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		99.40 1	99.26 1	99.84 2	99.98 2	99.51 2	99.98 6	98.69 65	98.20 3	99.93 1	99.98 2	96.82 14	100.00 1	99.75 10	100.00 1	99.99 12

NCCC		99.37 2	99.25 2	99.71 5	99.96 8	99.15 9	99.97 12	98.62 77	98.02 6	99.90 2	99.95 3	97.33 12	100.00 1	99.54 20	100.00 1	100.00 1

TSAR-MVS + MP.		98.93 12	98.77 13	99.41 31	99.74 59	98.67 36	99.77 114	98.38 137	96.73 27	99.88 3	99.74 63	94.89 58	99.59 120	99.80 6	99.98 25	99.97 53

xiu_mvs_v2_base		98.23 51	97.97 50	99.02 73	98.69 125	98.66 37	99.52 172	98.08 173	97.05 17	99.86 4	99.86 17	90.65 138	99.71 109	99.39 28	98.63 107	98.69 197

PS-MVSNAJ		98.44 39	98.20 40	99.16 49	98.80 121	98.92 15	99.54 170	98.17 162	97.34 12	99.85 5	99.85 21	91.20 130	99.89 70	99.41 27	99.67 75	98.69 197

旧先验2							99.46 181		94.21 90	99.85 5			99.95 51	96.96 109

SD-MVS		98.92 13	98.70 14	99.56 16	99.70 67	98.73 33	99.94 46	98.34 143	96.38 35	99.81 7	99.76 56	94.59 61	99.98 32	99.84 5	99.96 36	99.97 53

MSLP-MVS++		99.13 5	99.01 7	99.49 23	99.94 14	98.46 52	99.98 6	98.86 55	97.10 16	99.80 8	99.94 4	95.92 31	100.00 1	99.51 21	100.00 1	100.00 1

SteuartSystems-ACMMP		99.02 9	98.97 9	99.18 45	98.72 124	97.71 74	99.98 6	98.44 113	96.85 21	99.80 8	99.91 8	97.57 4	99.85 81	99.44 25	99.99 14	99.99 12
Skip Steuart: Steuart Systems R&D Blog.
testdata				98.42 117	99.47 81	95.33 162		98.56 87	93.78 110	99.79 10	99.85 21	93.64 92	99.94 59	94.97 134	99.94 43	100.00 1

SMA-MVS		98.76 21	98.48 25	99.62 11	99.87 41	98.87 19	99.86 89	98.38 137	93.19 123	99.77 11	99.94 4	95.54 38	100.00 1	99.74 12	99.99 14	100.00 1

CDPH-MVS		98.65 24	98.36 34	99.49 23	99.94 14	98.73 33	99.87 72	98.33 144	93.97 102	99.76 12	99.87 15	94.99 54	99.75 101	98.55 65	100.00 1	99.98 43

APD-MVS		98.62 25	98.35 35	99.41 31	99.90 34	98.51 49	99.87 72	98.36 141	94.08 94	99.74 13	99.73 65	94.08 78	99.74 105	99.42 26	99.99 14	99.99 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_prior3		98.99 11	98.84 12	99.43 27	99.94 14	98.49 50	99.95 32	98.65 70	95.78 50	99.73 14	99.76 56	96.00 27	99.80 92	99.78 8	100.00 1	99.99 12

test_prior2							99.95 32		95.78 50	99.73 14	99.76 56	96.00 27		99.78 8	100.00 1

TEST9					99.92 27	98.92 15	99.96 20	98.43 119	93.90 106	99.71 16	99.86 17	95.88 32	99.85 81

train_agg		98.88 15	98.65 16	99.59 14	99.92 27	98.92 15	99.96 20	98.43 119	94.35 85	99.71 16	99.86 17	95.94 29	99.85 81	99.69 18	99.98 25	99.99 12

test_8					99.92 27	98.88 18	99.96 20	98.43 119	94.35 85	99.69 18	99.85 21	95.94 29	99.85 81

agg_prior3		98.84 17	98.62 18	99.47 26	99.92 27	98.56 46	99.96 20	98.43 119	94.07 95	99.67 19	99.85 21	96.05 25	99.85 81	99.69 18	99.98 25	99.99 12

test12				99.43 27	99.74 59	98.56 46		98.40 131		99.65 20		94.76 59	99.75 101		99.98 25	99.99 12

ESAPD		99.26 4	99.10 5	99.74 3	99.89 37	99.24 7	99.87 72	98.44 113	97.48 11	99.64 21	99.94 4	96.68 15	99.99 27	99.99 1	100.00 1	99.99 12

agg_prior1		98.88 15	98.66 15	99.54 18	99.93 24	98.77 26	99.96 20	98.43 119	94.63 78	99.63 22	99.85 21	95.79 35	99.85 81	99.72 16	99.99 14	99.99 12

agg_prior					99.93 24	98.77 26		98.43 119		99.63 22			99.85 81

xiu_mvs_v1_base_debu		97.43 75	97.06 76	98.55 101	97.74 177	98.14 59	99.31 198	97.86 194	96.43 32	99.62 24	99.69 73	85.56 192	99.68 113	99.05 35	98.31 113	97.83 206

xiu_mvs_v1_base		97.43 75	97.06 76	98.55 101	97.74 177	98.14 59	99.31 198	97.86 194	96.43 32	99.62 24	99.69 73	85.56 192	99.68 113	99.05 35	98.31 113	97.83 206

xiu_mvs_v1_base_debi		97.43 75	97.06 76	98.55 101	97.74 177	98.14 59	99.31 198	97.86 194	96.43 32	99.62 24	99.69 73	85.56 192	99.68 113	99.05 35	98.31 113	97.83 206

原ACMM1				98.96 77	99.73 63	96.99 108		98.51 103	94.06 98	99.62 24	99.85 21	94.97 55	99.96 43	95.11 132	99.95 39	99.92 69

PHI-MVS		98.41 41	98.21 39	99.03 71	99.86 42	97.10 106	99.98 6	98.80 60	90.78 213	99.62 24	99.78 50	95.30 44	100.00 1	99.80 6	99.93 48	99.99 12

新几何1				99.42 30	99.75 58	98.27 57		98.63 76	92.69 142	99.55 29	99.82 40	94.40 64	100.00 1	91.21 201	99.94 43	99.99 12

ACMMP_Plus		98.49 36	98.14 43	99.54 18	99.66 69	98.62 41	99.85 91	98.37 140	94.68 77	99.53 30	99.83 37	92.87 104	100.00 1	98.66 61	99.84 60	99.99 12

1121		98.03 57	97.57 63	99.40 33	99.74 59	98.21 58	98.31 285	98.62 77	92.78 137	99.53 30	99.83 37	95.08 48	100.00 1	94.36 150	99.92 50	99.99 12

PMMVS		96.76 104	96.76 89	96.76 174	98.28 142	92.10 239	99.91 59	97.98 180	94.12 92	99.53 30	99.39 98	86.93 181	98.73 163	96.95 110	97.73 123	99.45 136

HSP-MVS		99.07 6	99.11 4	98.95 78	99.93 24	97.24 99	99.95 32	98.32 145	97.50 10	99.52 33	99.88 12	97.43 9	99.71 109	99.50 22	99.98 25	99.89 72

test_part2					99.89 37	99.25 6				99.49 34

v1.0		41.15 342	54.87 336	0.00 359	99.89 37	0.00 374	0.00 365	98.41 129	96.14 44	99.49 34	99.91 8	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

APDe-MVS		99.06 8	98.91 10	99.51 21	99.94 14	98.76 32	99.91 59	98.39 133	97.20 15	99.46 36	99.85 21	95.53 41	99.79 94	99.86 4	100.00 1	99.99 12

region2R		98.54 32	98.37 32	99.05 69	99.96 8	97.18 102	99.96 20	98.55 91	94.87 71	99.45 37	99.85 21	94.07 79	100.00 1	98.67 58	100.00 1	99.98 43

HPM-MVS++		99.07 6	98.88 11	99.63 8	99.90 34	99.02 12	99.95 32	98.56 87	97.56 9	99.44 38	99.85 21	95.38 43	100.00 1	99.31 29	99.99 14	99.87 75

MVSFormer		96.94 95	96.60 93	97.95 139	97.28 194	97.70 76	99.55 168	97.27 248	91.17 202	99.43 39	99.54 88	90.92 136	96.89 276	94.67 145	99.62 78	99.25 163

lupinMVS		97.85 62	97.60 61	98.62 95	97.28 194	97.70 76	99.99 3	97.55 216	95.50 60	99.43 39	99.67 77	90.92 136	98.71 165	98.40 69	99.62 78	99.45 136

Regformer-1		98.79 19	98.60 20	99.36 36	99.85 43	98.34 54	99.87 72	98.52 97	96.05 45	99.41 41	99.79 45	94.93 56	99.76 98	99.07 34	99.90 52	99.99 12

Regformer-2		98.78 20	98.59 21	99.36 36	99.85 43	98.32 55	99.87 72	98.52 97	96.04 46	99.41 41	99.79 45	94.92 57	99.76 98	99.05 35	99.90 52	99.98 43

XVS		98.70 23	98.55 22	99.15 53	99.94 14	97.50 84	99.94 46	98.42 127	96.22 40	99.41 41	99.78 50	94.34 68	99.96 43	98.92 44	99.95 39	99.99 12

X-MVStestdata		93.83 183	92.06 205	99.15 53	99.94 14	97.50 84	99.94 46	98.42 127	96.22 40	99.41 41	41.37 369	94.34 68	99.96 43	98.92 44	99.95 39	99.99 12

APD-MVS_3200maxsize		98.25 50	98.08 46	98.78 85	99.81 53	96.60 119	99.82 100	98.30 147	93.95 104	99.37 45	99.77 52	92.84 105	99.76 98	98.95 41	99.92 50	99.97 53

PGM-MVS		98.34 44	98.13 44	98.99 75	99.92 27	97.00 107	99.75 121	99.50 21	93.90 106	99.37 45	99.76 56	93.24 99	100.00 1	97.75 93	99.96 36	99.98 43

HFP-MVS		98.56 30	98.37 32	99.14 55	99.96 8	97.43 88	99.95 32	98.61 79	94.77 73	99.31 47	99.85 21	94.22 73	100.00 1	98.70 56	99.98 25	99.98 43

#test#		98.59 28	98.41 27	99.14 55	99.96 8	97.43 88	99.95 32	98.61 79	95.00 68	99.31 47	99.85 21	94.22 73	100.00 1	98.78 53	99.98 25	99.98 43

ACMMPR		98.50 35	98.32 36	99.05 69	99.96 8	97.18 102	99.95 32	98.60 81	94.77 73	99.31 47	99.84 35	93.73 89	100.00 1	98.70 56	99.98 25	99.98 43

test222					99.55 76	97.41 91	99.34 193	98.55 91	91.86 178	99.27 50	99.83 37	93.84 87			99.95 39	99.99 12

abl_6		97.67 71	97.34 69	98.66 92	99.68 68	96.11 141	99.68 143	98.14 168	93.80 109	99.27 50	99.70 70	88.65 165	99.98 32	97.46 96	99.72 72	99.89 72

