 115	81.31 332	98.81 155	87.38 290	98.67 191	98.06 199

APD_test1		95.91 64	95.42 98	97.36 26	98.82 30	96.62 6	95.64 92	97.64 148	93.38 82	95.89 153	97.23 134	93.35 109	97.66 328	88.20 268	98.66 193	97.79 246

fmvsm_s_conf0.5_n_3		95.20 101	95.95 67	92.94 218	96.60 212	82.18 268	93.13 202	98.39 33	91.44 146	97.16 75	97.68 84	93.03 124	97.82 309	97.54 2	98.63 194	98.81 100

DeepC-MVS_fast	89.96 7	93.73 178	93.44 200	94.60 136	96.14 264	87.90 136	93.36 195	97.14 199	85.53 311	93.90 258	95.45 279	91.30 174	98.59 198	89.51 225	98.62 195	97.31 288
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPU-MVS				95.15 108	96.84 186	89.43 98	95.21 114				95.66 269	93.12 117	98.06 281	86.28 312	98.61 196	97.95 218

114514_t		90.51 288	89.80 309	92.63 238	98.00 102	82.24 267	93.40 193	97.29 188	65.84 481	89.40 395	94.80 308	86.99 265	98.75 167	83.88 347	98.61 196	96.89 313

SSC-MVS		90.16 303	92.96 214	81.78 463	97.88 110	48.48 496	90.75 317	87.69 429	96.02 40	96.70 101	97.63 90	85.60 288	97.80 312	85.73 317	98.60 198	99.06 59

patch_mono-2		92.46 238	92.72 226	91.71 283	96.65 202	78.91 346	88.85 380	97.17 197	83.89 339	92.45 322	96.76 179	89.86 217	97.09 369	90.24 203	98.59 199	99.12 52

dcpmvs_2		93.96 171	95.01 124	90.82 335	97.60 133	74.04 424	93.68 181	98.85 9	89.80 193	97.82 36	97.01 159	91.14 182	99.21 91	90.56 185	98.59 199	99.19 43

CDPH-MVS		92.67 228	91.83 258	95.18 107	96.94 177	88.46 125	90.70 320	97.07 206	77.38 415	92.34 331	95.08 296	92.67 134	98.88 141	85.74 316	98.57 201	98.20 186

c3_l		91.32 272	91.42 267	91.00 323	92.29 409	76.79 389	87.52 406	96.42 261	85.76 304	94.72 230	93.89 349	82.73 316	98.16 264	90.93 178	98.55 202	98.04 203

test_prior2							90.21 338		89.33 204	90.77 363	94.81 306	90.41 201		88.21 267	98.55 202

LCM-MVSNet-Re		94.20 159	94.58 147	93.04 211	95.91 284	83.13 246	93.79 176	99.19 5	92.00 113	98.84 8	98.04 52	93.64 98	99.02 122	81.28 378	98.54 204	96.96 310

SymmetryMVS		93.26 199	92.36 241	95.97 61	97.13 164	90.84 76	94.70 134	91.61 399	90.98 157	93.22 289	95.73 264	78.94 350	99.12 104	90.38 192	98.53 205	97.97 216

Patchmatch-RL test		88.81 341	88.52 333	89.69 368	95.33 325	79.94 311	86.22 438	92.71 374	78.46 409	95.80 156	94.18 337	66.25 431	95.33 428	89.22 237	98.53 205	93.78 432

Anonymous202405211		92.58 233	92.50 235	92.83 225	96.55 216	83.22 242	92.43 244	91.64 398	94.10 65	95.59 174	96.64 190	81.88 329	97.50 339	85.12 327	98.52 207	97.77 249

CNVR-MVS		94.58 130	94.29 162	95.46 90	96.94 177	89.35 102	91.81 281	96.80 232	89.66 197	93.90 258	95.44 280	92.80 131	98.72 173	92.74 119	98.52 207	98.32 172

HQP_MVS		94.26 152	93.93 178	95.23 103	97.71 124	88.12 132	94.56 143	97.81 131	91.74 132	93.31 280	95.59 271	86.93 267	98.95 134	89.26 235	98.51 209	98.60 141

plane_prior5								97.81 131					98.95 134	89.26 235	98.51 209	98.60 141

baseline		94.26 152	94.80 130	92.64 235	96.08 270	80.99 290	93.69 180	98.04 95	90.80 164	94.89 222	96.32 218	93.19 114	98.48 224	91.68 154	98.51 209	98.43 157

test_fmvsm_n_1920		94.72 121	94.74 136	94.67 130	96.30 247	88.62 117	93.19 200	98.07 85	85.63 308	97.08 79	97.35 121	90.86 188	97.66 328	95.70 30	98.48 212	97.74 253

fmvsm_s_conf0.5_n_5		94.50 134	94.80 130	93.60 183	96.80 190	84.93 211	92.81 221	97.59 156	85.27 316	96.85 95	97.29 126	91.48 168	98.05 282	96.67 15	98.47 213	97.83 240

AstraMVS		92.75 225	92.73 224	92.79 228	97.02 172	81.48 281	92.88 219	90.62 409	87.99 247	96.48 112	96.71 186	82.02 325	98.48 224	92.44 130	98.46 214	98.40 164

thisisatest0530		88.69 346	87.52 358	92.20 261	96.33 243	79.36 334	92.81 221	84.01 463	86.44 283	93.67 266	92.68 381	53.62 471	99.25 88	89.65 224	98.45 215	98.00 208

viewmacassd2359aftdt		93.83 175	94.36 159	92.24 259	96.45 226	79.58 326	91.60 287	97.96 106	89.14 209	95.05 214	97.09 151	93.69 97	98.48 224	89.79 219	98.43 216	98.65 129

train_agg		92.71 227	91.83 258	95.35 94	96.45 226	89.46 96	90.60 323	96.92 217	79.37 399	90.49 368	94.39 329	91.20 178	98.88 141	88.66 256	98.43 216	97.72 254

E4		94.00 169	94.53 151	92.42 254	96.78 193	79.99 309	91.33 297	98.16 70	89.69 195	95.27 195	97.16 141	93.94 95	98.64 190	89.99 214	98.42 218	98.61 140

fmvsm_l_conf0.5_n_3		95.19 102	95.36 101	94.68 129	96.79 192	87.49 144	93.05 205	98.38 34	87.21 268	96.59 109	97.76 80	94.20 88	98.11 270	95.90 26	98.40 219	98.42 158

GeoE		94.55 131	94.68 142	94.15 155	97.23 156	85.11 209	94.14 161	97.34 183	88.71 221	95.26 196	95.50 276	94.65 75	99.12 104	90.94 177	98.40 219	98.23 182

ZD-MVS					97.23 156	90.32 85		97.54 161	84.40 334	94.78 226	95.79 258	92.76 132	99.39 54	88.72 255	98.40 219

test9_res														88.16 272	98.40 219	97.83 240

TSAR-MVS + GP.		93.07 212	92.41 238	95.06 110	95.82 291	90.87 75	90.97 309	92.61 378	88.04 246	94.61 233	93.79 352	88.08 241	97.81 311	89.41 228	98.39 223	96.50 332

VNet		92.67 228	92.96 214	91.79 279	96.27 251	80.15 301	91.95 268	94.98 316	92.19 108	94.52 236	96.07 243	87.43 256	97.39 350	84.83 332	98.38 224	97.83 240

GBi-Net		93.21 204	92.96 214	93.97 163	95.40 320	84.29 219	95.99 75	96.56 253	88.63 222	95.10 210	98.53 30	81.31 332	98.98 126	86.74 297	98.38 224	98.65 129

test1		93.21 204	92.96 214	93.97 163	95.40 320	84.29 219	95.99 75	96.56 253	88.63 222	95.10 210	98.53 30	81.31 332	98.98 126	86.74 297	98.38 224	98.65 129

FMVSNet3		90.78 279	90.32 299	92.16 266	93.03 391	79.92 312	92.54 236	94.95 317	86.17 292	95.10 210	96.01 246	69.97 414	98.75 167	86.74 297	98.38 224	97.82 243

MVS_111021_HR		93.63 180	93.42 202	94.26 152	96.65 202	86.96 159	89.30 369	96.23 270	88.36 237	93.57 269	94.60 318	93.45 104	97.77 317	90.23 204	98.38 224	98.03 206

guyue		92.60 231	92.62 230	92.52 250	96.73 195	81.00 289	93.00 207	91.83 395	88.28 238	96.38 118	96.23 227	80.71 338	98.37 238	92.06 140	98.37 229	98.20 186

agg_prior2														87.06 295	98.36 230	97.98 212

TSAR-MVS + MP.		94.96 111	94.75 134	95.57 83	98.86 27	88.69 114	96.37 51	96.81 231	85.23 317	94.75 227	97.12 147	91.85 154	99.40 51	93.45 90	98.33 231	98.62 139
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
pmmvs-eth3d		91.54 266	90.73 289	93.99 161	95.76 298	87.86 138	90.83 313	93.98 346	78.23 411	94.02 253	96.22 228	82.62 319	96.83 384	86.57 303	98.33 231	97.29 289

mamba_0408		93.60 183	93.72 185	93.27 203	96.65 202	82.79 254	88.81 383	97.68 144	90.62 171	95.19 203	96.01 246	91.54 166	99.08 110	88.63 257	98.32 233	97.93 221

SSM_04072		93.25 202	93.72 185	91.84 276	96.65 202	82.79 254	88.81 383	97.68 144	90.62 171	95.19 203	96.01 246	91.54 166	94.81 437	88.63 257	98.32 233	97.93 221

