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 3	99.98 2	99.51 3	99.98 8	98.69 54	98.20 3	99.93 1	99.98 2	96.82 17	100.00 1	99.75 15	100.00 1	99.99 15

NCCC		99.37 2	99.25 2	99.71 8	99.96 8	99.15 12	99.97 16	98.62 66	98.02 6	99.90 2	99.95 3	97.33 12	100.00 1	99.54 24	100.00 1	100.00 1

TSAR-MVS + MP.		98.93 13	98.77 14	99.41 33	99.74 64	98.67 38	99.77 115	98.38 130	96.73 30	99.88 3	99.74 68	94.89 57	99.59 126	99.80 10	99.98 30	99.97 58
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test0726					99.93 26	99.29 6	99.96 23	98.42 118	97.28 16	99.86 4	99.94 4	97.22 14

xiu_mvs_v2_base		98.23 54	97.97 54	99.02 69	98.69 122	98.66 39	99.52 161	98.08 168	97.05 21	99.86 4	99.86 22	90.65 142	99.71 115	99.39 32	98.63 115	98.69 189

PS-MVSNAJ		98.44 42	98.20 44	99.16 51	98.80 118	98.92 18	99.54 159	98.17 157	97.34 14	99.85 6	99.85 26	91.20 133	99.89 75	99.41 31	99.67 82	98.69 189

旧先验2							99.46 170		94.21 100	99.85 6			99.95 56	96.96 120

save filter2										99.83 8	99.91 10	95.88 29	100.00 1	99.80 10	99.99 17	99.99 15

DVP-MVS		99.30 4	99.16 3	99.73 6	99.93 26	99.29 6	99.95 37	98.32 140	97.28 16	99.83 8	99.91 10	97.22 14	100.00 1	99.99 1	100.00 1	99.89 78

test_0728_THIRD									96.48 35	99.83 8	99.91 10	97.87 4	100.00 1	99.92 4	100.00 1	100.00 1

SD-MVS		98.92 14	98.70 15	99.56 18	99.70 72	98.73 35	99.94 52	98.34 137	96.38 39	99.81 11	99.76 61	94.59 60	99.98 38	99.84 8	99.96 41	99.97 58
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
MSLP-MVS++		99.13 6	99.01 8	99.49 26	99.94 14	98.46 50	99.98 8	98.86 44	97.10 20	99.80 12	99.94 4	95.92 28	100.00 1	99.51 25	100.00 1	100.00 1

SteuartSystems-ACMMP		99.02 10	98.97 10	99.18 47	98.72 121	97.71 72	99.98 8	98.44 103	96.85 24	99.80 12	99.91 10	97.57 6	99.85 86	99.44 29	99.99 17	99.99 15
Skip Steuart: Steuart Systems R&D Blog.
testdata				98.42 109	99.47 85	95.33 154		98.56 76	93.78 120	99.79 14	99.85 26	93.64 91	99.94 64	94.97 143	99.94 49	100.00 1

9.14			98.38 33		99.87 44		99.91 64	98.33 138	93.22 134	99.78 15	99.89 16	94.57 61	99.85 86	99.84 8	99.97 38

SMA-MVS		98.76 23	98.48 26	99.62 13	99.87 44	98.87 22	99.86 90	98.38 130	93.19 135	99.77 16	99.94 4	95.54 36	100.00 1	99.74 17	99.99 17	100.00 1

CDPH-MVS		98.65 26	98.36 37	99.49 26	99.94 14	98.73 35	99.87 79	98.33 138	93.97 111	99.76 17	99.87 20	94.99 53	99.75 107	98.55 72	100.00 1	99.98 47

APD-MVS		98.62 27	98.35 38	99.41 33	99.90 37	98.51 47	99.87 79	98.36 134	94.08 104	99.74 18	99.73 70	94.08 78	99.74 111	99.42 30	99.99 17	99.99 15
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_prior3		98.99 12	98.84 13	99.43 29	99.94 14	98.49 48	99.95 37	98.65 59	95.78 53	99.73 19	99.76 61	96.00 24	99.80 97	99.78 13	100.00 1	99.99 15

test_prior2							99.95 37		95.78 53	99.73 19	99.76 61	96.00 24		99.78 13	100.00 1

testtj		98.89 16	98.69 16	99.52 22	99.94 14	98.56 44	99.90 68	98.55 80	95.14 72	99.72 21	99.84 39	95.46 40	100.00 1	99.65 23	99.99 17	99.99 15