CANet_DTU		96.76 104	96.15 105	98.60 97	98.78 122	97.53 80	99.84 95	97.63 208	97.25 14	99.20 52	99.64 81	81.36 235	99.98 32	92.77 186	98.89 101	98.28 200

EPNet		98.49 36	98.40 29	98.77 86	99.62 71	96.80 114	99.90 62	99.51 20	97.60 8	99.20 52	99.36 101	93.71 90	99.91 65	97.99 83	98.71 106	99.61 110
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	95.94 2	97.71 70	98.98 8	93.92 259	99.63 70	81.76 334	99.96 20	98.56 87	99.47 1	99.19 54	99.99 1	94.16 77	100.00 1	99.92 2	99.93 48	100.00 1

VNet		97.21 86	96.57 95	99.13 60	98.97 101	97.82 72	99.03 229	99.21 31	94.31 87	99.18 55	98.88 134	86.26 187	99.89 70	98.93 43	94.32 192	99.69 97

MCST-MVS		99.32 3	99.14 3	99.86 1	99.97 3	99.59 1	99.97 12	98.64 73	98.47 2	99.13 56	99.92 7	96.38 24	100.00 1	99.74 12	100.00 1	100.00 1

DeepC-MVS_fast	96.59 1	98.81 18	98.54 23	99.62 11	99.90 34	98.85 21	99.24 206	98.47 109	98.14 4	99.08 57	99.91 8	93.09 102	100.00 1	99.04 39	99.99 14	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs1		96.98 93	96.57 95	98.22 127	98.06 154	96.28 127	99.58 162	97.41 234	92.98 128	99.06 58	97.44 193	90.11 144	98.77 159	96.88 112	97.64 127	99.59 114

114514_t		97.41 79	96.83 84	99.14 55	99.51 80	97.83 71	99.89 67	98.27 152	88.48 247	99.06 58	99.66 79	90.30 141	99.64 119	96.32 117	99.97 34	99.96 57

PVSNet	91.05 13	97.13 88	96.69 91	98.45 114	99.52 78	95.81 146	99.95 32	99.65 16	94.73 75	99.04 60	99.21 109	84.48 200	99.95 51	94.92 135	98.74 105	99.58 118

CHOSEN 280x420		99.01 10	99.03 6	98.95 78	99.38 85	98.87 19	98.46 275	99.42 25	97.03 18	99.02 61	99.09 113	99.35 1	98.21 208	99.73 15	99.78 68	99.77 87

Regformer-3		98.58 29	98.41 27	99.10 61	99.84 48	97.57 78	99.66 148	98.52 97	95.79 49	99.01 62	99.77 52	94.40 64	99.75 101	98.82 49	99.83 61	99.98 43

Regformer-4		98.56 30	98.39 30	99.08 63	99.84 48	97.52 81	99.66 148	98.52 97	95.76 52	99.01 62	99.77 52	94.33 70	99.75 101	98.80 52	99.83 61	99.98 43

MG-MVS		98.91 14	98.65 16	99.68 7	99.94 14	99.07 11	99.64 155	99.44 23	97.33 13	99.00 64	99.72 66	94.03 80	99.98 32	98.73 55	100.00 1	100.00 1

HPM-MVS_fast		97.80 66	97.50 64	98.68 90	99.79 55	96.42 123	99.88 69	98.16 165	91.75 182	98.94 65	99.54 88	91.82 125	99.65 118	97.62 95	99.99 14	99.99 12

CP-MVS		98.45 38	98.32 36	98.87 82	99.96 8	96.62 118	99.97 12	98.39 133	94.43 83	98.90 66	99.87 15	94.30 71	100.00 1	99.04 39	99.99 14	99.99 12

MVS_0304		97.52 74	96.79 87	99.69 6	99.59 72	99.30 4	99.97 12	98.01 177	96.99 20	98.84 67	99.79 45	78.90 266	99.96 43	99.74 12	99.32 95	99.81 80

MVS_Test		96.46 124	95.74 132	98.61 96	98.18 149	97.23 100	99.31 198	97.15 256	91.07 206	98.84 67	97.05 208	88.17 169	98.97 150	94.39 149	97.50 129	99.61 110

API-MVS		97.86 61	97.66 58	98.47 112	99.52 78	95.41 160	99.47 179	98.87 54	91.68 184	98.84 67	99.85 21	92.34 114	99.99 27	98.44 68	99.96 36	100.00 1

GST-MVS		98.27 47	97.97 50	99.17 48	99.92 27	97.57 78	99.93 51	98.39 133	94.04 100	98.80 70	99.74 63	92.98 103	100.00 1	98.16 74	99.76 69	99.93 65

MVS_111021_LR		98.42 40	98.38 31	98.53 105	99.39 84	95.79 147	99.87 72	99.86 2	96.70 28	98.78 71	99.79 45	92.03 120	99.90 66	99.17 31	99.86 59	99.88 74

TSAR-MVS + GP.		98.60 26	98.51 24	98.86 83	99.73 63	96.63 117	99.97 12	97.92 187	98.07 5	98.76 72	99.55 86	95.00 53	99.94 59	99.91 3	97.68 125	99.99 12

sss		97.57 73	97.03 80	99.18 45	98.37 139	98.04 64	99.73 130	99.38 26	93.46 118	98.76 72	99.06 115	91.21 129	99.89 70	96.33 116	97.01 145	99.62 108

CostFormer		96.10 138	95.88 121	96.78 173	97.03 199	92.55 231	97.08 314	97.83 197	90.04 224	98.72 74	94.89 284	95.01 52	98.29 202	96.54 115	95.77 167	99.50 132

tpmrst		96.27 137	95.98 112	97.13 165	97.96 159	93.15 215	96.34 324	98.17 162	92.07 172	98.71 75	95.12 268	93.91 84	98.73 163	94.91 137	96.62 150	99.50 132

casdiffmvs		96.67 112	96.14 106	98.27 125	97.95 161	96.49 121	99.48 177	97.29 246	92.09 170	98.67 76	97.12 202	89.10 158	98.74 162	96.27 118	97.25 138	99.57 120

MVS_111021_HR		98.72 22	98.62 18	99.01 74	99.36 86	97.18 102	99.93 51	99.90 1	96.81 25	98.67 76	99.77 52	93.92 82	99.89 70	99.27 30	99.94 43	99.96 57

MAR-MVS		97.43 75	97.19 73	98.15 132	99.47 81	94.79 176	99.05 227	98.76 61	92.65 146	98.66 78	99.82 40	88.52 166	99.98 32	98.12 76	99.63 77	99.67 100
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
Effi-MVS+		96.30 134	95.69 133	98.16 129	97.85 168	96.26 129	97.41 307	97.21 251	90.37 217	98.65 79	98.58 167	86.61 184	98.70 166	97.11 104	97.37 134	99.52 129

HPM-MVS		97.96 58	97.72 57	98.68 90	99.84 48	96.39 126	99.90 62	98.17 162	92.61 148	98.62 80	99.57 85	91.87 123	99.67 116	98.87 47	99.99 14	99.99 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		98.39 43	98.20 40	98.97 76	99.97 3	96.92 111	99.95 32	98.38 137	95.04 67	98.61 81	99.80 44	93.39 94	100.00 1	98.64 62	100.00 1	99.98 43

jason		97.24 84	96.86 83	98.38 122	95.73 236	97.32 98	99.97 12	97.40 236	95.34 63	98.60 82	99.54 88	87.70 171	98.56 176	97.94 86	99.47 89	99.25 163
jason: jason.
CANet		98.27 47	97.82 56	99.63 8	99.72 65	99.10 10	99.98 6	98.51 103	97.00 19	98.52 83	99.71 68	87.80 170	99.95 51	99.75 10	99.38 93	99.83 78

EI-MVSNet-Vis-set		98.27 47	98.11 45	98.75 87	99.83 51	96.59 120	99.40 185	98.51 103	95.29 64	98.51 84	99.76 56	93.60 93	99.71 109	98.53 66	99.52 86	99.95 62

LFMVS		94.75 167	93.56 179	98.30 124	99.03 95	95.70 154	98.74 252	97.98 180	87.81 256	98.47 85	99.39 98	67.43 326	99.53 121	98.01 81	95.20 178	99.67 100

tpm2		95.47 153	95.18 151	96.35 188	96.91 204	91.70 254	96.96 317	97.93 185	88.04 254	98.44 86	95.40 253	93.32 96	97.97 217	94.00 160	95.61 171	99.38 148

alignmvs		97.81 65	97.33 70	99.25 40	98.77 123	98.66 37	99.99 3	98.44 113	94.40 84	98.41 87	99.47 92	93.65 91	99.42 137	98.57 64	94.26 194	99.67 100

UA-Net		96.54 115	95.96 115	98.27 125	98.23 146	95.71 153	98.00 300	98.45 112	93.72 112	98.41 87	99.27 103	88.71 164	99.66 117	91.19 202	97.69 124	99.44 138

DP-MVS Recon		98.41 41	98.02 47	99.56 16	99.97 3	98.70 35	99.92 54	98.44 113	92.06 174	98.40 89	99.84 35	95.68 36	100.00 1	98.19 72	99.71 73	99.97 53

CPTT-MVS		97.64 72	97.32 71	98.58 100	99.97 3	95.77 148	99.96 20	98.35 142	89.90 225	98.36 90	99.79 45	91.18 133	99.99 27	98.37 70	99.99 14	99.99 12

PAPM		98.60 26	98.42 26	99.14 55	96.05 224	98.96 13	99.90 62	99.35 27	96.68 29	98.35 91	99.66 79	96.45 23	98.51 179	99.45 24	99.89 54	99.96 57

HY-MVS	92.50 7	97.79 67	97.17 75	99.63 8	98.98 100	99.32 3	97.49 306	99.52 18	95.69 56	98.32 92	97.41 195	93.32 96	99.77 96	98.08 80	95.75 169	99.81 80

EI-MVSNet-UG-set		98.14 53	97.99 49	98.60 97	99.80 54	96.27 128	99.36 192	98.50 107	95.21 66	98.30 93	99.75 61	93.29 98	99.73 108	98.37 70	99.30 96	99.81 80

PVSNet_BlendedMVS		96.05 140	95.82 130	96.72 176	99.59 72	96.99 108	99.95 32	99.10 32	94.06 98	98.27 94	95.80 241	89.00 160	99.95 51	99.12 32	87.53 248	93.24 303

PVSNet_Blended		97.94 59	97.64 59	98.83 84	99.59 72	96.99 108	100.00 1	99.10 32	95.38 61	98.27 94	99.08 114	89.00 160	99.95 51	99.12 32	99.25 97	99.57 120

MP-MVS		98.23 51	97.97 50	99.03 71	99.94 14	97.17 105	99.95 32	98.39 133	94.70 76	98.26 96	99.81 43	91.84 124	100.00 1	98.85 48	99.97 34	99.93 65
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
WTY-MVS		98.10 55	97.60 61	99.60 13	98.92 107	99.28 5	99.89 67	99.52 18	95.58 58	98.24 97	99.39 98	93.33 95	99.74 105	97.98 85	95.58 172	99.78 86