SSM_0407		94.23 157	94.56 149	93.24 205	96.65 202	82.79 254	93.66 182	97.84 126	91.46 144	95.19 203	96.56 199	92.50 140	98.99 125	88.83 249	98.32 233	97.93 221

casdiffmvs		94.32 150	94.80 130	92.85 224	96.05 273	81.44 282	92.35 249	98.05 89	91.53 140	95.75 164	96.80 175	93.35 109	98.49 219	91.01 176	98.32 233	98.64 135
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
3Dnovator+	92.74 2	95.86 68	95.77 82	96.13 57	96.81 189	90.79 78	96.30 65	97.82 130	96.13 35	94.74 228	97.23 134	91.33 172	99.16 97	93.25 101	98.30 237	98.46 154

MVS_111021_LR		93.66 179	93.28 206	94.80 122	96.25 254	90.95 72	90.21 338	95.43 303	87.91 248	93.74 263	94.40 328	92.88 129	96.38 401	90.39 191	98.28 238	97.07 301

CANet		92.38 241	91.99 252	93.52 191	93.82 376	83.46 234	91.14 302	97.00 210	89.81 192	86.47 436	94.04 341	87.90 248	99.21 91	89.50 226	98.27 239	97.90 229

EI-MVSNet		92.99 213	93.26 208	92.19 262	92.12 416	79.21 340	92.32 252	94.67 329	91.77 130	95.24 199	95.85 253	87.14 262	98.49 219	91.99 141	98.26 240	98.86 93

MVSTER		89.32 324	88.75 329	91.03 320	90.10 457	76.62 395	90.85 312	94.67 329	82.27 365	95.24 199	95.79 258	61.09 457	98.49 219	90.49 188	98.26 240	97.97 216

MSLP-MVS++		93.25 202	93.88 179	91.37 301	96.34 241	82.81 253	93.11 203	97.74 139	89.37 203	94.08 248	95.29 288	90.40 202	96.35 403	90.35 196	98.25 242	94.96 401

LF4IMVS		92.72 226	92.02 251	94.84 121	95.65 305	91.99 57	92.92 216	96.60 249	85.08 323	92.44 323	93.62 356	86.80 270	96.35 403	86.81 296	98.25 242	96.18 353

viewdifsd2359ckpt07		93.63 180	94.33 161	91.55 290	96.19 259	77.86 368	90.11 344	97.74 139	90.76 165	96.11 141	96.61 194	94.37 85	98.27 248	88.82 251	98.23 244	98.51 149

SSM_0404		94.38 144	94.69 138	93.43 195	97.16 161	83.23 240	93.95 170	97.84 126	91.46 144	95.70 169	96.56 199	92.50 140	99.08 110	88.83 249	98.23 244	97.98 212

EI-MVSNet-UG-set		94.35 148	94.27 165	94.59 137	92.46 404	85.87 194	92.42 245	94.69 327	93.67 77	96.13 139	95.84 255	91.20 178	98.86 145	93.78 74	98.23 244	99.03 61

PM-MVS		93.33 196	92.67 229	95.33 96	96.58 213	94.06 24	92.26 258	92.18 385	85.92 296	96.22 133	96.61 194	85.64 287	95.99 413	90.35 196	98.23 244	95.93 364

EI-MVSNet-Vis-set		94.36 147	94.28 163	94.61 133	92.55 401	85.98 189	92.44 243	94.69 327	93.70 74	96.12 140	95.81 257	91.24 175	98.86 145	93.76 77	98.22 248	98.98 69

V42		93.43 191	93.58 194	92.97 214	95.34 324	81.22 286	92.67 229	96.49 258	87.25 266	96.20 135	96.37 215	87.32 258	98.85 147	92.39 132	98.21 249	98.85 96

TAMVS		90.16 303	89.05 320	93.49 193	96.49 223	86.37 177	90.34 335	92.55 379	80.84 385	92.99 301	94.57 321	81.94 328	98.20 257	73.51 444	98.21 249	95.90 367

K. test v3		93.37 193	93.27 207	93.66 180	98.05 94	82.62 260	94.35 149	86.62 438	96.05 38	97.51 52	98.85 17	76.59 382	99.65 4	93.21 102	98.20 251	98.73 117

DELS-MVS		92.05 254	92.16 246	91.72 282	94.44 358	80.13 303	87.62 400	97.25 191	87.34 264	92.22 334	93.18 369	89.54 221	98.73 172	89.67 223	98.20 251	96.30 345
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
TAPA-MVS	88.58 10	92.49 237	91.75 260	94.73 125	96.50 222	89.69 92	92.91 217	97.68 144	78.02 412	92.79 310	94.10 339	90.85 189	97.96 295	84.76 334	98.16 253	96.54 325
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LS3D		96.11 54	95.83 78	96.95 39	94.75 344	94.20 22	97.34 13	97.98 102	97.31 14	95.32 190	96.77 177	93.08 119	99.20 94	91.79 148	98.16 253	97.44 277

GDP-MVS		91.56 265	90.83 284	93.77 175	96.34 241	83.65 231	93.66 182	98.12 75	87.32 265	92.98 303	94.71 312	63.58 447	99.30 80	92.61 124	98.14 255	98.35 170

DP-MVS Recon		92.31 244	91.88 256	93.60 183	97.18 160	86.87 161	91.10 304	97.37 176	84.92 327	92.08 339	94.08 340	88.59 231	98.20 257	83.50 348	98.14 255	95.73 374

EG-PatchMatch MVS		94.54 132	94.67 143	94.14 157	97.87 112	86.50 171	92.00 266	96.74 237	88.16 244	96.93 89	97.61 91	93.04 123	97.90 298	91.60 156	98.12 257	98.03 206

PCF-MVS	84.52 17	89.12 329	87.71 355	93.34 198	96.06 272	85.84 195	86.58 430	97.31 185	68.46 474	93.61 268	93.89 349	87.51 255	98.52 216	67.85 470	98.11 258	95.66 379
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
3Dnovator	92.54 3	94.80 119	94.90 126	94.47 145	95.47 318	87.06 154	96.63 36	97.28 190	91.82 127	94.34 241	97.41 112	90.60 198	98.65 189	92.47 129	98.11 258	97.70 255

WBMVS		84.00 411	83.48 411	85.56 436	92.71 397	61.52 483	83.82 467	89.38 415	79.56 397	90.74 364	93.20 368	48.21 475	97.28 354	75.63 430	98.10 260	97.88 232

PMMVS2		81.31 434	83.44 412	74.92 473	90.52 450	46.49 499	69.19 490	85.23 457	84.30 336	87.95 422	94.71 312	76.95 377	84.36 490	64.07 479	98.09 261	93.89 430

lessismore_v0				93.87 170	98.05 94	83.77 230		80.32 482		97.13 77	97.91 70	77.49 365	99.11 108	92.62 123	98.08 262	98.74 116

viewdifsd2359ckpt11		93.36 194	93.99 173	91.48 294	95.50 316	78.39 357	90.47 327	96.69 241	88.59 225	96.03 145	96.88 168	93.48 102	97.63 331	90.20 206	98.07 263	98.41 161

viewmsd2359difaftdt		93.36 194	93.99 173	91.48 294	95.50 316	78.39 357	90.47 327	96.69 241	88.59 225	96.03 145	96.88 168	93.48 102	97.63 331	90.20 206	98.07 263	98.41 161

new-patchmatchnet		88.97 337	90.79 287	83.50 456	94.28 362	55.83 492	85.34 450	93.56 358	86.18 291	95.47 180	95.73 264	83.10 309	96.51 394	85.40 321	98.06 265	98.16 191

plane_prior						88.12 132	93.01 206		88.98 212						98.06 265

PVSNet_BlendedMVS		90.35 297	89.96 305	91.54 292	94.81 340	78.80 351	90.14 341	96.93 215	79.43 398	88.68 411	95.06 297	86.27 278	98.15 266	80.27 386	98.04 267	97.68 257

fmvsm_l_conf0.5_n_a		93.59 184	93.63 191	93.49 193	96.10 268	85.66 200	92.32 252	96.57 252	81.32 379	95.63 172	97.14 145	90.19 205	97.73 324	95.37 44	98.03 268	97.07 301

CL-MVSNet_self_test		90.04 311	89.90 307	90.47 346	95.24 326	77.81 369	86.60 429	92.62 377	85.64 307	93.25 287	93.92 347	83.84 302	96.06 410	79.93 394	98.03 268	97.53 270

FMVSNet5		87.82 362	86.56 381	91.62 287	92.31 408	79.81 316	93.49 189	94.81 323	83.26 345	91.36 349	96.93 164	52.77 472	97.49 342	76.07 426	98.03 268	97.55 269

fmvsm_s_conf0.5_n_4		94.26 152	94.58 147	93.31 200	96.40 232	82.73 259	92.59 234	97.41 174	86.60 279	96.33 122	97.07 152	89.91 215	98.07 279	96.88 10	98.01 271	99.13 49

testing3-2		83.95 412	84.22 404	83.13 458	96.28 248	54.34 495	88.51 392	83.01 469	92.19 108	89.09 400	90.98 412	45.51 481	97.44 345	74.38 438	98.01 271	97.60 263

原ACMM1				92.87 223	96.91 180	84.22 222		97.01 209	76.84 422	89.64 391	94.46 327	88.00 245	98.70 180	81.53 374	98.01 271	95.70 377

fmvsm_l_conf0.5_n		93.79 176	93.81 180	93.73 178	96.16 261	86.26 181	92.46 241	96.72 238	81.69 374	95.77 157	97.11 148	90.83 190	97.82 309	95.58 34	97.99 274	97.11 296

v148		92.87 219	93.29 204	91.62 287	96.25 254	77.72 373	91.28 298	95.05 313	89.69 195	95.93 150	96.04 244	87.34 257	98.38 234	90.05 213	97.99 274	98.78 109