TEST9					99.92 31	98.92 18	99.96 23	98.43 110	93.90 116	99.71 22	99.86 22	95.88 29	99.85 86

train_agg		98.88 17	98.65 18	99.59 16	99.92 31	98.92 18	99.96 23	98.43 110	94.35 94	99.71 22	99.86 22	95.94 26	99.85 86	99.69 22	99.98 30	99.99 15

test_8					99.92 31	98.88 21	99.96 23	98.43 110	94.35 94	99.69 24	99.85 26	95.94 26	99.85 86

test12				99.43 29	99.74 64	98.56 44		98.40 123		99.65 25		94.76 58	99.75 107		99.98 30	99.99 15

DPE-MVS		99.26 5	99.10 6	99.74 5	99.89 40	99.24 10	99.87 79	98.44 103	97.48 13	99.64 26	99.94 4	96.68 18	99.99 33	99.99 1	100.00 1	99.99 15

agg_prior1		98.88 17	98.66 17	99.54 20	99.93 26	98.77 30	99.96 23	98.43 110	94.63 86	99.63 27	99.85 26	95.79 33	99.85 86	99.72 20	99.99 17	99.99 15

agg_prior					99.93 26	98.77 30		98.43 110		99.63 27			99.85 86

xiu_mvs_v1_base_debu		97.43 80	97.06 83	98.55 96	97.74 168	98.14 56	99.31 187	97.86 188	96.43 36	99.62 29	99.69 78	85.56 187	99.68 119	99.05 40	98.31 122	97.83 198

xiu_mvs_v1_base		97.43 80	97.06 83	98.55 96	97.74 168	98.14 56	99.31 187	97.86 188	96.43 36	99.62 29	99.69 78	85.56 187	99.68 119	99.05 40	98.31 122	97.83 198

xiu_mvs_v1_base_debi		97.43 80	97.06 83	98.55 96	97.74 168	98.14 56	99.31 187	97.86 188	96.43 36	99.62 29	99.69 78	85.56 187	99.68 119	99.05 40	98.31 122	97.83 198

原ACMM1				98.96 74	99.73 68	96.99 101		98.51 93	94.06 107	99.62 29	99.85 26	94.97 54	99.96 49	95.11 141	99.95 44	99.92 75

PHI-MVS		98.41 44	98.21 43	99.03 67	99.86 46	97.10 98	99.98 8	98.80 49	90.78 208	99.62 29	99.78 55	95.30 43	100.00 1	99.80 10	99.93 55	99.99 15

DPM-MVS		98.83 19	98.46 27	99.97 1	99.33 91	99.92 1	99.96 23	98.44 103	97.96 7	99.55 34	99.94 4	97.18 16	100.00 1	93.81 174	99.94 49	99.98 47

新几何1				99.42 32	99.75 63	98.27 54		98.63 65	92.69 152	99.55 34	99.82 45	94.40 64	100.00 1	91.21 205	99.94 49	99.99 15

ACMMP_NAP		98.49 38	98.14 47	99.54 20	99.66 74	98.62 43	99.85 92	98.37 133	94.68 84	99.53 36	99.83 42	92.87 105	100.00 1	98.66 68	99.84 67	99.99 15

1121		98.03 60	97.57 69	99.40 35	99.74 64	98.21 55	98.31 262	98.62 66	92.78 147	99.53 36	99.83 42	95.08 47	100.00 1	94.36 160	99.92 57	99.99 15

PMMVS		96.76 107	96.76 95	96.76 166	98.28 135	92.10 217	99.91 64	97.98 175	94.12 102	99.53 36	99.39 107	86.93 176	98.73 161	96.95 121	97.73 133	99.45 143

MSP-MVS		99.09 7	99.12 5	98.98 72	99.93 26	97.24 91	99.95 37	98.42 118	97.50 12	99.52 39	99.88 17	97.43 11	99.71 115	99.50 26	99.98 30	100.00 1

test_part2					99.89 40	99.25 9				99.49 40

APDe-MVS		99.06 9	98.91 11	99.51 24	99.94 14	98.76 34	99.91 64	98.39 126	97.20 19	99.46 41	99.85 26	95.53 39	99.79 99	99.86 7	100.00 1	99.99 15

region2R		98.54 34	98.37 35	99.05 65	99.96 8	97.18 94	99.96 23	98.55 80	94.87 78	99.45 42	99.85 26	94.07 79	100.00 1	98.67 65	100.00 1	99.98 47