DELS-MVS		98.54 32	98.22 38	99.50 22	99.15 91	98.65 39	100.00 1	98.58 83	97.70 7	98.21 98	99.24 107	92.58 112	99.94 59	98.63 63	99.94 43	99.92 69
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
diffmvs		96.06 139	95.46 141	97.89 143	97.92 163	95.28 164	99.16 213	97.28 247	91.73 183	98.16 99	96.74 221	87.48 174	98.81 155	93.69 171	96.95 147	99.58 118

diffmvs1		96.38 130	95.79 131	98.13 133	97.97 158	96.07 142	99.32 197	97.33 242	92.42 160	98.10 100	96.90 212	88.50 167	98.83 153	94.18 157	97.42 132	99.48 134

DWT-MVSNet_test		97.31 81	97.19 73	97.66 149	98.24 145	94.67 178	98.86 247	98.20 160	93.60 116	98.09 101	98.89 132	97.51 5	98.78 158	94.04 159	97.28 135	99.55 123

MDTV_nov1_ep13_2view						96.26 129	96.11 327		91.89 177	98.06 102		94.40 64		94.30 153		99.67 100

PAPR		98.52 34	98.16 42	99.58 15	99.97 3	98.77 26	99.95 32	98.43 119	95.35 62	98.03 103	99.75 61	94.03 80	99.98 32	98.11 77	99.83 61	99.99 12

MDTV_nov1_ep13			95.69 133		97.90 164	94.15 185	95.98 329	98.44 113	93.12 125	97.98 104	95.74 243	95.10 47	98.58 175	90.02 222	96.92 148

GG-mvs-BLEND				98.54 104	98.21 147	98.01 65	93.87 339	98.52 97		97.92 105	97.92 187	99.02 2	97.94 221	98.17 73	99.58 83	99.67 100

0601test		97.83 63	97.37 67	99.21 42	99.18 88	97.98 66	99.64 155	99.27 29	91.43 192	97.88 106	98.99 122	95.84 33	99.84 89	98.82 49	95.32 176	99.79 83

Anonymous20240521		97.83 63	97.37 67	99.21 42	99.18 88	97.98 66	99.64 155	99.27 29	91.43 192	97.88 106	98.99 122	95.84 33	99.84 89	98.82 49	95.32 176	99.79 83

canonicalmvs		97.09 91	96.32 101	99.39 34	98.93 105	98.95 14	99.72 135	97.35 240	94.45 81	97.88 106	99.42 94	86.71 182	99.52 122	98.48 67	93.97 205	99.72 94

PatchFormer-LS_test		97.01 92	96.79 87	97.69 148	98.26 144	94.80 174	98.66 263	98.13 170	93.70 113	97.86 109	98.80 150	95.54 38	98.67 167	94.12 158	96.00 160	99.60 112

VDDNet		93.12 197	91.91 207	96.76 174	96.67 217	92.65 229	98.69 257	98.21 157	82.81 309	97.75 110	99.28 102	61.57 341	99.48 131	98.09 79	94.09 197	98.15 202

EPMVS		96.53 116	96.01 109	98.09 136	98.43 138	96.12 140	96.36 323	99.43 24	93.53 117	97.64 111	95.04 274	94.41 63	98.38 196	91.13 203	98.11 117	99.75 89

JIA-IIPM		91.76 225	90.70 221	94.94 219	96.11 222	87.51 305	93.16 342	98.13 170	75.79 339	97.58 112	77.68 354	92.84 105	97.97 217	88.47 240	96.54 151	99.33 155

EPNet_dtu		95.71 148	95.39 144	96.66 178	98.92 107	93.41 206	99.57 164	98.90 52	96.19 42	97.52 113	98.56 169	92.65 110	97.36 236	77.89 321	98.33 112	99.20 166
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PAPM_NR		98.12 54	97.93 54	98.70 89	99.94 14	96.13 137	99.82 100	98.43 119	94.56 79	97.52 113	99.70 70	94.40 64	99.98 32	97.00 107	99.98 25	99.99 12

thisisatest0515		97.41 79	97.02 81	98.59 99	97.71 183	97.52 81	99.97 12	98.54 94	91.83 179	97.45 115	99.04 116	97.50 6	99.10 143	94.75 142	96.37 155	99.16 172

OMC-MVS		97.28 82	97.23 72	97.41 158	99.76 56	93.36 210	99.65 151	97.95 183	96.03 47	97.41 116	99.70 70	89.61 147	99.51 123	96.73 114	98.25 116	99.38 148

gg-mvs-nofinetune		93.51 192	91.86 208	98.47 112	97.72 181	97.96 68	92.62 344	98.51 103	74.70 343	97.33 117	69.59 358	98.91 3	97.79 224	97.77 92	99.56 84	99.67 100

PatchT		90.38 255	88.75 267	95.25 207	95.99 226	90.16 278	91.22 351	97.54 218	76.80 335	97.26 118	86.01 349	91.88 122	96.07 303	66.16 344	95.91 164	99.51 130

PLC	95.54 3	97.93 60	97.89 55	98.05 137	99.82 52	94.77 177	99.92 54	98.46 111	93.93 105	97.20 119	99.27 103	95.44 42	99.97 41	97.41 97	99.51 88	99.41 141
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpmp4_e23		95.15 159	94.69 160	96.55 180	97.84 169	91.77 249	97.10 313	97.91 188	88.33 250	97.19 120	95.06 272	93.92 82	98.51 179	89.64 225	95.19 179	99.37 150

zzz-MVS		98.33 45	98.00 48	99.30 38	99.85 43	97.93 69	99.80 105	98.28 149	95.76 52	97.18 121	99.88 12	92.74 108	100.00 1	98.67 58	99.88 56	99.99 12

MTAPA		98.29 46	97.96 53	99.30 38	99.85 43	97.93 69	99.39 188	98.28 149	95.76 52	97.18 121	99.88 12	92.74 108	100.00 1	98.67 58	99.88 56	99.99 12

PatchmatchNet		95.94 143	95.45 142	97.39 160	97.83 170	94.41 181	96.05 328	98.40 131	92.86 130	97.09 123	95.28 265	94.21 76	98.07 214	89.26 234	98.11 117	99.70 95
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thisisatest0530		97.10 89	96.72 90	98.22 127	97.60 185	96.70 115	99.92 54	98.54 94	91.11 205	97.07 124	98.97 126	97.47 7	99.03 147	93.73 169	96.09 158	98.92 188

CR-MVSNet		93.45 195	92.62 195	95.94 195	96.29 219	92.66 227	92.01 347	96.23 308	92.62 147	96.94 125	93.31 314	91.04 134	96.03 304	79.23 314	95.96 162	99.13 181

RPMNet		89.39 274	87.20 286	95.94 195	96.29 219	92.66 227	92.01 347	97.63 208	70.19 351	96.94 125	85.87 350	87.25 177	96.03 304	62.69 347	95.96 162	99.13 181

Vis-MVSNet		95.72 146	95.15 152	97.45 156	97.62 184	94.28 183	99.28 203	98.24 154	94.27 89	96.84 127	98.94 130	79.39 258	98.76 161	93.25 177	98.49 108	99.30 158
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
VDD-MVS		93.77 187	92.94 190	96.27 189	98.55 133	90.22 277	98.77 251	97.79 199	90.85 211	96.82 128	99.42 94	61.18 343	99.77 96	98.95 41	94.13 196	98.82 193

UGNet		95.33 155	94.57 161	97.62 151	98.55 133	94.85 172	98.67 260	99.32 28	95.75 55	96.80 129	96.27 233	72.18 306	99.96 43	94.58 147	99.05 100	98.04 204
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
AdaColmap		97.23 85	96.80 86	98.51 106	99.99 1	95.60 156	99.09 216	98.84 57	93.32 121	96.74 130	99.72 66	86.04 188	100.00 1	98.01 81	99.43 92	99.94 64

tpm		93.70 190	93.41 185	94.58 235	95.36 246	87.41 307	97.01 315	96.90 287	90.85 211	96.72 131	94.14 303	90.40 140	96.84 279	90.75 212	88.54 236	99.51 130

tttt0517		96.85 99	96.49 98	97.92 141	97.48 191	95.89 145	99.85 91	98.54 94	90.72 214	96.63 132	98.93 131	97.47 7	99.02 148	93.03 184	95.76 168	98.85 192

mvs-test1		95.53 151	95.97 114	94.20 247	97.77 174	85.44 317	99.95 32	97.06 260	94.92 69	96.58 133	98.72 157	85.81 189	98.98 149	94.80 139	98.11 117	98.18 201

CNLPA		97.76 68	97.38 66	98.92 80	99.53 77	96.84 112	99.87 72	98.14 168	93.78 110	96.55 134	99.69 73	92.28 115	99.98 32	97.13 103	99.44 91	99.93 65

tfpn_ndepth		97.21 86	96.63 92	98.92 80	99.06 93	98.28 56	99.95 32	98.91 44	92.96 129	96.49 135	98.67 159	97.40 10	99.07 144	91.87 197	94.38 187	99.41 141

PatchMatch-RL		96.04 141	95.40 143	97.95 139	99.59 72	95.22 168	99.52 172	99.07 35	93.96 103	96.49 135	98.35 177	82.28 213	99.82 91	90.15 221	99.22 98	98.81 194

MP-MVS-pluss		98.07 56	97.64 59	99.38 35	99.74 59	98.41 53	99.74 124	98.18 161	93.35 120	96.45 137	99.85 21	92.64 111	99.97 41	98.91 46	99.89 54	99.77 87
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ADS-MVSNet2		93.80 186	93.88 172	93.55 268	97.87 166	85.94 312	94.24 335	96.84 292	90.07 222	96.43 138	94.48 296	90.29 142	95.37 313	87.44 250	97.23 139	99.36 151

ADS-MVSNet		94.79 164	94.02 169	97.11 167	97.87 166	93.79 192	94.24 335	98.16 165	90.07 222	96.43 138	94.48 296	90.29 142	98.19 209	87.44 250	97.23 139	99.36 151

ACMMP		97.74 69	97.44 65	98.66 92	99.92 27	96.13 137	99.18 210	99.45 22	94.84 72	96.41 140	99.71 68	91.40 127	99.99 27	97.99 83	98.03 121	99.87 75
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
PVSNet_Blended_VisFu		97.27 83	96.81 85	98.66 92	98.81 120	96.67 116	99.92 54	98.64 73	94.51 80	96.38 141	98.49 171	89.05 159	99.88 76	97.10 105	98.34 111	99.43 139

thres200		96.96 94	96.21 104	99.22 41	98.97 101	98.84 22	99.85 91	99.71 5	93.17 124	96.26 142	98.88 134	89.87 145	99.51 123	94.26 154	94.91 180	99.31 156

HyFIR lowres test		96.66 113	96.43 99	97.36 161	99.05 94	93.91 190	99.70 137	99.80 3	90.54 215	96.26 142	98.08 182	92.15 118	98.23 207	96.84 113	95.46 173	99.93 65