E2		93.53 185	93.96 175	92.25 257	96.39 233	79.76 318	91.06 307	98.05 89	88.58 227	94.71 231	96.64 190	93.08 119	98.57 204	89.16 239	97.97 276	98.42 158

E3		93.53 185	93.96 175	92.25 257	96.39 233	79.76 318	91.06 307	98.05 89	88.58 227	94.71 231	96.64 190	93.07 121	98.57 204	89.16 239	97.97 276	98.42 158

WB-MVS		89.44 322	92.15 248	81.32 464	97.73 122	48.22 497	89.73 355	87.98 427	95.24 47	96.05 143	96.99 160	85.18 291	96.95 376	82.45 363	97.97 276	98.78 109

ITE_SJBPF				95.95 63	97.34 150	93.36 43		96.55 256	91.93 116	94.82 224	95.39 286	91.99 151	97.08 370	85.53 319	97.96 279	97.41 278

test12				94.43 147	95.95 281	86.75 165		96.24 269		89.76 389		89.79 218	98.79 159		97.95 280	97.75 252

MCST-MVS		92.91 215	92.51 234	94.10 159	97.52 138	85.72 198	91.36 296	97.13 201	80.33 388	92.91 307	94.24 334	91.23 176	98.72 173	89.99 214	97.93 281	97.86 236

CDS-MVSNet		89.55 318	88.22 347	93.53 189	95.37 323	86.49 172	89.26 370	93.59 356	79.76 393	91.15 356	92.31 389	77.12 371	98.38 234	77.51 414	97.92 282	95.71 375
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
旧先验1					96.20 257	84.17 224		94.82 321			95.57 275	89.57 220			97.89 283	96.32 344

reproduce_monomvs		87.13 383	86.90 373	87.84 408	90.92 445	68.15 457	91.19 300	93.75 352	85.84 301	94.21 244	95.83 256	42.99 489	97.10 368	89.46 227	97.88 284	98.26 180

alignmvs		93.26 199	92.85 218	94.50 142	95.70 300	87.45 145	93.45 191	95.76 286	91.58 137	95.25 198	92.42 388	81.96 327	98.72 173	91.61 155	97.87 285	97.33 287

testgi		90.38 295	91.34 270	87.50 411	97.49 140	71.54 441	89.43 364	95.16 311	88.38 234	94.54 235	94.68 314	92.88 129	93.09 457	71.60 457	97.85 286	97.88 232

fmvsm_s_conf0.1_n		94.19 161	94.41 154	93.52 191	97.22 158	84.37 216	93.73 178	95.26 308	84.45 333	95.76 160	98.00 55	91.85 154	97.21 360	95.62 31	97.82 287	98.98 69

fmvsm_s_conf0.5_n		94.00 169	94.20 167	93.42 196	96.69 199	84.37 216	93.38 194	95.13 312	84.50 332	95.40 184	97.55 99	91.77 156	97.20 361	95.59 33	97.79 288	98.69 125

balanced_ft_v1		92.65 230	93.17 210	91.10 318	94.47 357	77.32 378	96.67 34	96.70 240	88.23 240	93.70 265	97.16 141	83.33 306	99.41 43	90.51 187	97.76 289	96.57 324

新几何1				93.17 209	97.16 161	87.29 147		94.43 333	67.95 475	91.29 350	94.94 301	86.97 266	98.23 254	81.06 382	97.75 290	93.98 428

viewmanbaseed2359cas		93.08 209	93.43 201	92.01 273	95.69 301	79.29 336	91.15 301	97.70 143	87.45 262	94.18 245	96.12 240	92.31 143	98.37 238	88.58 260	97.73 291	98.38 166

ETV-MVS		92.99 213	92.74 222	93.72 179	95.86 288	86.30 180	92.33 251	97.84 126	91.70 135	92.81 308	86.17 459	92.22 146	99.19 95	88.03 278	97.73 291	95.66 379

HQP3-MVS								97.31 185							97.73 291

HQP-MVS		92.09 253	91.49 266	93.88 169	96.36 237	84.89 212	91.37 293	97.31 185	87.16 269	88.81 404	93.40 362	84.76 295	98.60 196	86.55 305	97.73 291	98.14 195

viewdifsd2359ckpt09		92.60 231	92.34 242	93.36 197	95.94 283	83.36 236	92.35 249	97.93 113	83.17 350	92.92 306	94.66 315	89.87 216	98.57 204	86.51 307	97.71 295	98.15 193

CANet_DTU		89.85 315	89.17 318	91.87 275	92.20 413	80.02 308	90.79 315	95.87 284	86.02 294	82.53 469	91.77 401	80.01 342	98.57 204	85.66 318	97.70 296	97.01 306

NCCC		94.08 165	93.54 197	95.70 80	96.49 223	89.90 90	92.39 247	96.91 219	90.64 169	92.33 332	94.60 318	90.58 199	98.96 132	90.21 205	97.70 296	98.23 182

Vis-MVSNet		95.50 83	95.48 93	95.56 84	98.11 89	89.40 100	95.35 104	98.22 58	92.36 100	94.11 246	98.07 49	92.02 150	99.44 33	93.38 96	97.67 298	97.85 238
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MGCFI-Net		94.44 141	94.67 143	93.75 176	95.56 312	85.47 203	95.25 113	98.24 54	91.53 140	95.04 215	92.21 392	94.94 63	98.54 211	91.56 159	97.66 299	97.24 291

AdaColmap		91.63 263	91.36 269	92.47 252	95.56 312	86.36 178	92.24 260	96.27 267	88.88 216	89.90 385	92.69 380	91.65 159	98.32 242	77.38 416	97.64 300	92.72 453

EPNet_dtu		85.63 395	84.37 401	89.40 373	86.30 483	74.33 419	91.64 286	88.26 421	84.84 329	72.96 490	89.85 423	71.27 409	97.69 326	76.60 421	97.62 301	96.18 353
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs_AUTHOR		92.34 243	92.70 227	91.26 309	94.20 363	78.42 354	89.12 374	97.60 154	87.16 269	93.17 294	95.50 276	88.66 230	97.57 335	91.30 165	97.61 302	97.79 246

XVG-OURS		94.72 121	94.12 170	96.50 50	98.00 102	94.23 21	91.48 292	98.17 67	90.72 166	95.30 191	96.47 202	87.94 247	96.98 374	91.41 163	97.61 302	98.30 176

sasdasda		94.59 128	94.69 138	94.30 150	95.60 309	87.03 155	95.59 93	98.24 54	91.56 138	95.21 201	92.04 397	94.95 61	98.66 186	91.45 161	97.57 304	97.20 293

canonicalmvs		94.59 128	94.69 138	94.30 150	95.60 309	87.03 155	95.59 93	98.24 54	91.56 138	95.21 201	92.04 397	94.95 61	98.66 186	91.45 161	97.57 304	97.20 293

viewcassd2359sk11		93.16 207	93.51 199	92.13 268	96.07 271	79.59 323	90.88 311	97.97 104	87.82 252	94.23 242	96.19 233	92.31 143	98.53 214	88.58 260	97.51 306	98.28 177

XXY-MVS		92.58 233	93.16 211	90.84 334	97.75 119	79.84 313	91.87 277	96.22 272	85.94 295	95.53 176	97.68 84	92.69 133	94.48 441	83.21 351	97.51 306	98.21 184

FA-MVS(test-final)		91.81 258	91.85 257	91.68 285	94.95 335	79.99 309	96.00 74	93.44 361	87.80 253	94.02 253	97.29 126	77.60 364	98.45 228	88.04 277	97.49 308	96.61 323

Effi-MVS+-dtu		93.90 174	92.60 232	97.77 3	94.74 347	96.67 5	94.00 167	95.41 304	89.94 189	91.93 342	92.13 395	90.12 209	98.97 131	87.68 284	97.48 309	97.67 258

OpenMVS	89.45 8	92.27 248	92.13 249	92.68 234	94.53 356	84.10 225	95.70 88	97.03 208	82.44 364	91.14 357	96.42 207	88.47 234	98.38 234	85.95 314	97.47 310	95.55 384

fmvsm_s_conf0.1_n_a		94.26 152	94.37 157	93.95 166	97.36 149	85.72 198	94.15 159	95.44 301	83.25 346	95.51 177	98.05 50	92.54 136	97.19 363	95.55 36	97.46 311	98.94 81

ab-mvs		92.40 240	92.62 230	91.74 281	97.02 172	81.65 276	95.84 84	95.50 300	86.95 276	92.95 305	97.56 95	90.70 196	97.50 339	79.63 397	97.43 312	96.06 358

fmvsm_s_conf0.5_n_a		94.02 168	94.08 172	93.84 172	96.72 197	85.73 197	93.65 184	95.23 310	83.30 344	95.13 208	97.56 95	92.22 146	97.17 364	95.51 37	97.41 313	98.64 135

thisisatest0515		84.72 404	82.99 416	89.90 363	92.96 393	75.33 410	84.36 461	83.42 466	77.37 416	88.27 417	86.65 454	53.94 469	98.72 173	82.56 360	97.40 314	95.67 378

test222					96.95 176	85.27 208	88.83 381	93.61 355	65.09 483	90.74 364	94.85 304	84.62 297			97.36 315	93.91 429