HPM-MVS++		99.07 8	98.88 12	99.63 10	99.90 37	99.02 15	99.95 37	98.56 76	97.56 11	99.44 43	99.85 26	95.38 42	100.00 1	99.31 33	99.99 17	99.87 81

MVSFormer		96.94 99	96.60 99	97.95 128	97.28 188	97.70 74	99.55 157	97.27 240	91.17 197	99.43 44	99.54 95	90.92 140	96.89 261	94.67 155	99.62 85	99.25 163

lupinMVS		97.85 66	97.60 67	98.62 90	97.28 188	97.70 74	99.99 4	97.55 213	95.50 64	99.43 44	99.67 82	90.92 140	98.71 163	98.40 76	99.62 85	99.45 143

Regformer-1		98.79 21	98.60 21	99.36 38	99.85 47	98.34 52	99.87 79	98.52 87	96.05 48	99.41 46	99.79 51	94.93 55	99.76 104	99.07 39	99.90 59	99.99 15

Regformer-2		98.78 22	98.59 22	99.36 38	99.85 47	98.32 53	99.87 79	98.52 87	96.04 49	99.41 46	99.79 51	94.92 56	99.76 104	99.05 40	99.90 59	99.98 47

XVS		98.70 25	98.55 23	99.15 53	99.94 14	97.50 82	99.94 52	98.42 118	96.22 44	99.41 46	99.78 55	94.34 68	99.96 49	98.92 51	99.95 44	99.99 15

X-MVStestdata		93.83 175	92.06 199	99.15 53	99.94 14	97.50 82	99.94 52	98.42 118	96.22 44	99.41 46	41.37 336	94.34 68	99.96 49	98.92 51	99.95 44	99.99 15

APD-MVS_3200maxsize		98.25 53	98.08 50	98.78 81	99.81 58	96.60 112	99.82 103	98.30 142	93.95 113	99.37 50	99.77 57	92.84 106	99.76 104	98.95 48	99.92 57	99.97 58

PGM-MVS		98.34 47	98.13 48	98.99 71	99.92 31	97.00 100	99.75 121	99.50 16	93.90 116	99.37 50	99.76 61	93.24 100	100.00 1	97.75 103	99.96 41	99.98 47

SR-MVS		98.46 40	98.30 41	98.93 76	99.88 43	97.04 99	99.84 96	98.35 135	94.92 75	99.32 52	99.80 49	93.35 94	99.78 101	99.30 34	99.95 44	99.96 62

HFP-MVS		98.56 32	98.37 35	99.14 55	99.96 8	97.43 86	99.95 37	98.61 68	94.77 80	99.31 53	99.85 26	94.22 73	100.00 1	98.70 63	99.98 30	99.98 47

#test#		98.59 30	98.41 29	99.14 55	99.96 8	97.43 86	99.95 37	98.61 68	95.00 74	99.31 53	99.85 26	94.22 73	100.00 1	98.78 60	99.98 30	99.98 47

ACMMPR		98.50 37	98.32 39	99.05 65	99.96 8	97.18 94	99.95 37	98.60 70	94.77 80	99.31 53	99.84 39	93.73 88	100.00 1	98.70 63	99.98 30	99.98 47

EIA-MVS		97.92 64	97.80 61	98.25 117	98.14 146	96.48 114	99.98 8	97.63 201	95.61 61	99.29 56	99.46 101	92.55 114	98.82 153	99.02 46	98.54 116	99.46 141

test222					99.55 80	97.41 89	99.34 183	98.55 80	91.86 180	99.27 57	99.83 42	93.84 86			99.95 44	99.99 15

abl_6		97.67 76	97.34 76	98.66 87	99.68 73	96.11 134	99.68 136	98.14 163	93.80 119	99.27 57	99.70 75	88.65 162	99.98 38	97.46 107	99.72 79	99.89 78