Patchmatch-test1		94.39 177	93.46 181	97.17 164	97.10 196	94.44 180	98.86 247	98.32 145	93.30 122	96.17 144	95.38 256	76.48 283	97.34 238	88.12 244	97.43 130	99.74 90

tfpn200view9		96.79 102	95.99 110	99.19 44	98.94 103	98.82 23	99.78 109	99.71 5	92.86 130	96.02 145	98.87 136	89.33 148	99.50 125	93.84 162	94.57 181	99.27 161

thres400		96.78 103	95.99 110	99.16 49	98.94 103	98.82 23	99.78 109	99.71 5	92.86 130	96.02 145	98.87 136	89.33 148	99.50 125	93.84 162	94.57 181	99.16 172

dp		95.05 160	94.43 163	96.91 169	97.99 157	92.73 225	96.29 325	97.98 180	89.70 228	95.93 147	94.67 292	93.83 88	98.45 185	86.91 264	96.53 152	99.54 127

tfpn1000		96.90 98	96.29 102	98.74 88	99.00 98	98.09 62	99.92 54	98.91 44	92.08 171	95.85 148	98.65 161	97.39 11	98.83 153	90.56 213	94.23 195	99.31 156

tfpn111		96.69 109	95.87 128	99.16 49	98.90 110	98.77 26	99.74 124	99.71 5	92.59 150	95.84 149	98.86 138	89.25 150	99.50 125	93.44 174	94.50 185	99.20 166

conf200view11		96.73 108	95.92 118	99.16 49	98.90 110	98.77 26	99.74 124	99.71 5	92.59 150	95.84 149	98.86 138	89.25 150	99.50 125	93.84 162	94.57 181	99.20 166

thres100view900		96.74 106	95.92 118	99.18 45	98.90 110	98.77 26	99.74 124	99.71 5	92.59 150	95.84 149	98.86 138	89.25 150	99.50 125	93.84 162	94.57 181	99.27 161

thres600view7		96.69 109	95.87 128	99.14 55	98.90 110	98.78 25	99.74 124	99.71 5	92.59 150	95.84 149	98.86 138	89.25 150	99.50 125	93.44 174	94.50 185	99.16 172

EPP-MVSNet		96.69 109	96.60 93	96.96 168	97.74 177	93.05 218	99.37 190	98.56 87	88.75 243	95.83 153	99.01 119	96.01 26	98.56 176	96.92 111	97.20 141	99.25 163

TESTMET0.1,1		96.74 106	96.26 103	98.16 129	97.36 193	96.48 122	99.96 20	98.29 148	91.93 176	95.77 154	98.07 183	95.54 38	98.29 202	90.55 214	98.89 101	99.70 95

F-COLMAP		96.93 96	96.95 82	96.87 171	99.71 66	91.74 250	99.85 91	97.95 183	93.11 126	95.72 155	99.16 111	92.35 113	99.94 59	95.32 130	99.35 94	98.92 188

test-LLR		96.47 123	96.04 108	97.78 145	97.02 200	95.44 158	99.96 20	98.21 157	94.07 95	95.55 156	96.38 229	93.90 85	98.27 205	90.42 216	98.83 103	99.64 106

test-mter		96.39 129	95.93 117	97.78 145	97.02 200	95.44 158	99.96 20	98.21 157	91.81 181	95.55 156	96.38 229	95.17 45	98.27 205	90.42 216	98.83 103	99.64 106

IS-MVSNet		96.29 135	95.90 120	97.45 156	98.13 152	94.80 174	99.08 218	97.61 213	92.02 175	95.54 158	98.96 127	90.64 139	98.08 212	93.73 169	97.41 133	99.47 135

CHOSEN 1792x2688		96.81 101	96.53 97	97.64 150	98.91 109	93.07 216	99.65 151	99.80 3	95.64 57	95.39 159	98.86 138	84.35 202	99.90 66	96.98 108	99.16 99	99.95 62

CDS-MVSNet		96.34 131	96.07 107	97.13 165	97.37 192	94.96 170	99.53 171	97.91 188	91.55 187	95.37 160	98.32 178	95.05 50	97.13 260	93.80 166	95.75 169	99.30 158
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Effi-MVS+-dtu		94.53 174	95.30 147	92.22 297	97.77 174	82.54 328	99.59 161	97.06 260	94.92 69	95.29 161	95.37 258	85.81 189	97.89 222	94.80 139	97.07 144	96.23 218

CSCG		97.10 89	97.04 79	97.27 163	99.89 37	91.92 244	99.90 62	99.07 35	88.67 244	95.26 162	99.82 40	93.17 101	99.98 32	98.15 75	99.47 89	99.90 71

view600		96.46 124	95.59 135	99.06 65	98.87 115	98.60 42	99.69 138	99.71 5	92.20 164	95.23 163	98.80 150	89.17 154	99.43 133	92.29 188	94.37 188	99.16 172

view800		96.46 124	95.59 135	99.06 65	98.87 115	98.60 42	99.69 138	99.71 5	92.20 164	95.23 163	98.80 150	89.17 154	99.43 133	92.29 188	94.37 188	99.16 172

conf0.05thres1000		96.46 124	95.59 135	99.06 65	98.87 115	98.60 42	99.69 138	99.71 5	92.20 164	95.23 163	98.80 150	89.17 154	99.43 133	92.29 188	94.37 188	99.16 172

tfpn		96.46 124	95.59 135	99.06 65	98.87 115	98.60 42	99.69 138	99.71 5	92.20 164	95.23 163	98.80 150	89.17 154	99.43 133	92.29 188	94.37 188	99.16 172

conf0.01		96.52 121	95.88 121	98.41 120	98.59 126	97.38 92	99.87 72	98.91 44	91.32 196	95.22 167	98.83 144	96.57 17	98.66 169	89.55 226	94.09 197	99.20 166

conf0.002		96.52 121	95.88 121	98.41 120	98.59 126	97.38 92	99.87 72	98.91 44	91.32 196	95.22 167	98.83 144	96.57 17	98.66 169	89.55 226	94.09 197	99.20 166

thresconf0.02		96.53 116	95.88 121	98.48 108	98.59 126	97.38 92	99.87 72	98.91 44	91.32 196	95.22 167	98.83 144	96.57 17	98.66 169	89.55 226	94.09 197	99.40 144

tfpn_n400		96.53 116	95.88 121	98.48 108	98.59 126	97.38 92	99.87 72	98.91 44	91.32 196	95.22 167	98.83 144	96.57 17	98.66 169	89.55 226	94.09 197	99.40 144

tfpnconf		96.53 116	95.88 121	98.48 108	98.59 126	97.38 92	99.87 72	98.91 44	91.32 196	95.22 167	98.83 144	96.57 17	98.66 169	89.55 226	94.09 197	99.40 144

tfpnview11		96.53 116	95.88 121	98.48 108	98.59 126	97.38 92	99.87 72	98.91 44	91.32 196	95.22 167	98.83 144	96.57 17	98.66 169	89.55 226	94.09 197	99.40 144

Vis-MVSNet (Re-imp)		96.32 132	95.98 112	97.35 162	97.93 162	94.82 173	99.47 179	98.15 167	91.83 179	95.09 173	99.11 112	91.37 128	97.47 232	93.47 173	97.43 130	99.74 90

TAMVS		95.85 144	95.58 139	96.65 179	97.07 197	93.50 199	99.17 211	97.82 198	91.39 195	95.02 174	98.01 184	92.20 116	97.30 244	93.75 168	95.83 166	99.14 179

XVG-OURS-SEG-HR		94.79 164	94.70 159	95.08 211	98.05 155	89.19 289	99.08 218	97.54 218	93.66 114	94.87 175	99.58 84	78.78 267	99.79 94	97.31 99	93.40 209	96.25 216

XVG-OURS		94.82 163	94.74 158	95.06 212	98.00 156	89.19 289	99.08 218	97.55 216	94.10 93	94.71 176	99.62 82	80.51 249	99.74 105	96.04 121	93.06 214	96.25 216

ab-mvs		94.69 168	93.42 183	98.51 106	98.07 153	96.26 129	96.49 321	98.68 66	90.31 219	94.54 177	97.00 210	76.30 284	99.71 109	95.98 122	93.38 210	99.56 122

TAPA-MVS	92.12 8	94.42 176	93.60 176	96.90 170	99.33 87	91.78 248	99.78 109	98.00 178	89.89 226	94.52 178	99.47 92	91.97 121	99.18 141	69.90 336	99.52 86	99.73 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		94.54 172	93.56 179	97.49 154	97.96 159	94.34 182	98.71 255	97.51 224	90.30 220	94.51 179	98.69 158	75.56 289	98.77 159	92.82 185	95.99 161	99.35 153

Fast-Effi-MVS+		95.02 161	94.19 166	97.52 153	97.88 165	94.55 179	99.97 12	97.08 259	88.85 242	94.47 180	97.96 186	84.59 199	98.41 188	89.84 223	97.10 143	99.59 114

DeepC-MVS	94.51 4	96.92 97	96.40 100	98.45 114	99.16 90	95.90 144	99.66 148	98.06 174	96.37 38	94.37 181	99.49 91	83.29 209	99.90 66	97.63 94	99.61 81	99.55 123
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPSCF		91.80 222	92.79 193	88.83 323	98.15 151	69.87 346	98.11 296	96.60 302	83.93 304	94.33 182	99.27 103	79.60 257	99.46 132	91.99 194	93.16 213	97.18 211

BH-RMVSNet		95.18 156	94.31 165	97.80 144	98.17 150	95.23 167	99.76 120	97.53 220	92.52 156	94.27 183	99.25 106	76.84 279	98.80 156	90.89 210	99.54 85	99.35 153

CVMVSNet		94.68 169	94.94 155	93.89 261	96.80 210	86.92 309	99.06 224	98.98 38	94.45 81	94.23 184	99.02 117	85.60 191	95.31 314	90.91 209	95.39 175	99.43 139

Anonymous202405211		93.10 198	91.99 206	96.40 185	99.10 92	89.65 287	98.88 241	97.93 185	83.71 305	94.00 185	98.75 156	68.79 319	99.88 76	95.08 133	91.71 216	99.68 98

cascas		94.64 170	93.61 174	97.74 147	97.82 171	96.26 129	99.96 20	97.78 200	85.76 286	94.00 185	97.54 191	76.95 278	99.21 140	97.23 101	95.43 174	97.76 209

Anonymous20240529		92.10 216	90.65 222	96.47 181	98.82 119	90.61 269	98.72 254	98.67 69	75.54 340	93.90 187	98.58 167	66.23 329	99.90 66	94.70 144	90.67 217	98.90 191

LS3D		95.84 145	95.11 153	98.02 138	99.85 43	95.10 169	98.74 252	98.50 107	87.22 269	93.66 188	99.86 17	87.45 175	99.95 51	90.94 208	99.81 67	99.02 186

HQP-NCC					95.78 230		99.87 72		96.82 22	93.37 189

ACMP_Plane					95.78 230		99.87 72		96.82 22	93.37 189

HQP4-MVS										93.37 189			98.39 192			94.53 222

HQP-MVS		94.61 171	94.50 162	94.92 221	95.78 230	91.85 245	99.87 72	97.89 190	96.82 22	93.37 189	98.65 161	80.65 247	98.39 192	97.92 87	89.60 218	94.53 222