API-MVS		91.52 267	91.61 261	91.26 309	94.16 364	86.26 181	94.66 137	94.82 321	91.17 154	92.13 338	91.08 411	90.03 214	97.06 372	79.09 404	97.35 316	90.45 470

usedtu_dtu_shiyan1		89.18 325	88.59 331	90.95 327	94.75 344	77.79 370	86.25 435	94.63 331	81.61 375	90.88 359	92.24 391	77.03 373	98.08 275	82.62 357	97.27 317	96.97 308

FE-MVSNET3		89.18 325	88.59 331	90.95 327	94.75 344	77.79 370	86.25 435	94.63 331	81.61 375	90.88 359	92.25 390	77.03 373	98.08 275	82.62 357	97.27 317	96.97 308

EIA-MVS		92.35 242	92.03 250	93.30 202	95.81 293	83.97 227	92.80 223	98.17 67	87.71 256	89.79 388	87.56 449	91.17 181	99.18 96	87.97 279	97.27 317	96.77 319

testdata				91.03 320	96.87 183	82.01 269		94.28 337	71.55 456	92.46 321	95.42 281	85.65 286	97.38 352	82.64 356	97.27 317	93.70 435

N_pmnet		88.90 339	87.25 365	93.83 173	94.40 360	93.81 38	84.73 454	87.09 434	79.36 401	93.26 285	92.43 387	79.29 348	91.68 464	77.50 415	97.22 321	96.00 360

testing3		83.66 414	82.52 419	87.08 414	95.84 289	65.84 470	89.80 354	77.17 491	88.17 243	90.84 362	88.63 440	30.95 499	98.11 270	84.05 343	97.19 322	97.28 290

ppachtmachnet_test		88.61 347	88.64 330	88.50 393	91.76 427	70.99 445	84.59 459	92.98 367	79.30 403	92.38 326	93.53 360	79.57 345	97.45 344	86.50 308	97.17 323	97.07 301

CNLPA		91.72 261	91.20 272	93.26 204	96.17 260	91.02 70	91.14 302	95.55 298	90.16 186	90.87 361	93.56 359	86.31 277	94.40 444	79.92 396	97.12 324	94.37 419

FE-MVS		89.06 332	88.29 341	91.36 302	94.78 342	79.57 327	96.77 29	90.99 403	84.87 328	92.96 304	96.29 220	60.69 459	98.80 158	80.18 389	97.11 325	95.71 375

icg_test_0407_2		91.18 274	91.92 255	88.94 381	95.19 328	76.72 390	84.66 458	96.89 220	85.92 296	93.55 270	94.50 323	91.06 183	92.99 458	88.49 263	97.07 326	97.10 297

IMVS_0407		92.28 245	92.83 219	90.63 342	95.19 328	76.72 390	92.79 224	96.89 220	85.92 296	93.55 270	94.50 323	91.06 183	98.07 279	88.49 263	97.07 326	97.10 297

IMVS_0404		90.67 284	91.06 277	89.50 369	95.19 328	76.72 390	86.58 430	96.89 220	85.92 296	89.17 397	94.50 323	85.77 282	94.67 438	88.49 263	97.07 326	97.10 297

IMVS_0403		92.20 250	92.70 227	90.69 338	95.19 328	76.72 390	92.39 247	96.89 220	85.92 296	93.66 267	94.50 323	90.18 206	98.24 252	88.49 263	97.07 326	97.10 297

jason		89.17 328	88.32 339	91.70 284	95.73 299	80.07 304	88.10 395	93.22 364	71.98 453	90.09 376	92.79 377	78.53 357	98.56 208	87.43 288	97.06 330	96.46 336
jason: jason.
RPSCF		95.58 80	94.89 127	97.62 8	97.58 135	96.30 7	95.97 78	97.53 164	92.42 97	93.41 275	97.78 75	91.21 177	97.77 317	91.06 173	97.06 330	98.80 102

cl22		89.02 333	88.50 334	90.59 344	89.76 459	76.45 397	86.62 428	94.03 342	82.98 354	92.65 314	92.49 383	72.05 405	97.53 337	88.93 245	97.02 332	97.78 248

miper_ehance_all_eth		90.48 289	90.42 296	90.69 338	91.62 432	76.57 396	86.83 421	96.18 274	83.38 343	94.06 250	92.66 382	82.20 322	98.04 284	89.79 219	97.02 332	97.45 275

miper_enhance_ethall		88.42 351	87.87 353	90.07 358	88.67 472	75.52 408	85.10 451	95.59 295	75.68 426	92.49 319	89.45 433	78.96 349	97.88 302	87.86 282	97.02 332	96.81 317

eth_miper_zixun_eth		90.72 281	90.61 291	91.05 319	92.04 419	76.84 388	86.91 418	96.67 245	85.21 318	94.41 237	93.92 347	79.53 346	98.26 249	89.76 221	97.02 332	98.06 199

QAPM		92.88 217	92.77 220	93.22 206	95.82 291	83.31 237	96.45 46	97.35 182	83.91 338	93.75 261	96.77 177	89.25 223	98.88 141	84.56 336	97.02 332	97.49 272

E3new		92.83 221	93.10 212	92.04 271	95.78 295	79.45 330	90.76 316	97.90 114	87.23 267	93.79 260	95.70 267	91.55 162	98.49 219	88.17 271	96.99 337	98.16 191

thres600view7		87.66 365	87.10 371	89.36 374	96.05 273	73.17 428	92.72 225	85.31 454	91.89 118	93.29 282	90.97 413	63.42 448	98.39 231	73.23 446	96.99 337	96.51 329

tt0805		95.42 89	95.93 70	93.86 171	98.75 36	88.47 124	97.68 9	94.29 336	96.48 26	95.38 185	93.63 355	94.89 65	97.94 297	95.38 43	96.92 339	95.17 391

test_yl		90.11 306	89.73 312	91.26 309	94.09 367	79.82 314	90.44 329	92.65 375	90.90 159	93.19 292	93.30 364	73.90 393	98.03 285	82.23 365	96.87 340	95.93 364

DCV-MVSNet		90.11 306	89.73 312	91.26 309	94.09 367	79.82 314	90.44 329	92.65 375	90.90 159	93.19 292	93.30 364	73.90 393	98.03 285	82.23 365	96.87 340	95.93 364

test_fmvs3		92.42 239	92.40 239	92.46 253	93.80 377	87.28 148	93.86 174	97.05 207	76.86 421	96.25 130	98.66 23	82.87 313	91.26 466	95.44 39	96.83 342	98.82 98

MSP-MVS		95.34 92	94.63 145	97.48 17	98.67 40	94.05 26	96.41 50	98.18 63	91.26 151	95.12 209	95.15 290	86.60 274	99.50 23	93.43 94	96.81 343	98.89 90
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
pmmvs5		87.87 360	87.14 368	90.07 358	93.26 386	76.97 387	88.89 378	92.18 385	73.71 442	88.36 415	93.89 349	76.86 380	96.73 388	80.32 385	96.81 343	96.51 329

PVSNet_Blended_VisFu		91.63 263	91.20 272	92.94 218	97.73 122	83.95 228	92.14 261	97.46 171	78.85 408	92.35 329	94.98 299	84.16 299	99.08 110	86.36 310	96.77 345	95.79 372

MVSFormer		92.18 251	92.23 244	92.04 271	94.74 347	80.06 305	97.15 15	97.37 176	88.98 212	88.83 402	92.79 377	77.02 375	99.60 9	96.41 18	96.75 346	96.46 336

lupinMVS		88.34 354	87.31 362	91.45 296	94.74 347	80.06 305	87.23 410	92.27 384	71.10 460	88.83 402	91.15 409	77.02 375	98.53 214	86.67 301	96.75 346	95.76 373

ttmdpeth		86.91 388	86.57 380	87.91 406	89.68 461	74.24 421	91.49 291	87.09 434	79.84 390	89.46 394	97.86 73	65.42 435	91.04 467	81.57 373	96.74 348	98.44 156

diffmvs		91.74 260	91.93 254	91.15 317	93.06 389	78.17 363	88.77 386	97.51 167	86.28 286	92.42 324	93.96 346	88.04 244	97.46 343	90.69 183	96.67 349	97.82 243
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DPM-MVS		89.35 323	88.40 336	92.18 265	96.13 266	84.20 223	86.96 417	96.15 276	75.40 430	87.36 431	91.55 406	83.30 307	98.01 289	82.17 367	96.62 350	94.32 421

test_fmvs2		90.62 287	90.40 297	91.29 307	91.93 423	85.46 204	92.70 228	96.48 259	74.44 436	94.91 221	97.59 92	75.52 386	90.57 469	93.44 91	96.56 351	97.84 239

thres100view900		87.35 376	86.89 374	88.72 386	96.14 264	73.09 430	93.00 207	85.31 454	92.13 110	93.26 285	90.96 414	63.42 448	98.28 244	71.27 459	96.54 352	94.79 409

tfpn200view9		87.05 385	86.52 383	88.67 387	95.77 296	72.94 432	91.89 274	86.00 443	90.84 161	92.61 315	89.80 425	63.93 444	98.28 244	71.27 459	96.54 352	94.79 409

thres400		87.20 380	86.52 383	89.24 378	95.77 296	72.94 432	91.89 274	86.00 443	90.84 161	92.61 315	89.80 425	63.93 444	98.28 244	71.27 459	96.54 352	96.51 329

CMPMVS	68.83 22	87.28 377	85.67 393	92.09 269	88.77 471	85.42 205	90.31 336	94.38 334	70.02 468	88.00 420	93.30 364	73.78 395	94.03 450	75.96 428	96.54 352	96.83 316
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UWE-MVS		80.29 445	79.10 446	83.87 453	91.97 422	59.56 487	86.50 433	77.43 490	75.40 430	87.79 426	88.10 446	44.08 486	96.90 381	64.23 478	96.36 356	95.14 394

pmmvs4		88.95 338	87.70 356	92.70 232	94.30 361	85.60 201	87.22 411	92.16 387	74.62 435	89.75 390	94.19 336	77.97 362	96.41 399	82.71 355	96.36 356	96.09 356