CS-MVS		97.84 67	97.69 63	98.31 114	98.28 135	96.27 120	100.00 1	97.52 219	95.29 68	99.25 59	99.65 86	91.18 136	98.94 150	98.96 47	99.04 108	99.73 96

CANet_DTU		96.76 107	96.15 110	98.60 92	98.78 119	97.53 78	99.84 96	97.63 201	97.25 18	99.20 60	99.64 87	81.36 217	99.98 38	92.77 193	98.89 109	98.28 192

EPNet		98.49 38	98.40 31	98.77 82	99.62 76	96.80 107	99.90 68	99.51 15	97.60 10	99.20 60	99.36 110	93.71 89	99.91 70	97.99 92	98.71 114	99.61 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	95.94 2	97.71 75	98.98 9	93.92 244	99.63 75	81.76 310	99.96 23	98.56 76	99.47 1	99.19 62	99.99 1	94.16 77	100.00 1	99.92 4	99.93 55	100.00 1

VNet		97.21 91	96.57 101	99.13 60	98.97 104	97.82 70	99.03 216	99.21 27	94.31 97	99.18 63	98.88 147	86.26 182	99.89 75	98.93 50	94.32 191	99.69 102

MCST-MVS		99.32 3	99.14 4	99.86 2	99.97 3	99.59 2	99.97 16	98.64 62	98.47 2	99.13 64	99.92 9	96.38 21	100.00 1	99.74 17	100.00 1	100.00 1

DeepC-MVS_fast	96.59 1	98.81 20	98.54 24	99.62 13	99.90 37	98.85 24	99.24 194	98.47 99	98.14 4	99.08 65	99.91 10	93.09 103	100.00 1	99.04 44	99.99 17	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
114514_t		97.41 84	96.83 91	99.14 55	99.51 84	97.83 69	99.89 74	98.27 147	88.48 242	99.06 66	99.66 84	90.30 145	99.64 125	96.32 128	99.97 38	99.96 62

PVSNet	91.05 13	97.13 92	96.69 97	98.45 106	99.52 82	95.81 139	99.95 37	99.65 10	94.73 82	99.04 67	99.21 121	84.48 196	99.95 56	94.92 144	98.74 113	99.58 125

CHOSEN 280x420		99.01 11	99.03 7	98.95 75	99.38 89	98.87 22	98.46 255	99.42 20	97.03 22	99.02 68	99.09 125	99.35 1	98.21 202	99.73 19	99.78 75	99.77 92

Regformer-3		98.58 31	98.41 29	99.10 61	99.84 52	97.57 76	99.66 139	98.52 87	95.79 52	99.01 69	99.77 57	94.40 64	99.75 107	98.82 56	99.83 68	99.98 47

Regformer-4		98.56 32	98.39 32	99.08 63	99.84 52	97.52 79	99.66 139	98.52 87	95.76 55	99.01 69	99.77 57	94.33 70	99.75 107	98.80 59	99.83 68	99.98 47

MG-MVS		98.91 15	98.65 18	99.68 9	99.94 14	99.07 14	99.64 146	99.44 18	97.33 15	99.00 71	99.72 71	94.03 80	99.98 38	98.73 62	100.00 1	100.00 1

diffmvs		97.00 97	96.64 98	98.09 124	97.64 175	96.17 129	99.81 105	97.19 244	94.67 85	98.95 72	99.28 111	86.43 180	98.76 159	98.37 77	97.42 141	99.33 156

HPM-MVS_fast		97.80 71	97.50 70	98.68 85	99.79 60	96.42 116	99.88 76	98.16 160	91.75 184	98.94 73	99.54 95	91.82 128	99.65 124	97.62 105	99.99 17	99.99 15

CP-MVS		98.45 41	98.32 39	98.87 78	99.96 8	96.62 111	99.97 16	98.39 126	94.43 91	98.90 74	99.87 20	94.30 71	100.00 1	99.04 44	99.99 17	99.99 15

MVS_Test		96.46 119	95.74 129	98.61 91	98.18 143	97.23 92	99.31 187	97.15 250	91.07 201	98.84 75	97.05 207	88.17 165	98.97 148	94.39 159	97.50 138	99.61 115