HQP_MVS		94.49 175	94.36 164	94.87 224	95.71 239	91.74 250	99.84 95	97.87 192	96.38 35	93.01 193	98.59 165	80.47 251	98.37 197	97.79 90	89.55 221	94.52 224

plane_prior3						91.64 256			96.63 30	93.01 193

GA-MVS		93.83 183	92.84 191	96.80 172	95.73 236	93.57 198	99.88 69	97.24 250	92.57 154	92.92 195	96.66 222	78.73 268	97.67 227	87.75 247	94.06 204	99.17 171

tpm cat1		93.51 192	92.52 198	96.47 181	97.77 174	91.47 260	96.13 326	98.06 174	80.98 325	92.91 196	93.78 308	89.66 146	98.87 152	87.03 260	96.39 154	99.09 184

1112_ss		96.01 142	95.20 150	98.42 117	97.80 172	96.41 124	99.65 151	96.66 299	92.71 140	92.88 197	99.40 96	92.16 117	99.30 138	91.92 195	93.66 206	99.55 123

Test_1112_low_res		95.72 146	94.83 156	98.42 117	97.79 173	96.41 124	99.65 151	96.65 300	92.70 141	92.86 198	96.13 237	92.15 118	99.30 138	91.88 196	93.64 207	99.55 123

IB-MVS	92.85 6	94.99 162	93.94 170	98.16 129	97.72 181	95.69 155	99.99 3	98.81 58	94.28 88	92.70 199	96.90 212	95.08 48	99.17 142	96.07 120	73.88 330	99.60 112
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
Fast-Effi-MVS+-dtu		93.72 189	93.86 173	93.29 271	97.06 198	86.16 310	99.80 105	96.83 293	92.66 144	92.58 200	97.83 188	81.39 234	97.67 227	89.75 224	96.87 149	96.05 220

tpmvs		94.28 179	93.57 178	96.40 185	98.55 133	91.50 259	95.70 333	98.55 91	87.47 264	92.15 201	94.26 300	91.42 126	98.95 151	88.15 242	95.85 165	98.76 196

BH-w/o		95.71 148	95.38 145	96.68 177	98.49 137	92.28 235	99.84 95	97.50 225	92.12 169	92.06 202	98.79 155	84.69 198	98.67 167	95.29 131	99.66 76	99.09 184

VPA-MVSNet		92.70 205	91.55 211	96.16 191	95.09 247	96.20 134	98.88 241	99.00 37	91.02 208	91.82 203	95.29 264	76.05 288	97.96 219	95.62 129	81.19 280	94.30 242

OPM-MVS		93.21 196	92.80 192	94.44 240	93.12 296	90.85 266	99.77 114	97.61 213	96.19 42	91.56 204	98.65 161	75.16 294	98.47 181	93.78 167	89.39 224	93.99 267

EI-MVSNet		93.73 188	93.40 186	94.74 228	96.80 210	92.69 226	99.06 224	97.67 206	88.96 238	91.39 205	99.02 117	88.75 163	97.30 244	91.07 204	87.85 243	94.22 247

MVSTER		95.53 151	95.22 149	96.45 183	98.56 132	97.72 73	99.91 59	97.67 206	92.38 161	91.39 205	97.14 201	97.24 13	97.30 244	94.80 139	87.85 243	94.34 240

BH-untuned		95.18 156	94.83 156	96.22 190	98.36 140	91.22 261	99.80 105	97.32 244	90.91 209	91.08 207	98.67 159	83.51 206	98.54 178	94.23 155	99.61 81	98.92 188

CLD-MVS		94.06 181	93.90 171	94.55 237	96.02 225	90.69 267	99.98 6	97.72 203	96.62 31	91.05 208	98.85 143	77.21 275	98.47 181	98.11 77	89.51 223	94.48 226
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS		96.60 114	95.56 140	99.72 4	96.85 207	99.22 8	98.31 285	98.94 40	91.57 186	90.90 209	99.61 83	86.66 183	99.96 43	97.36 98	99.88 56	99.99 12

MSDG		94.37 178	93.36 187	97.40 159	98.88 114	93.95 189	99.37 190	97.38 238	85.75 289	90.80 210	99.17 110	84.11 204	99.88 76	86.35 268	98.43 110	98.36 199

VPNet		91.81 220	90.46 226	95.85 199	94.74 254	95.54 157	98.98 232	98.59 82	92.14 168	90.77 211	97.44 193	68.73 321	97.54 230	94.89 138	77.89 311	94.46 227

MIMVSNet		90.30 258	88.67 269	95.17 210	96.45 218	91.64 256	92.39 345	97.15 256	85.99 283	90.50 212	93.19 316	66.95 327	94.86 321	82.01 299	93.43 208	99.01 187

mvs_anonymous		95.65 150	95.03 154	97.53 152	98.19 148	95.74 150	99.33 194	97.49 226	90.87 210	90.47 213	97.10 204	88.23 168	97.16 254	95.92 123	97.66 126	99.68 98

Patchmatch-test		92.65 208	91.50 212	96.10 193	96.85 207	90.49 272	91.50 349	97.19 252	82.76 310	90.23 214	95.59 248	95.02 51	98.00 216	77.41 324	96.98 146	99.82 79

v1neww		91.44 228	90.28 232	94.91 222	93.50 274	93.43 202	99.73 130	97.06 260	87.55 258	90.08 215	95.11 269	81.98 219	97.32 241	87.41 252	80.15 293	93.99 267

v7new		91.44 228	90.28 232	94.91 222	93.50 274	93.43 202	99.73 130	97.06 260	87.55 258	90.08 215	95.11 269	81.98 219	97.32 241	87.41 252	80.15 293	93.99 267

v6		91.44 228	90.27 234	94.93 220	93.44 278	93.44 201	99.73 130	97.05 264	87.57 257	90.05 217	95.10 271	81.87 224	97.39 234	87.45 249	80.17 292	93.98 271

LPG-MVS_test		92.96 200	92.71 194	93.71 264	95.43 244	88.67 294	99.75 121	97.62 210	92.81 134	90.05 217	98.49 171	75.24 292	98.40 190	95.84 126	89.12 225	94.07 256

LGP-MVS_train				93.71 264	95.43 244	88.67 294		97.62 210	92.81 134	90.05 217	98.49 171	75.24 292	98.40 190	95.84 126	89.12 225	94.07 256

DP-MVS		94.54 172	93.42 183	97.91 142	99.46 83	94.04 187	98.93 238	97.48 227	81.15 324	90.04 220	99.55 86	87.02 180	99.95 51	88.97 236	98.11 117	99.73 92

test_djsdf		92.83 203	92.29 201	94.47 239	91.90 317	92.46 232	99.55 168	97.27 248	91.17 202	89.96 221	96.07 239	81.10 238	96.89 276	94.67 145	88.91 227	94.05 258

ACMM	91.95 10	92.88 202	92.52 198	93.98 258	95.75 235	89.08 291	99.77 114	97.52 222	93.00 127	89.95 222	97.99 185	76.17 286	98.46 184	93.63 172	88.87 229	94.39 234
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
1314		96.84 100	95.96 115	99.48 25	96.74 214	98.52 48	98.31 285	98.86 55	95.82 48	89.91 223	98.98 124	87.49 173	99.96 43	97.80 89	99.73 71	99.96 57

XVG-ACMP-BASELINE		91.22 238	90.75 220	92.63 284	93.73 269	85.61 314	98.52 272	97.44 229	92.77 138	89.90 224	96.85 217	66.64 328	98.39 192	92.29 188	88.61 234	93.89 279

v7		91.20 239	89.99 244	94.82 227	93.57 271	93.41 206	99.57 164	96.98 276	86.83 274	89.88 225	95.22 266	81.01 240	97.14 258	85.53 274	81.31 279	93.90 277

nrg030		93.51 192	92.53 197	96.45 183	94.36 258	97.20 101	99.81 102	97.16 255	91.60 185	89.86 226	97.46 192	86.37 186	97.68 226	95.88 124	80.31 290	94.46 227

V42		91.28 236	90.12 242	94.74 228	93.42 280	93.46 200	99.68 143	97.02 270	87.36 266	89.85 227	95.05 273	81.31 236	97.34 238	87.34 255	80.07 295	93.40 297

v144192		90.79 246	89.52 252	94.59 234	93.11 297	92.77 223	99.56 166	96.99 274	86.38 279	89.82 228	94.95 281	80.50 250	97.10 263	83.98 286	80.41 288	93.90 277

v1		91.36 232	90.14 240	95.04 213	93.35 284	93.80 191	99.77 114	97.05 264	87.53 261	89.77 229	94.91 282	81.99 218	97.33 240	86.90 266	79.98 298	94.00 264

v1141		91.36 232	90.14 240	95.00 215	93.33 286	93.79 192	99.78 109	97.05 264	87.52 262	89.75 230	94.89 284	82.13 215	97.21 250	86.84 267	80.00 297	94.00 264

divwei89l23v2f112		91.37 231	90.15 239	95.00 215	93.35 284	93.78 195	99.78 109	97.05 264	87.54 260	89.73 231	94.89 284	82.24 214	97.21 250	86.91 264	79.90 299	94.00 264

GBi-Net		90.88 244	89.82 246	94.08 251	97.53 187	91.97 240	98.43 277	96.95 281	87.05 270	89.68 232	94.72 288	71.34 309	96.11 299	87.01 261	85.65 256	94.17 249

test1		90.88 244	89.82 246	94.08 251	97.53 187	91.97 240	98.43 277	96.95 281	87.05 270	89.68 232	94.72 288	71.34 309	96.11 299	87.01 261	85.65 256	94.17 249

FMVSNet3		92.69 206	91.58 210	95.99 194	98.29 141	97.42 90	99.26 205	97.62 210	89.80 227	89.68 232	95.32 260	81.62 230	96.27 295	87.01 261	85.65 256	94.29 243

IterMVS-LS		92.69 206	92.11 203	94.43 242	96.80 210	92.74 224	99.45 182	96.89 288	88.98 236	89.65 235	95.38 256	88.77 162	96.34 293	90.98 207	82.04 275	94.22 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1144		91.09 240	89.83 245	94.87 224	93.25 291	93.69 197	99.62 159	96.98 276	86.83 274	89.64 236	94.99 278	80.94 241	97.05 266	85.08 279	81.16 281	93.87 281

testpf		89.10 278	88.73 268	90.24 314	97.59 186	83.48 325	74.22 362	97.39 237	79.66 329	89.64 236	93.92 304	86.38 185	95.76 309	85.42 275	94.31 193	91.49 324

v1921920		90.46 253	89.12 259	94.50 238	92.96 301	92.46 232	99.49 175	96.98 276	86.10 282	89.61 238	95.30 261	78.55 270	97.03 270	82.17 298	80.89 287	94.01 261

v1192		90.62 251	89.25 257	94.72 230	93.13 294	93.07 216	99.50 174	97.02 270	86.33 280	89.56 239	95.01 275	79.22 261	97.09 265	82.34 297	81.16 281	94.01 261

PCF-MVS	94.20 5	95.18 156	94.10 168	98.43 116	98.55 133	95.99 143	97.91 302	97.31 245	90.35 218	89.48 240	99.22 108	85.19 197	99.89 70	90.40 218	98.47 109	99.41 141
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
3Dnovator	91.47 12	96.28 136	95.34 146	99.08 63	96.82 209	97.47 87	99.45 182	98.81 58	95.52 59	89.39 241	99.00 121	81.97 221	99.95 51	97.27 100	99.83 61	99.84 77

v1240		90.20 261	88.79 266	94.44 240	93.05 299	92.27 236	99.38 189	96.92 285	85.89 284	89.36 242	94.87 287	77.89 274	97.03 270	80.66 305	81.08 283	94.01 261