MVStest1		84.79 403	84.06 406	86.98 417	77.73 498	74.76 411	91.08 306	85.63 448	77.70 413	96.86 92	97.97 59	41.05 496	88.24 482	92.22 134	96.28 358	97.94 220

Fast-Effi-MVS+-dtu		92.77 224	92.16 246	94.58 140	94.66 352	88.25 127	92.05 263	96.65 246	89.62 198	90.08 380	91.23 408	92.56 135	98.60 196	86.30 311	96.27 359	96.90 312

viewdifsd2359ckpt13		92.57 235	92.48 237	92.83 225	95.60 309	82.35 266	91.80 283	97.49 169	85.04 324	93.14 295	95.41 284	90.94 187	98.25 250	86.68 300	96.24 360	97.87 235

MAR-MVS		90.32 299	88.87 328	94.66 132	94.82 339	91.85 60	94.22 156	94.75 325	80.91 382	87.52 430	88.07 447	86.63 273	97.87 305	76.67 420	96.21 361	94.25 422
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
viewmambaseed2359dif		90.77 280	90.81 285	90.64 341	93.46 381	77.04 382	88.83 381	96.29 265	80.79 386	92.21 335	95.11 293	88.99 225	97.28 354	85.39 322	96.20 362	97.59 264

AUN-MVS		90.05 310	88.30 340	95.32 98	96.09 269	90.52 84	92.42 245	92.05 391	82.08 368	88.45 414	92.86 374	65.76 433	98.69 182	88.91 247	96.07 363	96.75 321

hse-mvs2		92.24 249	91.20 272	95.38 92	96.16 261	90.65 81	92.52 237	92.01 392	89.23 205	93.95 255	92.99 372	76.88 378	98.69 182	91.02 174	96.03 364	96.81 317

PVSNet_Blended		88.74 344	88.16 350	90.46 348	94.81 340	78.80 351	86.64 426	96.93 215	74.67 434	88.68 411	89.18 437	86.27 278	98.15 266	80.27 386	96.00 365	94.44 418

F-COLMAP		92.28 245	91.06 277	95.95 63	97.52 138	91.90 59	93.53 187	97.18 196	83.98 337	88.70 410	94.04 341	88.41 236	98.55 210	80.17 390	95.99 366	97.39 283

xiu_mvs_v1_base_debu		91.47 268	91.52 263	91.33 304	95.69 301	81.56 277	89.92 349	96.05 279	83.22 347	91.26 351	90.74 416	91.55 162	98.82 150	89.29 232	95.91 367	93.62 438

xiu_mvs_v1_base		91.47 268	91.52 263	91.33 304	95.69 301	81.56 277	89.92 349	96.05 279	83.22 347	91.26 351	90.74 416	91.55 162	98.82 150	89.29 232	95.91 367	93.62 438

xiu_mvs_v1_base_debi		91.47 268	91.52 263	91.33 304	95.69 301	81.56 277	89.92 349	96.05 279	83.22 347	91.26 351	90.74 416	91.55 162	98.82 150	89.29 232	95.91 367	93.62 438

thres200		85.85 394	85.18 395	87.88 407	94.44 358	72.52 437	89.08 375	86.21 440	88.57 229	91.44 348	88.40 443	64.22 442	98.00 291	68.35 468	95.88 370	93.12 444

RRT-MVS		92.28 245	93.01 213	90.07 358	94.06 369	73.01 431	95.36 103	97.88 119	92.24 106	95.16 206	97.52 100	78.51 358	99.29 81	90.55 186	95.83 371	97.92 226

Patchmatch-test		86.10 393	86.01 390	86.38 429	90.63 448	74.22 422	89.57 359	86.69 437	85.73 305	89.81 387	92.83 375	65.24 438	91.04 467	77.82 412	95.78 372	93.88 431

h-mvs33		92.89 216	91.99 252	95.58 82	96.97 175	90.55 82	93.94 171	94.01 345	89.23 205	93.95 255	96.19 233	76.88 378	99.14 100	91.02 174	95.71 373	97.04 305

test_fmvs1_n		88.73 345	88.38 337	89.76 365	92.06 418	82.53 261	92.30 255	96.59 251	71.14 459	92.58 317	95.41 284	68.55 417	89.57 477	91.12 172	95.66 374	97.18 295

myMVS_eth3d28		80.97 438	80.42 439	82.62 460	93.35 383	58.25 490	84.70 457	85.62 450	86.31 285	84.04 455	85.20 468	46.00 479	94.07 449	62.93 482	95.65 375	95.53 385

cascas		87.02 386	86.28 389	89.25 377	91.56 434	76.45 397	84.33 462	96.78 233	71.01 461	86.89 435	85.91 460	81.35 331	96.94 377	83.09 352	95.60 376	94.35 420

XVG-OURS-SEG-HR		95.38 90	95.00 125	96.51 49	98.10 90	94.07 23	92.46 241	98.13 73	90.69 167	93.75 261	96.25 226	98.03 2	97.02 373	92.08 137	95.55 377	98.45 155

DSMNet-mixed		82.21 427	81.56 425	84.16 451	89.57 464	70.00 452	90.65 322	77.66 489	54.99 492	83.30 463	97.57 93	77.89 363	90.50 471	66.86 473	95.54 378	91.97 458

MVS_Test		92.57 235	93.29 204	90.40 349	93.53 380	75.85 404	92.52 237	96.96 213	88.73 219	92.35 329	96.70 187	90.77 191	98.37 238	92.53 127	95.49 379	96.99 307

MIMVSNet		87.13 383	86.54 382	88.89 383	96.05 273	76.11 401	94.39 148	88.51 419	81.37 378	88.27 417	96.75 181	72.38 400	95.52 420	65.71 476	95.47 380	95.03 399

Fast-Effi-MVS+		91.28 273	90.86 282	92.53 249	95.45 319	82.53 261	89.25 372	96.52 257	85.00 325	89.91 384	88.55 442	92.94 125	98.84 148	84.72 335	95.44 381	96.22 351

ET-MVSNet_ETH3D		86.15 392	84.27 403	91.79 279	93.04 390	81.28 283	87.17 413	86.14 441	79.57 396	83.65 458	88.66 439	57.10 463	98.18 260	87.74 283	95.40 382	95.90 367

BH-RMVSNet		90.47 290	90.44 295	90.56 345	95.21 327	78.65 353	89.15 373	93.94 347	88.21 241	92.74 312	94.22 335	86.38 275	97.88 302	78.67 406	95.39 383	95.14 394

CHOSEN 1792x2688		87.19 381	85.92 392	91.00 323	97.13 164	79.41 333	84.51 460	95.60 291	64.14 484	90.07 381	94.81 306	78.26 360	97.14 367	73.34 445	95.38 384	96.46 336

test_fmvs1		87.59 368	87.27 364	88.54 390	88.32 473	81.26 284	90.43 332	95.72 288	70.55 465	91.70 344	94.63 316	68.13 418	89.42 479	90.59 184	95.34 385	94.94 404

wanda-best-256-512		87.53 370	86.39 386	90.97 325	91.29 438	78.39 357	85.63 446	93.75 352	81.91 370	90.09 376	83.30 477	72.25 401	98.18 260	83.96 344	95.32 386	96.33 341

FE-blended-shiyan7		87.53 370	86.39 386	90.97 325	91.29 438	78.39 357	85.63 446	93.75 352	81.91 370	90.09 376	83.30 477	72.25 401	98.18 260	83.96 344	95.32 386	96.33 341

blended_shiyan6		88.42 351	87.43 360	91.40 299	92.37 405	79.43 332	87.41 408	93.91 350	82.51 361	91.17 355	85.44 464	74.34 391	98.24 252	84.38 340	95.32 386	96.53 327

usedtu_blend_shiyan5		89.08 331	88.33 338	91.34 303	91.29 438	79.59 323	94.02 165	97.13 201	90.07 187	90.09 376	83.30 477	72.25 401	98.10 273	81.45 375	95.32 386	96.33 341

Effi-MVS+		92.79 222	92.74 222	92.94 218	95.10 332	83.30 238	94.00 167	97.53 164	91.36 149	89.35 396	90.65 421	94.01 93	98.66 186	87.40 289	95.30 390	96.88 315

blended_shiyan8		88.43 350	87.44 359	91.40 299	92.37 405	79.45 330	87.43 407	93.92 349	82.51 361	91.24 354	85.42 465	74.35 390	98.23 254	84.43 339	95.28 391	96.52 328

MG-MVS		89.54 319	89.80 309	88.76 385	94.88 336	72.47 438	89.60 358	92.44 381	85.82 302	89.48 393	95.98 249	82.85 314	97.74 323	81.87 368	95.27 392	96.08 357

HyFIR lowres test		87.19 381	85.51 394	92.24 259	97.12 166	80.51 294	85.03 452	96.06 277	66.11 480	91.66 345	92.98 373	70.12 413	99.14 100	75.29 431	95.23 393	97.07 301

mvsmamba		90.24 301	89.43 315	92.64 235	95.52 314	82.36 264	96.64 35	92.29 383	81.77 372	92.14 337	96.28 222	70.59 411	99.10 109	84.44 338	95.22 394	96.47 335