API-MVS		97.86 65	97.66 64	98.47 104	99.52 82	95.41 152	99.47 168	98.87 43	91.68 185	98.84 75	99.85 26	92.34 117	99.99 33	98.44 75	99.96 41	100.00 1

GST-MVS		98.27 50	97.97 54	99.17 50	99.92 31	97.57 76	99.93 57	98.39 126	94.04 109	98.80 77	99.74 68	92.98 104	100.00 1	98.16 82	99.76 76	99.93 71

MVS_111021_LR		98.42 43	98.38 33	98.53 101	99.39 88	95.79 140	99.87 79	99.86 2	96.70 31	98.78 78	99.79 51	92.03 123	99.90 71	99.17 36	99.86 66	99.88 80

TSAR-MVS + GP.		98.60 28	98.51 25	98.86 79	99.73 68	96.63 110	99.97 16	97.92 182	98.07 5	98.76 79	99.55 93	95.00 52	99.94 64	99.91 6	97.68 135	99.99 15

sss		97.57 78	97.03 87	99.18 47	98.37 131	98.04 61	99.73 129	99.38 21	93.46 129	98.76 79	99.06 127	91.21 132	99.89 75	96.33 127	97.01 152	99.62 113

CostFormer		96.10 130	95.88 126	96.78 165	97.03 194	92.55 209	97.08 289	97.83 191	90.04 219	98.72 81	94.89 274	95.01 51	98.29 196	96.54 126	95.77 174	99.50 138

tpmrst		96.27 129	95.98 116	97.13 157	97.96 153	93.15 196	96.34 298	98.17 157	92.07 174	98.71 82	95.12 265	93.91 83	98.73 161	94.91 146	96.62 157	99.50 138

MVS_111021_HR		98.72 24	98.62 20	99.01 70	99.36 90	97.18 94	99.93 57	99.90 1	96.81 28	98.67 83	99.77 57	93.92 82	99.89 75	99.27 35	99.94 49	99.96 62

MAR-MVS		97.43 80	97.19 80	98.15 122	99.47 85	94.79 170	99.05 214	98.76 50	92.65 156	98.66 84	99.82 45	88.52 163	99.98 38	98.12 84	99.63 84	99.67 105
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+		96.30 126	95.69 130	98.16 119	97.85 160	96.26 122	97.41 285	97.21 243	90.37 212	98.65 85	98.58 167	86.61 179	98.70 164	97.11 115	97.37 143	99.52 135

HPM-MVS		97.96 61	97.72 62	98.68 85	99.84 52	96.39 119	99.90 68	98.17 157	92.61 158	98.62 86	99.57 92	91.87 126	99.67 122	98.87 54	99.99 17	99.99 15
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		98.39 46	98.20 44	98.97 73	99.97 3	96.92 104	99.95 37	98.38 130	95.04 73	98.61 87	99.80 49	93.39 93	100.00 1	98.64 69	100.00 1	99.98 47

jason		97.24 89	96.86 90	98.38 112	95.73 231	97.32 90	99.97 16	97.40 232	95.34 67	98.60 88	99.54 95	87.70 167	98.56 171	97.94 95	99.47 96	99.25 163
jason: jason.
CANet		98.27 50	97.82 60	99.63 10	99.72 70	99.10 13	99.98 8	98.51 93	97.00 23	98.52 89	99.71 73	87.80 166	99.95 56	99.75 15	99.38 100	99.83 84

EI-MVSNet-Vis-set		98.27 50	98.11 49	98.75 83	99.83 55	96.59 113	99.40 174	98.51 93	95.29 68	98.51 90	99.76 61	93.60 92	99.71 115	98.53 73	99.52 93	99.95 68

LFMVS		94.75 158	93.56 172	98.30 115	99.03 99	95.70 146	98.74 238	97.98 175	87.81 250	98.47 91	99.39 107	67.43 298	99.53 127	98.01 90	95.20 185	99.67 105

tpm2		95.47 145	95.18 143	96.35 179	96.91 199	91.70 231	96.96 292	97.93 180	88.04 248	98.44 92	95.40 251	93.32 96	97.97 211	94.00 169	95.61 178	99.38 150

alignmvs		97.81 70	97.33 77	99.25 42	98.77 120	98.66 39	99.99 4	98.44 103	94.40 93	98.41 93	99.47 99	93.65 90	99.42 137	98.57 71	94.26 192	99.67 105