FIs		94.10 180	93.43 182	96.11 192	94.70 255	96.82 113	99.58 162	98.93 43	92.54 155	89.34 243	97.31 197	87.62 172	97.10 263	94.22 156	86.58 252	94.40 233

ITE_SJBPF				92.38 293	95.69 241	85.14 318		95.71 317	92.81 134	89.33 244	98.11 181	70.23 316	98.42 187	85.91 272	88.16 241	93.59 294

v2v482		91.30 234	90.07 243	95.01 214	93.13 294	93.79 192	99.77 114	97.02 270	88.05 253	89.25 245	95.37 258	80.73 245	97.15 256	87.28 256	80.04 296	94.09 255

UniMVSNet (Re)		93.07 199	92.13 202	95.88 197	94.84 252	96.24 133	99.88 69	98.98 38	92.49 158	89.25 245	95.40 253	87.09 179	97.14 258	93.13 182	78.16 309	94.26 244

UniMVSNet_NR-MVSNet		92.95 201	92.11 203	95.49 202	94.61 256	95.28 164	99.83 99	99.08 34	91.49 188	89.21 247	96.86 216	87.14 178	96.73 283	93.20 178	77.52 315	94.46 227

DU-MVS		92.46 211	91.45 214	95.49 202	94.05 263	95.28 164	99.81 102	98.74 62	92.25 163	89.21 247	96.64 224	81.66 228	96.73 283	93.20 178	77.52 315	94.46 227

Baseline_NR-MVSNet		90.33 257	89.51 253	92.81 281	92.84 302	89.95 283	99.77 114	93.94 347	84.69 299	89.04 249	95.66 246	81.66 228	96.52 288	90.99 206	76.98 320	91.97 319

FC-MVSNet-test		93.81 185	93.15 189	95.80 200	94.30 260	96.20 134	99.42 184	98.89 53	92.33 162	89.03 250	97.27 199	87.39 176	96.83 280	93.20 178	86.48 253	94.36 236

QAPM		95.40 154	94.17 167	99.10 61	96.92 203	97.71 74	99.40 185	98.68 66	89.31 230	88.94 251	98.89 132	82.48 212	99.96 43	93.12 183	99.83 61	99.62 108

AllTest		92.48 209	91.64 209	95.00 215	99.01 96	88.43 298	98.94 237	96.82 295	86.50 277	88.71 252	98.47 175	74.73 296	99.88 76	85.39 276	96.18 156	96.71 213

TestCases				95.00 215	99.01 96	88.43 298		96.82 295	86.50 277	88.71 252	98.47 175	74.73 296	99.88 76	85.39 276	96.18 156	96.71 213

pmmvs4		92.10 216	91.07 218	95.18 209	92.82 303	94.96 170	99.48 177	96.83 293	87.45 265	88.66 254	96.56 227	83.78 205	96.83 280	89.29 233	84.77 264	93.75 287

PS-MVSNAJss		93.64 191	93.31 188	94.61 233	92.11 313	92.19 237	99.12 214	97.38 238	92.51 157	88.45 255	96.99 211	91.20 130	97.29 247	94.36 150	87.71 245	94.36 236

TranMVSNet+NR-MVSNet		91.68 226	90.61 223	94.87 224	93.69 270	93.98 188	99.69 138	98.65 70	91.03 207	88.44 256	96.83 220	80.05 255	96.18 298	90.26 220	76.89 322	94.45 232

FMVSNet2		91.02 241	89.56 250	95.41 205	97.53 187	95.74 150	98.98 232	97.41 234	87.05 270	88.43 257	95.00 277	71.34 309	96.24 297	85.12 278	85.21 261	94.25 246

COLMAP_ROB	90.47 14	92.18 215	91.49 213	94.25 246	99.00 98	88.04 303	98.42 280	96.70 298	82.30 314	88.43 257	99.01 119	76.97 277	99.85 81	86.11 271	96.50 153	94.86 221
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
3Dnovator+	91.53 11	96.31 133	95.24 148	99.52 20	96.88 206	98.64 40	99.72 135	98.24 154	95.27 65	88.42 259	98.98 124	82.76 211	99.94 59	97.10 105	99.83 61	99.96 57

v148		90.70 247	89.63 248	93.92 259	92.97 300	90.97 263	99.75 121	96.89 288	87.51 263	88.27 260	95.01 275	81.67 227	97.04 267	87.40 254	77.17 319	93.75 287

DSMNet-mixed		88.28 285	88.24 276	88.42 326	89.64 336	75.38 344	98.06 298	89.86 361	85.59 291	88.20 261	92.14 321	76.15 287	91.95 342	78.46 319	96.05 159	97.92 205

WR-MVS		92.31 213	91.25 215	95.48 204	94.45 257	95.29 163	99.60 160	98.68 66	90.10 221	88.07 262	96.89 214	80.68 246	96.80 282	93.14 181	79.67 300	94.36 236

test0.0.03 1		93.86 182	93.61 174	94.64 232	95.02 251	92.18 238	99.93 51	98.58 83	94.07 95	87.96 263	98.50 170	93.90 85	94.96 319	81.33 302	93.17 212	96.78 212

XXY-MVS		91.82 219	90.46 226	95.88 197	93.91 266	95.40 161	98.87 245	97.69 205	88.63 246	87.87 264	97.08 205	74.38 299	97.89 222	91.66 199	84.07 267	94.35 239

Patchmtry		89.70 268	88.49 270	93.33 270	96.24 221	89.94 285	91.37 350	96.23 308	78.22 332	87.69 265	93.31 314	91.04 134	96.03 304	80.18 308	82.10 274	94.02 259

v8		90.54 252	89.17 258	94.66 231	93.43 279	93.40 209	99.20 208	96.94 284	85.76 286	87.56 266	94.51 294	81.96 222	97.19 252	84.94 280	78.25 308	93.38 299

anonymousdsp		91.79 224	90.92 219	94.41 243	90.76 330	92.93 221	98.93 238	97.17 254	89.08 232	87.46 267	95.30 261	78.43 272	96.92 275	92.38 187	88.73 232	93.39 298

jajsoiax		91.92 218	91.18 216	94.15 248	91.35 325	90.95 264	99.00 231	97.42 232	92.61 148	87.38 268	97.08 205	72.46 305	97.36 236	94.53 148	88.77 231	94.13 253

mvs_tets		91.81 220	91.08 217	94.00 256	91.63 323	90.58 270	98.67 260	97.43 230	92.43 159	87.37 269	97.05 208	71.76 307	97.32 241	94.75 142	88.68 233	94.11 254

v10		90.25 260	88.82 265	94.57 236	93.53 273	93.43 202	99.08 218	96.87 291	85.00 295	87.34 270	94.51 294	80.93 242	97.02 272	82.85 294	79.23 301	93.26 302

pmmvs5		90.17 263	89.09 260	93.40 269	92.10 314	89.77 286	99.74 124	95.58 321	85.88 285	87.24 271	95.74 243	73.41 303	96.48 289	88.54 238	83.56 270	93.95 273

ACMP	92.05 9	92.74 204	92.42 200	93.73 262	95.91 229	88.72 293	99.81 102	97.53 220	94.13 91	87.00 272	98.23 179	74.07 300	98.47 181	96.22 119	88.86 230	93.99 267
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v52		89.55 271	88.41 272	92.98 277	92.32 310	90.01 281	98.88 241	96.89 288	84.51 300	86.89 273	94.22 301	79.23 260	97.16 254	84.46 282	78.27 307	91.76 321

V4		89.55 271	88.41 272	92.98 277	92.21 312	90.03 280	98.87 245	96.91 286	84.51 300	86.84 274	94.21 302	79.37 259	97.15 256	84.45 283	78.28 306	91.76 321

MVS-HIRNet		86.22 297	83.19 312	95.31 206	96.71 216	90.29 276	92.12 346	97.33 242	62.85 354	86.82 275	70.37 357	69.37 318	97.49 231	75.12 331	97.99 122	98.15 202

Anonymous20231211		89.86 266	88.44 271	94.13 250	98.93 105	90.68 268	98.54 269	98.26 153	76.28 336	86.73 276	95.54 249	70.60 313	97.56 229	90.82 211	80.27 291	94.15 252

v7n		89.65 269	88.29 275	93.72 263	92.22 311	90.56 271	99.07 222	97.10 258	85.42 294	86.73 276	94.72 288	80.06 254	97.13 260	81.14 303	78.12 310	93.49 295

semantic-postprocess				92.93 279	96.72 215	89.96 282		96.99 274	88.95 239	86.63 278	95.67 245	76.50 282	95.00 318	87.04 259	84.04 269	93.84 283

EU-MVSNet		90.14 264	90.34 230	89.54 320	92.55 307	81.06 337	98.69 257	98.04 176	91.41 194	86.59 279	96.84 219	80.83 243	93.31 341	86.20 269	81.91 276	94.26 244

OpenMVS	90.15 15	94.77 166	93.59 177	98.33 123	96.07 223	97.48 86	99.56 166	98.57 85	90.46 216	86.51 280	98.95 129	78.57 269	99.94 59	93.86 161	99.74 70	97.57 210

IterMVS		90.91 243	90.17 238	93.12 274	96.78 213	90.42 275	98.89 240	97.05 264	89.03 234	86.49 281	95.42 252	76.59 281	95.02 317	87.22 257	84.09 266	93.93 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WR-MVS_H		91.30 234	90.35 229	94.15 248	94.17 262	92.62 230	99.17 211	98.94 40	88.87 241	86.48 282	94.46 298	84.36 201	96.61 286	88.19 241	78.51 305	93.21 304

MS-PatchMatch		90.65 248	90.30 231	91.71 303	94.22 261	85.50 316	98.24 290	97.70 204	88.67 244	86.42 283	96.37 231	67.82 325	98.03 215	83.62 289	99.62 78	91.60 323

CP-MVSNet		91.23 237	90.22 236	94.26 245	93.96 265	92.39 234	99.09 216	98.57 85	88.95 239	86.42 283	96.57 226	79.19 262	96.37 291	90.29 219	78.95 302	94.02 259

LF4IMVS		89.25 277	88.85 264	90.45 313	92.81 304	81.19 336	98.12 295	94.79 340	91.44 191	86.29 285	97.11 203	65.30 333	98.11 211	88.53 239	85.25 260	92.07 316

PVSNet_0	88.03 19	91.80 222	90.27 234	96.38 187	98.27 143	90.46 273	99.94 46	99.61 17	93.99 101	86.26 286	97.39 196	71.13 312	99.89 70	98.77 54	67.05 340	98.79 195

PS-CasMVS		90.63 250	89.51 253	93.99 257	93.83 267	91.70 254	98.98 232	98.52 97	88.48 247	86.15 287	96.53 228	75.46 290	96.31 294	88.83 237	78.86 304	93.95 273

FMVSNet1		88.50 283	86.64 287	94.08 251	95.62 243	91.97 240	98.43 277	96.95 281	83.00 308	86.08 288	94.72 288	59.09 346	96.11 299	81.82 301	84.07 267	94.17 249