BH-untuned		90.68 283	90.90 280	90.05 361	95.98 279	79.57 327	90.04 345	94.94 318	87.91 248	94.07 249	93.00 371	87.76 249	97.78 316	79.19 403	95.17 395	92.80 452

pmmvs3		80.83 440	78.96 448	86.45 426	87.23 479	77.48 376	84.87 453	82.31 472	63.83 485	85.03 446	89.50 432	49.66 473	93.10 456	73.12 448	95.10 396	88.78 476

testing222		80.54 443	78.53 451	86.58 424	92.54 403	68.60 456	86.24 437	82.72 471	83.78 341	82.68 468	84.24 472	39.25 497	95.94 414	60.25 484	95.09 397	95.20 390

mvs_anonymous		90.37 296	91.30 271	87.58 410	92.17 415	68.00 458	89.84 352	94.73 326	83.82 340	93.22 289	97.40 113	87.54 254	97.40 349	87.94 280	95.05 398	97.34 286

test_vis1_n		89.01 335	89.01 322	89.03 379	92.57 400	82.46 263	92.62 233	96.06 277	73.02 448	90.40 371	95.77 262	74.86 388	89.68 475	90.78 180	94.98 399	94.95 402

IterMVS-SCA-FT		91.65 262	91.55 262	91.94 274	93.89 373	79.22 339	87.56 403	93.51 359	91.53 140	95.37 187	96.62 193	78.65 354	98.90 138	91.89 145	94.95 400	97.70 255

test_vis3_rt		90.40 292	90.03 304	91.52 293	92.58 399	88.95 109	90.38 333	97.72 142	73.30 445	97.79 37	97.51 104	77.05 372	87.10 484	89.03 244	94.89 401	98.50 150

test-LLR		83.58 415	83.17 414	84.79 445	89.68 461	66.86 463	83.08 469	84.52 460	83.07 352	82.85 465	84.78 470	62.86 451	93.49 453	82.85 353	94.86 402	94.03 426

test-mter		81.21 436	80.01 444	84.79 445	89.68 461	66.86 463	83.08 469	84.52 460	73.85 441	82.85 465	84.78 470	43.66 487	93.49 453	82.85 353	94.86 402	94.03 426

PatchMatch-RL		89.18 325	88.02 352	92.64 235	95.90 285	92.87 48	88.67 390	91.06 402	80.34 387	90.03 382	91.67 403	83.34 305	94.42 443	76.35 424	94.84 404	90.64 469

OpenMVS_ROB	85.12 16	89.52 320	89.05 320	90.92 329	94.58 354	81.21 287	91.10 304	93.41 362	77.03 420	93.41 275	93.99 345	83.23 308	97.80 312	79.93 394	94.80 405	93.74 434

our_test_3		87.55 369	87.59 357	87.44 412	91.76 427	70.48 446	83.83 466	90.55 410	79.79 392	92.06 340	92.17 394	78.63 356	95.63 418	84.77 333	94.73 406	96.22 351

CHOSEN 280x420		80.04 447	77.97 454	86.23 432	90.13 456	74.53 416	72.87 488	89.59 414	66.38 479	76.29 486	85.32 467	56.96 464	95.36 426	69.49 467	94.72 407	88.79 475

IterMVS		90.18 302	90.16 300	90.21 355	93.15 387	75.98 403	87.56 403	92.97 368	86.43 284	94.09 247	96.40 209	78.32 359	97.43 346	87.87 281	94.69 408	97.23 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EMVS		80.35 444	80.28 442	80.54 466	84.73 491	69.07 454	72.54 489	80.73 480	87.80 253	81.66 475	81.73 482	62.89 450	89.84 474	75.79 429	94.65 409	82.71 486

PLC	85.34 15	90.40 292	88.92 324	94.85 120	96.53 220	90.02 88	91.58 288	96.48 259	80.16 389	86.14 438	92.18 393	85.73 284	98.25 250	76.87 419	94.61 410	96.30 345
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSDG		90.82 277	90.67 290	91.26 309	94.16 364	83.08 248	86.63 427	96.19 273	90.60 173	91.94 341	91.89 399	89.16 224	95.75 417	80.96 383	94.51 411	94.95 402

SD_0403		88.79 342	88.88 327	88.51 392	95.89 287	72.58 436	94.27 153	95.24 309	83.77 342	87.92 423	94.38 331	87.70 250	96.47 397	66.36 474	94.40 412	96.49 333

test_f		86.65 390	87.13 369	85.19 441	90.28 455	86.11 186	86.52 432	91.66 397	69.76 469	95.73 167	97.21 138	69.51 415	81.28 491	89.15 241	94.40 412	88.17 477

xiu_mvs_v2_base		89.00 336	89.19 317	88.46 395	94.86 338	74.63 414	86.97 416	95.60 291	80.88 383	87.83 424	88.62 441	91.04 185	98.81 155	82.51 362	94.38 414	91.93 459

PS-MVSNAJ		88.86 340	88.99 323	88.48 394	94.88 336	74.71 412	86.69 425	95.60 291	80.88 383	87.83 424	87.37 452	90.77 191	98.82 150	82.52 361	94.37 415	91.93 459

EU-MVSNet		87.39 375	86.71 378	89.44 371	93.40 382	76.11 401	94.93 127	90.00 412	57.17 490	95.71 168	97.37 115	64.77 440	97.68 327	92.67 122	94.37 415	94.52 416

E-PMN		80.72 441	80.86 433	80.29 467	85.11 489	68.77 455	72.96 487	81.97 473	87.76 255	83.25 464	83.01 481	62.22 454	89.17 480	77.15 418	94.31 417	82.93 485

GA-MVS		87.70 363	86.82 375	90.31 350	93.27 385	77.22 381	84.72 456	92.79 372	85.11 322	89.82 386	90.07 422	66.80 426	97.76 320	84.56 336	94.27 418	95.96 362

ETVMVS		79.85 448	77.94 455	85.59 435	92.97 392	66.20 468	86.13 439	80.99 479	81.41 377	83.52 461	83.89 473	41.81 495	94.98 436	56.47 488	94.25 419	95.61 383

mvsany_test3		89.11 330	88.21 348	91.83 277	91.30 437	90.25 86	88.09 396	78.76 485	76.37 424	96.43 116	98.39 38	83.79 303	90.43 472	86.57 303	94.20 420	94.80 408

sss		87.23 378	86.82 375	88.46 395	93.96 371	77.94 364	86.84 420	92.78 373	77.59 414	87.61 429	91.83 400	78.75 353	91.92 463	77.84 410	94.20 420	95.52 386

MDA-MVSNet-bldmvs		91.04 275	90.88 281	91.55 290	94.68 351	80.16 300	85.49 448	92.14 388	90.41 179	94.93 220	95.79 258	85.10 292	96.93 379	85.15 325	94.19 422	97.57 266

Syy-MVS		84.81 402	84.93 396	84.42 448	91.71 429	63.36 481	85.89 441	81.49 475	81.03 380	85.13 444	81.64 483	77.44 366	95.00 433	85.94 315	94.12 423	94.91 405

myMVS_eth3d		79.62 449	78.26 452	83.72 454	91.71 429	61.25 485	85.89 441	81.49 475	81.03 380	85.13 444	81.64 483	32.12 498	95.00 433	71.17 462	94.12 423	94.91 405

WB-MVSnew		84.20 409	83.89 409	85.16 442	91.62 432	66.15 469	88.44 394	81.00 478	76.23 425	87.98 421	87.77 448	84.98 294	93.35 455	62.85 483	94.10 425	95.98 361

testing91		83.56 416	82.45 420	86.91 420	92.92 394	67.29 459	86.33 434	88.07 426	86.22 288	84.26 453	85.76 461	48.15 476	97.17 364	76.27 425	94.08 426	96.27 348

PAPM_NR		91.03 276	90.81 285	91.68 285	96.73 195	81.10 288	93.72 179	96.35 264	88.19 242	88.77 408	92.12 396	85.09 293	97.25 357	82.40 364	93.90 427	96.68 322

YYNet1		88.17 356	88.24 345	87.93 404	92.21 412	73.62 426	80.75 478	88.77 417	82.51 361	94.99 218	95.11 293	82.70 317	93.70 451	83.33 349	93.83 428	96.48 334

MDA-MVSNet_test_wron		88.16 357	88.23 346	87.93 404	92.22 411	73.71 425	80.71 479	88.84 416	82.52 360	94.88 223	95.14 291	82.70 317	93.61 452	83.28 350	93.80 429	96.46 336

1112_ss		88.42 351	87.41 361	91.45 296	96.69 199	80.99 290	89.72 356	96.72 238	73.37 444	87.00 434	90.69 419	77.38 368	98.20 257	81.38 377	93.72 430	95.15 393

PVSNet	76.22 20	82.89 423	82.37 421	84.48 447	93.96 371	64.38 477	78.60 482	88.61 418	71.50 457	84.43 452	86.36 458	74.27 392	94.60 440	69.87 466	93.69 431	94.46 417

test_vis1_n_1920		89.45 321	89.85 308	88.28 397	93.59 379	76.71 394	90.67 321	97.78 137	79.67 395	90.30 374	96.11 241	76.62 381	92.17 462	90.31 198	93.57 432	95.96 362

testing99		82.94 422	81.72 424	86.59 423	92.55 401	66.53 465	86.08 440	85.70 446	85.47 315	83.95 456	85.70 462	45.87 480	97.07 371	76.58 422	93.56 433	96.17 355

test_cas_vis1_n_1920		88.25 355	88.27 343	88.20 399	92.19 414	78.92 345	89.45 363	95.44 301	75.29 433	93.23 288	95.65 270	71.58 407	90.23 473	88.05 276	93.55 434	95.44 387