UA-Net		96.54 116	95.96 121	98.27 116	98.23 140	95.71 145	98.00 276	98.45 102	93.72 123	98.41 93	99.27 114	88.71 161	99.66 123	91.19 206	97.69 134	99.44 145

DP-MVS Recon		98.41 44	98.02 51	99.56 18	99.97 3	98.70 37	99.92 60	98.44 103	92.06 176	98.40 95	99.84 39	95.68 34	100.00 1	98.19 80	99.71 80	99.97 58

CPTT-MVS		97.64 77	97.32 78	98.58 95	99.97 3	95.77 141	99.96 23	98.35 135	89.90 220	98.36 96	99.79 51	91.18 136	99.99 33	98.37 77	99.99 17	99.99 15

PAPM		98.60 28	98.42 28	99.14 55	96.05 219	98.96 16	99.90 68	99.35 23	96.68 32	98.35 97	99.66 84	96.45 20	98.51 174	99.45 28	99.89 61	99.96 62

HY-MVS	92.50 7	97.79 72	97.17 82	99.63 10	98.98 103	99.32 5	97.49 284	99.52 13	95.69 59	98.32 98	97.41 194	93.32 96	99.77 102	98.08 88	95.75 176	99.81 86

EI-MVSNet-UG-set		98.14 56	97.99 53	98.60 92	99.80 59	96.27 120	99.36 182	98.50 97	95.21 71	98.30 99	99.75 66	93.29 98	99.73 114	98.37 77	99.30 102	99.81 86

PVSNet_BlendedMVS		96.05 131	95.82 128	96.72 168	99.59 77	96.99 101	99.95 37	99.10 28	94.06 107	98.27 100	95.80 238	89.00 157	99.95 56	99.12 37	87.53 238	93.24 289

PVSNet_Blended		97.94 62	97.64 65	98.83 80	99.59 77	96.99 101	100.00 1	99.10 28	95.38 65	98.27 100	99.08 126	89.00 157	99.95 56	99.12 37	99.25 103	99.57 126

MP-MVS		98.23 54	97.97 54	99.03 67	99.94 14	97.17 97	99.95 37	98.39 126	94.70 83	98.26 102	99.81 48	91.84 127	100.00 1	98.85 55	99.97 38	99.93 71
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
WTY-MVS		98.10 58	97.60 67	99.60 15	98.92 110	99.28 8	99.89 74	99.52 13	95.58 62	98.24 103	99.39 107	93.33 95	99.74 111	97.98 94	95.58 179	99.78 91

DELS-MVS		98.54 34	98.22 42	99.50 25	99.15 96	98.65 41	100.00 1	98.58 72	97.70 8	98.21 104	99.24 119	92.58 113	99.94 64	98.63 70	99.94 49	99.92 75
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
DWT-MVSNet_test		97.31 86	97.19 80	97.66 140	98.24 139	94.67 172	98.86 233	98.20 155	93.60 127	98.09 105	98.89 145	97.51 7	98.78 156	94.04 168	97.28 144	99.55 128

MDTV_nov1_ep13_2view						96.26 122	96.11 301		91.89 179	98.06 106		94.40 64		94.30 163		99.67 105

PAPR		98.52 36	98.16 46	99.58 17	99.97 3	98.77 30	99.95 37	98.43 110	95.35 66	98.03 107	99.75 66	94.03 80	99.98 38	98.11 85	99.83 68	99.99 15

MDTV_nov1_ep13			95.69 130		97.90 156	94.15 179	95.98 303	98.44 103	93.12 137	97.98 108	95.74 240	95.10 46	98.58 170	90.02 226	96.92 154

GG-mvs-BLEND				98.54 99	98.21 141	98.01 62	93.87 312	98.52 87		97.92 109	97.92 187	99.02 2	97.94 216	98.17 81	99.58 90	99.67 105

ETV-MVS		97.53 79	97.46 71	97.76 136	98.04 150	94.84 166	99.98 8	97.61 207	94.41 92	97.90 110	99.59 90	92.40 115	98.87 151	98.04 89	99.13 106	99.59 119

test_yl		97.83 68	97.37 74	99.21 44	99.18 93	97.98 64	99.64 146	99.27 25	91.43 193	97.88 111	98.99 134	95.84 31	99.84 94	98.82 56	95.32 183	99.79 88