PEN-MVS		90.19 262	89.06 261	93.57 267	93.06 298	90.90 265	99.06 224	98.47 109	88.11 252	85.91 289	96.30 232	76.67 280	95.94 308	87.07 258	76.91 321	93.89 279

ppachtmachnet_test		89.58 270	88.35 274	93.25 272	92.40 308	90.44 274	99.33 194	96.73 297	85.49 292	85.90 290	95.77 242	81.09 239	96.00 307	76.00 330	82.49 273	93.30 300

v748		88.94 280	87.72 281	92.61 285	91.91 316	87.50 306	99.07 222	96.97 279	84.76 297	85.79 291	93.63 311	79.19 262	97.04 267	83.16 292	75.03 329	93.28 301

OurMVSNet-221017-0		89.81 267	89.48 255	90.83 309	91.64 322	81.21 335	98.17 294	95.38 332	91.48 189	85.65 292	97.31 197	72.66 304	97.29 247	88.15 242	84.83 263	93.97 272

our_test_3		90.39 254	89.48 255	93.12 274	92.40 308	89.57 288	99.33 194	96.35 307	87.84 255	85.30 293	94.99 278	84.14 203	96.09 302	80.38 306	84.56 265	93.71 292

testgi		89.01 279	88.04 278	91.90 301	93.49 276	84.89 320	99.73 130	95.66 319	93.89 108	85.14 294	98.17 180	59.68 345	94.66 323	77.73 322	88.88 228	96.16 219

DTE-MVSNet		89.40 273	88.24 276	92.88 280	92.66 306	89.95 283	99.10 215	98.22 156	87.29 267	85.12 295	96.22 234	76.27 285	95.30 315	83.56 290	75.74 325	93.41 296

LP		86.76 290	84.85 294	92.50 288	95.08 248	85.89 313	89.97 352	96.97 279	75.28 342	84.97 296	90.68 326	80.78 244	95.13 316	61.64 348	88.31 239	96.46 215

FMVSNet5		88.32 284	87.47 284	90.88 307	96.90 205	88.39 300	97.28 311	95.68 318	82.60 311	84.67 297	92.40 320	79.83 256	91.16 343	76.39 329	81.51 278	93.09 305

tfpnnormal		89.29 276	87.61 282	94.34 244	94.35 259	94.13 186	98.95 236	98.94 40	83.94 303	84.47 298	95.51 250	74.84 295	97.39 234	77.05 327	80.41 288	91.48 325

MVP-Stereo		90.93 242	90.45 228	92.37 294	91.25 327	88.76 292	98.05 299	96.17 310	87.27 268	84.04 299	95.30 261	78.46 271	97.27 249	83.78 288	99.70 74	91.09 326
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LTVRE_ROB	88.28 18	90.29 259	89.05 262	94.02 254	95.08 248	90.15 279	97.19 312	97.43 230	84.91 296	83.99 300	97.06 207	74.00 301	98.28 204	84.08 284	87.71 245	93.62 293
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
pm-mvs1		89.36 275	87.81 280	94.01 255	93.40 282	91.93 243	98.62 264	96.48 306	86.25 281	83.86 301	96.14 236	73.68 302	97.04 267	86.16 270	75.73 326	93.04 307

USDC		90.00 265	88.96 263	93.10 276	94.81 253	88.16 302	98.71 255	95.54 323	93.66 114	83.75 302	97.20 200	65.58 331	98.31 201	83.96 287	87.49 249	92.85 311

test2356		86.43 294	87.59 283	82.95 333	85.90 343	69.43 347	99.79 108	96.63 301	85.76 286	83.44 303	94.99 278	80.45 253	86.52 354	68.12 341	93.21 211	92.90 308

ACMH+	89.98 16	90.35 256	89.54 251	92.78 282	95.99 226	86.12 311	98.81 249	97.18 253	89.38 229	83.14 304	97.76 189	68.42 323	98.43 186	89.11 235	86.05 255	93.78 286

Anonymous20231206		86.32 295	85.42 291	89.02 322	89.11 338	80.53 340	99.05 227	95.28 334	85.43 293	82.82 305	93.92 304	74.40 298	93.44 340	66.99 342	81.83 277	93.08 306

testus		83.91 316	84.49 296	82.17 335	85.68 344	66.11 351	99.68 143	93.53 351	86.55 276	82.60 306	94.91 282	56.70 349	88.19 350	68.46 338	92.31 215	92.21 315

SixPastTwentyTwo		88.73 282	88.01 279	90.88 307	91.85 319	82.24 330	98.22 292	95.18 338	88.97 237	82.26 307	96.89 214	71.75 308	96.67 285	84.00 285	82.98 271	93.72 291

TinyColmap		87.87 287	86.51 288	91.94 300	95.05 250	85.57 315	97.65 304	94.08 345	84.40 302	81.82 308	96.85 217	62.14 340	98.33 199	80.25 307	86.37 254	91.91 320

ACMH	89.72 17	90.64 249	89.63 248	93.66 266	95.64 242	88.64 296	98.55 267	97.45 228	89.03 234	81.62 309	97.61 190	69.75 317	98.41 188	89.37 232	87.62 247	93.92 276
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		85.69 303	83.84 308	91.26 306	90.00 335	84.41 322	97.82 303	96.15 311	75.86 338	81.29 310	95.39 255	61.21 342	96.87 278	83.52 291	73.29 332	92.50 313

TransMVSNet (Re)		87.25 288	85.28 292	93.16 273	93.56 272	91.03 262	98.54 269	94.05 346	83.69 306	81.09 311	96.16 235	75.32 291	96.40 290	76.69 328	68.41 337	92.06 317

NR-MVSNet		91.56 227	90.22 236	95.60 201	94.05 263	95.76 149	98.25 289	98.70 64	91.16 204	80.78 312	96.64 224	83.23 210	96.57 287	91.41 200	77.73 313	94.46 227

LCM-MVSNet-Re		92.31 213	92.60 196	91.43 304	97.53 187	79.27 342	99.02 230	91.83 356	92.07 172	80.31 313	94.38 299	83.50 207	95.48 311	97.22 102	97.58 128	99.54 127

TDRefinement		84.76 310	82.56 314	91.38 305	74.58 356	84.80 321	97.36 308	94.56 343	84.73 298	80.21 314	96.12 238	63.56 337	98.39 192	87.92 245	63.97 350	90.95 329

N_pmnet		80.06 322	80.78 319	77.89 338	91.94 315	45.28 367	98.80 250	56.82 372	78.10 333	80.08 315	93.33 312	77.03 276	95.76 309	68.14 340	82.81 272	92.64 312

test_0402		85.58 304	83.94 307	90.50 311	93.81 268	85.04 319	98.55 267	95.20 337	76.01 337	79.72 316	95.13 267	64.15 336	96.26 296	66.04 345	86.88 251	90.21 336

test20.03		84.72 312	83.99 303	86.91 328	88.19 340	80.62 339	98.88 241	95.94 314	88.36 249	78.87 317	94.62 293	68.75 320	89.11 347	66.52 343	75.82 324	91.00 327

pmmvs3		80.27 321	77.77 325	87.76 327	80.32 351	82.43 329	98.23 291	91.97 355	72.74 347	78.75 318	87.97 332	57.30 348	90.99 344	70.31 335	62.37 352	89.87 343

MIMVSNet1		82.58 318	80.51 320	88.78 324	86.68 342	84.20 323	96.65 319	95.41 331	78.75 331	78.59 319	92.44 319	51.88 353	89.76 346	65.26 346	78.95 302	92.38 314

DeepMVS_CX				82.92 334	95.98 228	58.66 358		96.01 313	92.72 139	78.34 320	95.51 250	58.29 347	98.08 212	82.57 295	85.29 259	92.03 318

Patchmatch-RL test		86.90 289	85.98 289	89.67 319	84.45 346	75.59 343	89.71 353	92.43 353	86.89 273	77.83 321	90.94 325	94.22 73	93.63 338	87.75 247	69.61 334	99.79 83

lessismore_v0				90.53 310	90.58 331	80.90 338		95.80 316		77.01 322	95.84 240	66.15 330	96.95 273	83.03 293	75.05 328	93.74 290

K. test v3		88.05 286	87.24 285	90.47 312	91.82 321	82.23 331	98.96 235	97.42 232	89.05 233	76.93 323	95.60 247	68.49 322	95.42 312	85.87 273	81.01 285	93.75 287

ambc				83.23 332	77.17 355	62.61 353	87.38 356	94.55 344		76.72 324	86.65 347	30.16 360	96.36 292	84.85 281	69.86 333	90.73 330

PM-MVS		80.47 320	78.88 322	85.26 330	83.79 348	72.22 345	95.89 331	91.08 357	85.71 290	76.56 325	88.30 329	36.64 358	93.90 334	82.39 296	69.57 335	89.66 345

DI_MVS_plusplus_test		92.48 209	90.60 224	98.11 135	91.88 318	96.13 137	99.64 155	97.73 201	92.69 142	76.02 326	93.79 307	70.49 314	99.07 144	95.88 124	97.26 137	99.14 179

OpenMVS_ROB	79.82 20	83.77 317	81.68 317	90.03 317	88.30 339	82.82 326	98.46 275	95.22 336	73.92 346	76.00 327	91.29 324	55.00 350	96.94 274	68.40 339	88.51 237	90.34 331

UnsupCasMVSNet_eth		85.52 305	83.99 303	90.10 316	89.36 337	83.51 324	96.65 319	97.99 179	89.14 231	75.89 328	93.83 306	63.25 338	93.92 333	81.92 300	67.90 339	92.88 310

new_pmnet		84.49 313	82.92 313	89.21 321	90.03 334	82.60 327	96.89 318	95.62 320	80.59 326	75.77 329	89.17 328	65.04 334	94.79 322	72.12 333	81.02 284	90.23 335

test_normal		92.44 212	90.54 225	98.12 134	91.85 319	96.18 136	99.68 143	97.73 201	92.66 144	75.76 330	93.74 309	70.49 314	99.04 146	95.71 128	97.27 136	99.13 181

EG-PatchMatch MVS		85.35 308	83.81 309	89.99 318	90.39 332	81.89 333	98.21 293	96.09 312	81.78 321	74.73 331	93.72 310	51.56 354	97.12 262	79.16 315	88.61 234	90.96 328

v18		86.59 291	84.57 295	92.65 283	93.41 281	93.43 202	98.69 257	95.55 322	82.44 312	74.71 332	87.68 337	82.11 216	94.21 324	80.14 309	66.37 343	90.32 332

v17		86.51 293	84.49 296	92.57 287	93.38 283	93.29 212	98.61 265	95.54 323	82.32 313	74.69 333	87.63 338	82.03 217	94.17 326	80.02 310	66.17 344	90.26 334

v16		86.52 292	84.49 296	92.60 286	93.45 277	93.31 211	98.60 266	95.52 325	82.30 314	74.59 334	87.70 336	81.95 223	94.18 325	79.93 311	66.38 342	90.30 333

v15		86.26 296	84.19 299	92.47 289	93.29 288	93.28 213	98.53 271	95.47 326	82.24 316	74.34 335	87.34 340	81.71 226	94.07 327	79.39 312	65.42 345	90.06 340

v11		86.09 301	83.98 305	92.42 291	93.29 288	93.41 206	98.52 272	95.30 333	81.73 322	74.27 336	87.20 342	81.24 237	93.85 336	77.68 323	66.61 341	90.00 342