UBG		80.28 446	78.94 449	84.31 450	92.86 395	61.77 482	83.87 465	83.31 468	77.33 417	82.78 467	83.72 474	47.60 478	96.06 410	65.47 477	93.48 435	95.11 397

TESTMET0.1,1		79.09 451	78.04 453	82.25 461	87.52 477	64.03 478	83.08 469	80.62 481	70.28 467	80.16 480	83.22 480	44.13 485	90.56 470	79.95 392	93.36 436	92.15 457

PAPR		87.65 366	86.77 377	90.27 352	92.85 396	77.38 377	88.56 391	96.23 270	76.82 423	84.98 447	89.75 429	86.08 280	97.16 366	72.33 452	93.35 437	96.26 349

SCA		87.43 374	87.21 366	88.10 401	92.01 420	71.98 440	89.43 364	88.11 425	82.26 366	88.71 409	92.83 375	78.65 354	97.59 333	79.61 398	93.30 438	94.75 411

testing11		81.98 431	80.52 438	86.38 429	92.69 398	67.13 460	85.79 443	84.80 459	82.16 367	81.19 478	85.41 466	45.24 482	96.88 382	74.14 441	93.24 439	95.14 394

Test_1112_low_res		87.50 373	86.58 379	90.25 353	96.80 190	77.75 372	87.53 405	96.25 268	69.73 470	86.47 436	93.61 357	75.67 385	97.88 302	79.95 392	93.20 440	95.11 397

MDTV_nov1_ep13			83.88 410		89.42 466	61.52 483	88.74 387	87.41 431	73.99 440	84.96 448	94.01 344	65.25 437	95.53 419	78.02 408	93.16 441

WTY-MVS		86.93 387	86.50 385	88.24 398	94.96 334	74.64 413	87.19 412	92.07 390	78.29 410	88.32 416	91.59 405	78.06 361	94.27 446	74.88 434	93.15 442	95.80 371

UWE-MVS-28		74.73 456	73.18 458	79.35 469	85.42 488	55.55 493	87.63 399	65.92 495	74.39 437	77.33 485	88.19 445	47.63 477	89.48 478	39.01 494	93.14 443	93.03 448

PMMVS		83.00 421	81.11 429	88.66 388	83.81 493	86.44 175	82.24 474	85.65 447	61.75 488	82.07 471	85.64 463	79.75 344	91.59 465	75.99 427	93.09 444	87.94 478

UnsupCasMVSNet_bld		88.50 348	88.03 351	89.90 363	95.52 314	78.88 347	87.39 409	94.02 344	79.32 402	93.06 298	94.02 343	80.72 337	94.27 446	75.16 433	93.08 445	96.54 325

MVS		84.98 401	84.30 402	87.01 416	91.03 442	77.69 374	91.94 270	94.16 339	59.36 489	84.23 454	87.50 451	85.66 285	96.80 386	71.79 454	93.05 446	86.54 481

PatchT		87.51 372	88.17 349	85.55 437	90.64 447	66.91 462	92.02 265	86.09 442	92.20 107	89.05 401	97.16 141	64.15 443	96.37 402	89.21 238	92.98 447	93.37 442

MS-PatchMatch		88.05 358	87.75 354	88.95 380	93.28 384	77.93 365	87.88 398	92.49 380	75.42 429	92.57 318	93.59 358	80.44 339	94.24 448	81.28 378	92.75 448	94.69 414

CR-MVSNet		87.89 359	87.12 370	90.22 354	91.01 443	78.93 343	92.52 237	92.81 370	73.08 447	89.10 398	96.93 164	67.11 423	97.64 330	88.80 252	92.70 449	94.08 423

RPMNet		90.31 300	90.14 303	90.81 336	91.01 443	78.93 343	92.52 237	98.12 75	91.91 117	89.10 398	96.89 167	68.84 416	99.41 43	90.17 208	92.70 449	94.08 423

KD-MVS_2432*1600		82.17 428	80.75 434	86.42 427	82.04 495	70.09 449	81.75 475	90.80 406	82.56 358	90.37 372	89.30 434	42.90 490	96.11 408	74.47 436	92.55 451	93.06 445

miper_refine_blended		82.17 428	80.75 434	86.42 427	82.04 495	70.09 449	81.75 475	90.80 406	82.56 358	90.37 372	89.30 434	42.90 490	96.11 408	74.47 436	92.55 451	93.06 445

BH-w/o		87.21 379	87.02 372	87.79 409	94.77 343	77.27 380	87.90 397	93.21 366	81.74 373	89.99 383	88.39 444	83.47 304	96.93 379	71.29 458	92.43 453	89.15 472

IB-MVS	77.21 19	83.11 419	81.05 430	89.29 375	91.15 441	75.85 404	85.66 445	86.00 443	79.70 394	82.02 473	86.61 455	48.26 474	98.39 231	77.84 410	92.22 454	93.63 437
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
gg-mvs-nofinetune		82.10 430	81.02 431	85.34 439	87.46 478	71.04 443	94.74 131	67.56 494	96.44 28	79.43 482	98.99 11	45.24 482	96.15 406	67.18 472	92.17 455	88.85 474

HY-MVS	82.50 18	86.81 389	85.93 391	89.47 370	93.63 378	77.93 365	94.02 165	91.58 400	75.68 426	83.64 459	93.64 354	77.40 367	97.42 347	71.70 456	92.07 456	93.05 447

TR-MVS		87.70 363	87.17 367	89.27 376	94.11 366	79.26 337	88.69 388	91.86 394	81.94 369	90.69 366	89.79 427	82.82 315	97.42 347	72.65 451	91.98 457	91.14 465

new_pmnet		81.22 435	81.01 432	81.86 462	90.92 445	70.15 448	84.03 463	80.25 483	70.83 462	85.97 439	89.78 428	67.93 422	84.65 489	67.44 471	91.90 458	90.78 468

FPMVS		84.50 406	83.28 413	88.16 400	96.32 244	94.49 19	85.76 444	85.47 452	83.09 351	85.20 443	94.26 333	63.79 446	86.58 486	63.72 480	91.88 459	83.40 484

UnsupCasMVSNet_eth		90.33 298	90.34 298	90.28 351	94.64 353	80.24 299	89.69 357	95.88 283	85.77 303	93.94 257	95.69 268	81.99 326	92.98 459	84.21 342	91.30 460	97.62 261

MVP-Stereo		90.07 309	88.92 324	93.54 188	96.31 245	86.49 172	90.93 310	95.59 295	79.80 391	91.48 347	95.59 271	80.79 336	97.39 350	78.57 407	91.19 461	96.76 320
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		86.46 391	86.33 388	86.87 421	91.65 431	74.54 415	91.94 270	94.10 341	74.28 438	84.78 449	87.33 453	83.03 311	95.00 433	78.72 405	91.16 462	91.06 466

tpm		84.38 407	84.08 405	85.30 440	90.47 452	63.43 480	89.34 367	85.63 448	77.24 419	87.62 428	95.03 298	61.00 458	97.30 353	79.26 402	91.09 463	95.16 392

dmvs_re		84.69 405	83.94 408	86.95 419	92.24 410	82.93 251	89.51 361	87.37 432	84.38 335	85.37 441	85.08 469	72.44 399	86.59 485	68.05 469	91.03 464	91.33 463

CVMVSNet		85.16 399	84.72 397	86.48 425	92.12 416	70.19 447	92.32 252	88.17 424	56.15 491	90.64 367	95.85 253	67.97 421	96.69 389	88.78 253	90.52 465	92.56 454

test0.0.03 1		82.48 425	81.47 428	85.48 438	89.70 460	73.57 427	84.73 454	81.64 474	83.07 352	88.13 419	86.61 455	62.86 451	89.10 481	66.24 475	90.29 466	93.77 433

baseline2		83.38 417	81.54 427	88.90 382	91.38 435	72.84 434	88.78 385	81.22 477	78.97 405	79.82 481	87.56 449	61.73 455	97.80 312	74.30 440	90.05 467	96.05 359

test_vis1_rt		85.58 396	84.58 399	88.60 389	87.97 474	86.76 164	85.45 449	93.59 356	66.43 478	87.64 427	89.20 436	79.33 347	85.38 488	81.59 372	89.98 468	93.66 436

MonoMVSNet		88.46 349	89.28 316	85.98 433	90.52 450	70.07 451	95.31 109	94.81 323	88.38 234	93.47 274	96.13 239	73.21 396	95.07 432	82.61 359	89.12 469	92.81 451

PAPM		81.91 432	80.11 443	87.31 413	93.87 374	72.32 439	84.02 464	93.22 364	69.47 471	76.13 487	89.84 424	72.15 404	97.23 358	53.27 490	89.02 470	92.37 456

MVS-HIRNet		78.83 452	80.60 437	73.51 474	93.07 388	47.37 498	87.10 414	78.00 488	68.94 472	77.53 484	97.26 130	71.45 408	94.62 439	63.28 481	88.74 471	78.55 489

tpm2		81.46 433	80.35 441	84.80 444	89.90 458	65.14 473	90.44 329	85.36 453	65.82 482	82.05 472	92.44 386	57.94 462	96.69 389	70.71 463	88.49 472	92.56 454

CostFormer		83.09 420	82.21 422	85.73 434	89.27 467	67.01 461	90.35 334	86.47 439	70.42 466	83.52 461	93.23 367	61.18 456	96.85 383	77.21 417	88.26 473	93.34 443

GG-mvs-BLEND				83.24 457	85.06 490	71.03 444	94.99 126	65.55 496		74.09 488	75.51 488	44.57 484	94.46 442	59.57 486	87.54 474	84.24 483