V14		86.22 297	84.15 300	92.41 292	93.30 287	93.16 214	98.47 274	95.47 326	82.10 317	74.27 336	87.41 339	81.73 225	94.02 329	79.26 313	65.37 347	90.04 341

V9		86.16 299	84.07 301	92.43 290	93.27 290	93.04 219	98.40 281	95.45 328	81.98 319	74.18 338	87.31 341	81.58 232	94.06 328	79.12 316	65.33 348	90.20 337

pmmvs-eth3d		84.03 315	81.97 315	90.20 315	84.15 347	87.09 308	98.10 297	94.73 342	83.05 307	74.10 339	87.77 335	65.56 332	94.01 330	81.08 304	69.24 336	89.49 346

v12		86.10 300	84.01 302	92.37 294	93.23 293	92.96 220	98.33 284	95.45 328	81.87 320	74.05 340	87.15 343	81.60 231	93.98 332	79.09 317	65.28 349	90.18 338

v13		86.06 302	83.97 306	92.34 296	93.25 291	92.85 222	98.26 288	95.44 330	81.70 323	74.02 341	87.11 345	81.58 232	94.00 331	78.94 318	65.41 346	90.18 338

new-patchmatchnet		81.19 319	79.34 321	86.76 329	82.86 349	80.36 341	97.92 301	95.27 335	82.09 318	72.02 342	86.87 346	62.81 339	90.74 345	71.10 334	63.08 351	89.19 348

UnsupCasMVSNet_bld		79.97 323	77.03 326	88.78 324	85.62 345	81.98 332	93.66 340	97.35 240	75.51 341	70.79 343	83.05 351	48.70 355	94.91 320	78.31 320	60.29 354	89.46 347

Test4		88.80 281	85.91 290	97.48 155	87.33 341	95.72 152	99.29 202	97.04 269	92.82 133	70.35 344	91.46 323	44.37 357	97.43 233	93.37 176	97.17 142	99.29 160

CMPMVS	61.59 21	84.75 311	85.14 293	83.57 331	90.32 333	62.54 354	96.98 316	97.59 215	74.33 344	69.95 345	96.66 222	64.17 335	98.32 200	87.88 246	88.41 238	89.84 344
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test1235678		78.45 325	77.88 324	80.16 337	77.83 354	62.18 355	98.36 282	93.45 352	77.46 334	69.08 346	88.23 330	60.33 344	85.41 355	58.46 351	77.68 314	92.90 308

1111		79.11 324	78.74 323	80.23 336	78.33 352	67.13 349	97.31 309	93.65 349	71.34 348	68.35 347	87.87 333	85.42 195	88.46 348	52.93 355	73.46 331	85.11 351

.test1245		71.48 327	71.80 328	70.51 346	78.33 352	67.13 349	97.31 309	93.65 349	71.34 348	68.35 347	87.87 333	85.42 195	88.46 348	52.93 355	11.01 365	55.94 364

test12356		75.26 326	75.12 327	75.67 342	74.02 357	60.60 357	96.43 322	92.15 354	74.17 345	66.35 349	88.11 331	52.29 352	84.36 357	57.41 352	75.12 327	82.05 352

testmvs		40.60 343	44.45 344	29.05 357	19.49 373	14.11 373	99.68 143	18.47 373	20.74 366	64.59 350	98.48 174	10.95 370	17.09 370	56.66 354	11.01 365	55.94 364

testing_2		85.10 309	81.72 316	95.22 208	82.25 350	94.16 184	97.54 305	97.01 273	88.15 251	62.23 351	86.43 348	44.43 356	97.18 253	92.28 193	85.20 262	94.31 241

LCM-MVSNet		67.77 329	64.73 332	76.87 339	62.95 366	56.25 360	89.37 354	93.74 348	44.53 359	61.99 352	80.74 352	20.42 367	86.53 353	69.37 337	59.50 355	87.84 349

PMMVS2		67.15 331	64.15 333	76.14 340	70.56 360	62.07 356	93.89 338	87.52 365	58.09 355	60.02 353	78.32 353	22.38 365	84.54 356	59.56 350	47.03 356	81.80 353

Gipuma		66.95 332	65.00 331	72.79 343	91.52 324	67.96 348	66.16 363	95.15 339	47.89 357	58.54 354	67.99 360	29.74 361	87.54 352	50.20 357	77.83 312	62.87 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		85.50 307	83.33 310	92.00 299	90.89 329	88.38 301	99.22 207	96.55 303	79.60 330	57.26 355	92.72 317	79.09 265	93.78 337	77.25 325	77.37 318	93.84 283

MDA-MVSNet_test_wron		85.51 306	83.32 311	92.10 298	90.96 328	88.58 297	99.20 208	96.52 304	79.70 328	57.12 356	92.69 318	79.11 264	93.86 335	77.10 326	77.46 317	93.86 282

MDA-MVSNet-bldmvs		84.09 314	81.52 318	91.81 302	91.32 326	88.00 304	98.67 260	95.92 315	80.22 327	55.60 357	93.32 313	68.29 324	93.60 339	73.76 332	76.61 323	93.82 285

testmv		67.54 330	65.93 330	72.37 344	64.46 365	54.05 361	95.09 334	90.07 359	68.90 353	55.16 358	77.63 355	30.39 359	82.61 359	49.42 358	62.26 353	80.45 354

FPMVS		68.72 328	68.72 329	68.71 347	65.95 362	44.27 369	95.97 330	94.74 341	51.13 356	53.26 359	90.50 327	25.11 364	83.00 358	60.80 349	80.97 286	78.87 355

test123		37.68 344	39.14 346	33.31 355	19.94 372	24.83 372	98.36 282	9.75 374	15.53 367	51.31 360	87.14 344	19.62 368	17.74 369	47.10 360	3.47 368	57.36 363

tmp_tt		65.23 333	62.94 334	72.13 345	44.90 370	50.03 365	81.05 358	89.42 364	38.45 361	48.51 361	99.90 11	54.09 351	78.70 361	91.84 198	18.26 364	87.64 350

no-one		63.48 334	59.26 335	76.14 340	66.71 361	65.06 352	92.75 343	89.92 360	68.96 352	46.96 362	66.55 361	21.74 366	87.68 351	57.07 353	22.69 363	75.68 357

E-PMN		52.30 338	52.18 339	52.67 353	71.51 358	45.40 366	93.62 341	76.60 370	36.01 363	43.50 363	64.13 363	27.11 363	67.31 366	31.06 365	26.06 360	45.30 367

EMVS		51.44 340	51.22 341	52.11 354	70.71 359	44.97 368	94.04 337	75.66 371	35.34 365	42.40 364	61.56 366	28.93 362	65.87 367	27.64 366	24.73 361	45.49 366

MVE	53.74 22	51.54 339	47.86 342	62.60 351	59.56 367	50.93 363	79.41 359	77.69 369	35.69 364	36.27 365	61.76 365	5.79 375	69.63 364	37.97 364	36.61 358	67.24 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		56.44 335	53.54 337	65.14 350	65.34 363	50.33 364	89.06 355	79.57 367	45.77 358	35.75 366	68.95 359	10.75 371	74.40 362	48.48 359	38.20 357	70.70 358

ANet_high		56.10 336	52.24 338	67.66 348	49.27 369	56.82 359	83.94 357	82.02 366	70.47 350	33.28 367	64.54 362	17.23 369	69.16 365	45.59 362	23.85 362	77.02 356

PMVS	49.05 23	53.75 337	51.34 340	60.97 352	40.80 371	34.68 370	74.82 361	89.62 363	37.55 362	28.67 368	72.12 356	7.09 372	81.63 360	43.17 363	68.21 338	66.59 361
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		50.36 341	45.43 343	65.16 349	51.13 368	51.75 362	77.46 360	78.42 368	41.45 360	26.98 369	54.30 368	6.13 373	74.03 363	46.82 361	26.19 359	69.71 359

wuyk23d		20.37 347	20.84 348	18.99 358	65.34 363	27.73 371	50.43 364	7.67 375	9.50 368	8.01 370	6.34 370	6.13 373	26.24 368	23.40 367	10.69 367	2.99 368

cdsmvs_eth3d_5k		23.43 346	31.24 347	0.00 359	0.00 374	0.00 374	0.00 365	98.09 172	0.00 369	0.00 371	99.67 77	83.37 208	0.00 371	0.00 368	0.00 369	0.00 369

pcd_1.5k_mvsjas		7.60 349	10.13 350	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	91.20 130	0.00 371	0.00 368	0.00 369	0.00 369

pcd1.5k->3k		37.58 345	39.62 345	31.46 356	92.73 305	0.00 374	0.00 365	97.52 222	0.00 369	0.00 371	0.00 371	78.40 273	0.00 371	0.00 368	87.90 242	94.37 235

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

ab-mvs-re		8.28 348	11.04 349	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	99.40 96	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

GSMVS																99.59 114

test_part1				0.00 359		0.00 374	0.00 365	98.41 129				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sam_mvs1												94.72 60				99.59 114

sam_mvs												94.25 72

MTGPA								98.28 149

test_post1							95.78 332				59.23 367	93.20 100	97.74 225	91.06 205

test_post											63.35 364	94.43 62	98.13 210

patchmatchnet-post											91.70 322	95.12 46	97.95 220

MTMP							99.87 72	96.49 305

gm-plane-assit					96.97 202	93.76 196			91.47 190		98.96 127		98.79 157	94.92 135

test9_res														99.71 17	99.99 14	100.00 1

agg_prior2														99.48 23	100.00 1	100.00 1

test_prior4						98.05 63	99.94 46

test_prior				99.43 27	99.94 14	98.49 50		98.65 70					99.80 92			99.99 12

新几何2							99.40 185

旧先验1					99.76 56	97.52 81		98.64 73			99.85 21	95.63 37			99.94 43	99.99 12

无先验							99.49 175	98.71 63	93.46 118				100.00 1	94.36 150		99.99 12

原ACMM2							99.90 62

testdata2													99.99 27	90.54 215

segment_acmp												96.68 15

testdata1							99.28 203		96.35 39

plane_prior7					95.71 239	91.59 258

plane_prior6					95.76 234	91.72 253						80.47 251

plane_prior5								97.87 192					98.37 197	97.79 90	89.55 221	94.52 224

plane_prior4											98.59 165

plane_prior2							99.84 95		96.38 35

plane_prior1					95.73 236

plane_prior						91.74 250	99.86 89		96.76 26						89.59 220

n2								0.00 376

nn								0.00 376

door-mid								89.69 362

test11								98.44 113

door								90.31 358

HQP5-MVS						91.85 245

BP-MVS														97.92 87

HQP3-MVS								97.89 190							89.60 218

HQP2-MVS												80.65 247

NP-MVS					95.77 233	91.79 247					98.65 161

ACMMP++_ref															87.04 250

ACMMP++															88.23 240

Test By Simon												92.82 107

High-resolution multi-view benchmark