PatchmatchNet		85.22 398	84.64 398	86.98 417	89.51 465	69.83 453	90.52 325	87.34 433	78.87 407	87.22 433	92.74 379	66.91 425	96.53 392	81.77 369	86.88 475	94.58 415
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
mvsany_test1		83.91 413	82.93 417	86.84 422	86.18 484	85.93 192	81.11 477	75.03 492	70.80 464	88.57 413	94.63 316	83.08 310	87.38 483	80.39 384	86.57 476	87.21 479

baseline1		87.62 367	87.31 362	88.54 390	94.71 350	74.27 420	93.10 204	88.20 423	86.20 289	92.18 336	93.04 370	73.21 396	95.52 420	79.32 401	85.82 477	95.83 370

tpmvs		84.22 408	83.97 407	84.94 443	87.09 480	65.18 472	91.21 299	88.35 420	82.87 355	85.21 442	90.96 414	65.24 438	96.75 387	79.60 400	85.25 478	92.90 450

ADS-MVSNet2		84.01 410	82.20 423	89.41 372	89.04 468	76.37 399	87.57 401	90.98 404	72.71 451	84.46 450	92.45 384	68.08 419	96.48 395	70.58 464	83.97 479	95.38 388

ADS-MVSNet		82.25 426	81.55 426	84.34 449	89.04 468	65.30 471	87.57 401	85.13 458	72.71 451	84.46 450	92.45 384	68.08 419	92.33 461	70.58 464	83.97 479	95.38 388

JIA-IIPM		85.08 400	83.04 415	91.19 315	87.56 476	86.14 185	89.40 366	84.44 462	88.98 212	82.20 470	97.95 61	56.82 465	96.15 406	76.55 423	83.45 481	91.30 464

MVE	59.87 23	73.86 458	72.65 460	77.47 471	87.00 482	74.35 418	61.37 492	60.93 497	67.27 476	69.69 492	86.49 457	81.24 335	72.33 494	56.45 489	83.45 481	85.74 482
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		78.23 453	78.99 447	75.94 472	91.99 421	55.34 494	88.86 379	78.70 486	82.69 356	81.64 476	79.46 485	75.93 384	85.74 487	48.78 492	82.85 483	86.76 480

EPMVS		81.17 437	80.37 440	83.58 455	85.58 486	65.08 474	90.31 336	71.34 493	77.31 418	85.80 440	91.30 407	59.38 460	92.70 460	79.99 391	82.34 484	92.96 449

tpmrst		82.85 424	82.93 417	82.64 459	87.65 475	58.99 489	90.14 341	87.90 428	75.54 428	83.93 457	91.63 404	66.79 428	95.36 426	81.21 380	81.54 485	93.57 441

tpm cat1		80.61 442	79.46 445	84.07 452	88.78 470	65.06 475	89.26 370	88.23 422	62.27 487	81.90 474	89.66 431	62.70 453	95.29 429	71.72 455	80.60 486	91.86 461

0.4-1-1-0.1		77.15 454	73.55 457	87.95 403	85.49 487	75.84 406	80.59 480	82.87 470	73.51 443	73.61 489	68.65 490	42.84 493	97.22 359	75.20 432	79.18 487	90.80 467

0.4-1-1-0.2		75.80 455	72.05 461	87.04 415	82.70 494	74.17 423	77.51 483	83.48 465	71.80 454	71.57 491	65.16 491	43.07 488	96.96 375	74.34 439	78.78 488	90.00 471

dp		79.28 450	78.62 450	81.24 465	85.97 485	56.45 491	86.91 418	85.26 456	72.97 449	81.45 477	89.17 438	56.01 467	95.45 424	73.19 447	76.68 489	91.82 462

blend_shiyan4		83.29 418	80.66 436	91.19 315	91.86 424	79.59 323	87.05 415	93.91 350	82.66 357	89.60 392	83.36 476	42.82 494	98.10 273	81.45 375	73.26 490	95.87 369

DeepMVS_CX				53.83 476	70.38 499	64.56 476		48.52 500	33.01 494	65.50 494	74.21 489	56.19 466	46.64 497	38.45 495	70.07 491	50.30 492

tmp_tt		37.97 462	44.33 464	18.88 479	11.80 502	21.54 503	63.51 491	45.66 501	4.23 496	51.34 495	50.48 494	59.08 461	22.11 498	44.50 493	68.35 492	13.00 494

PVSNet_0	70.34 21	74.58 457	72.96 459	79.47 468	90.63 448	66.24 467	73.26 486	83.40 467	63.67 486	78.02 483	78.35 487	72.53 398	89.59 476	56.68 487	60.05 493	82.57 487

test_method		50.44 460	48.94 463	54.93 475	39.68 501	12.38 504	28.59 493	90.09 411	6.82 495	41.10 497	78.41 486	54.41 468	70.69 495	50.12 491	51.26 494	81.72 488

dongtai		53.72 459	53.79 462	53.51 477	79.69 497	36.70 501	77.18 484	32.53 503	71.69 455	68.63 493	60.79 492	26.65 500	73.11 493	30.67 496	36.29 495	50.73 491

kuosan		43.63 461	44.25 465	41.78 478	66.04 500	34.37 502	75.56 485	32.62 502	53.25 493	50.46 496	51.18 493	25.28 501	49.13 496	13.44 497	30.41 496	41.84 493

test123		9.49 464	12.01 467	1.91 480	2.87 503	1.30 505	82.38 473	1.34 505	1.36 498	2.84 499	6.56 497	2.45 502	0.97 499	2.73 498	5.56 497	3.47 495

testmvs		9.02 465	11.42 468	1.81 481	2.77 504	1.13 506	79.44 481	1.90 504	1.18 499	2.65 500	6.80 496	1.95 503	0.87 500	2.62 499	3.45 498	3.44 496

mmdepth		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

monomultidepth		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

test_blank		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

uanet_test		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

DCPMVS		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

cdsmvs_eth3d_5k		23.35 463	31.13 466	0.00 482	0.00 505	0.00 507	0.00 494	95.58 297	0.00 500	0.00 501	91.15 409	93.43 106	0.00 501	0.00 500	0.00 499	0.00 497

pcd_1.5k_mvsjas		7.56 466	10.09 469	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	90.77 191	0.00 501	0.00 500	0.00 499	0.00 497

sosnet-low-res		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

sosnet		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

uncertanet		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

Regformer		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

ab-mvs-re		7.56 466	10.08 470	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	90.69 419	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

uanet		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

TestfortrainingZip							96.32 56

WAC-MVS						61.25 485								74.55 435

FOURS1					99.21 3	94.68 15	98.45 4	98.81 10	97.73 9	98.27 23

test_one_0601					98.26 80	87.14 152		98.18 63	94.25 61	96.99 87	97.36 118	95.13 49

eth-test2					0.00 505

eth-test					0.00 505

test_241102_ONE					98.51 58	86.97 157		98.10 79	91.85 121	97.63 43	97.03 156	96.48 13	98.95 134

save fliter					97.46 143	88.05 134	92.04 264	97.08 205	87.63 259

test0726					98.51 58	86.69 167	95.34 105	98.18 63	91.85 121	97.63 43	97.37 115	95.58 28

GSMVS																94.75 411

test_part2					98.21 84	89.41 99				96.72 100

sam_mvs1												66.64 429				94.75 411

sam_mvs												66.41 430

MTGPA								97.62 150

test_post1							90.21 338				5.85 499	65.36 436	96.00 412	79.61 398

test_post											6.07 498	65.74 434	95.84 416

patchmatchnet-post											91.71 402	66.22 432	97.59 333

MTMP							94.82 129	54.62 499

gm-plane-assit					87.08 481	59.33 488			71.22 458		83.58 475		97.20 361	73.95 442

TEST9					96.45 226	89.46 96	90.60 323	96.92 217	79.09 404	90.49 368	94.39 329	91.31 173	98.88 141

test_8					96.37 235	89.14 106	90.51 326	96.89 220	79.37 399	90.42 370	94.36 332	91.20 178	98.82 150

agg_prior					96.20 257	88.89 111		96.88 225		90.21 375			98.78 163

test_prior4						89.91 89	90.74 318

test_prior				94.61 133	95.95 281	87.23 149		97.36 181					98.68 184			97.93 221

旧先验2							90.00 347		68.65 473	92.71 313			96.52 393	85.15 325

新几何2							90.02 346

无先验							89.94 348	95.75 287	70.81 463				98.59 198	81.17 381		94.81 407

原ACMM2							89.34 367

testdata2													98.03 285	80.24 388

segment_acmp												92.14 149

testdata1							88.96 377		88.44 232

plane_prior7					97.71 124	88.68 115

plane_prior6					97.21 159	88.23 128						86.93 267

plane_prior4											95.59 271

plane_prior3						88.43 126			90.35 180	93.31 280

plane_prior2							94.56 143		91.74 132

plane_prior1					97.38 147

n2								0.00 506

nn								0.00 506

door-mid								92.13 389

test11								96.65 246

door								91.26 401

HQP5-MVS						84.89 212

HQP-NCC					96.36 237		91.37 293		87.16 269	88.81 404

ACMP_Plane					96.36 237		91.37 293		87.16 269	88.81 404

BP-MVS														86.55 305

HQP4-MVS										88.81 404			98.61 194			98.15 193

HQP2-MVS												84.76 295

NP-MVS					96.82 188	87.10 153					93.40 362

MDTV_nov1_ep13_2view						42.48 500	88.45 393		67.22 477	83.56 460		66.80 426		72.86 450		94.06 425

Test By Simon												90.61 197

High-resolution multi-view benchmark