DCV-MVSNet		97.83 68	97.37 74	99.21 44	99.18 93	97.98 64	99.64 146	99.27 25	91.43 193	97.88 111	98.99 134	95.84 31	99.84 94	98.82 56	95.32 183	99.79 88

canonicalmvs		97.09 95	96.32 107	99.39 36	98.93 108	98.95 17	99.72 131	97.35 235	94.45 89	97.88 111	99.42 103	86.71 177	99.52 128	98.48 74	93.97 196	99.72 99

PatchFormer-LS_test		97.01 96	96.79 94	97.69 139	98.26 138	94.80 168	98.66 248	98.13 165	93.70 124	97.86 114	98.80 155	95.54 36	98.67 166	94.12 167	96.00 167	99.60 117

VDDNet		93.12 189	91.91 201	96.76 166	96.67 212	92.65 207	98.69 243	98.21 152	82.81 295	97.75 115	99.28 111	61.57 313	99.48 135	98.09 87	94.09 194	98.15 194

EPMVS		96.53 117	96.01 113	98.09 124	98.43 130	96.12 133	96.36 297	99.43 19	93.53 128	97.64 116	95.04 267	94.41 63	98.38 190	91.13 207	98.11 126	99.75 94

JIA-IIPM		91.76 218	90.70 216	94.94 207	96.11 217	87.51 283	93.16 315	98.13 165	75.79 314	97.58 117	77.68 324	92.84 106	97.97 211	88.47 237	96.54 158	99.33 156

EPNet_dtu		95.71 140	95.39 136	96.66 170	98.92 110	93.41 193	99.57 154	98.90 40	96.19 46	97.52 118	98.56 169	92.65 111	97.36 231	77.89 301	98.33 121	99.20 166
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PAPM_NR		98.12 57	97.93 58	98.70 84	99.94 14	96.13 130	99.82 103	98.43 110	94.56 87	97.52 118	99.70 75	94.40 64	99.98 38	97.00 118	99.98 30	99.99 15

thisisatest0515		97.41 84	97.02 88	98.59 94	97.71 174	97.52 79	99.97 16	98.54 84	91.83 181	97.45 120	99.04 128	97.50 8	99.10 143	94.75 152	96.37 162	99.16 168

OMC-MVS		97.28 87	97.23 79	97.41 149	99.76 61	93.36 195	99.65 142	97.95 178	96.03 50	97.41 121	99.70 75	89.61 151	99.51 129	96.73 125	98.25 125	99.38 150

gg-mvs-nofinetune		93.51 184	91.86 202	98.47 104	97.72 172	97.96 66	92.62 316	98.51 93	74.70 317	97.33 122	69.59 327	98.91 3	97.79 219	97.77 101	99.56 91	99.67 105

PatchT		90.38 242	88.75 255	95.25 198	95.99 221	90.16 255	91.22 323	97.54 215	76.80 310	97.26 123	86.01 319	91.88 125	96.07 290	66.16 321	95.91 171	99.51 136

PLC	95.54 3	97.93 63	97.89 59	98.05 126	99.82 56	94.77 171	99.92 60	98.46 101	93.93 114	97.20 124	99.27 114	95.44 41	99.97 47	97.41 108	99.51 95	99.41 148
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
zzz-MVS		98.33 48	98.00 52	99.30 40	99.85 47	97.93 67	99.80 109	98.28 144	95.76 55	97.18 125	99.88 17	92.74 109	100.00 1	98.67 65	99.88 63	99.99 15

MTAPA		98.29 49	97.96 57	99.30 40	99.85 47	97.93 67	99.39 177	98.28 144	95.76 55	97.18 125	99.88 17	92.74 109	100.00 1	98.67 65	99.88 63	99.99 15

PatchmatchNet		95.94 134	95.45 134	97.39 151	97.83 161	94.41 175	96.05 302	98.40 123	92.86 141	97.09 127	95.28 262	94.21 76	98.07 208	89.26 231	98.11 126	99.70 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thisisatest0530		97.10 93	96.72 96	98.22 118	97.60 177	96.70 108	99.92 60	98.54 84	91.11 200	97.07 128	98.97 138	97.47 9	99.03 145	93.73 179	96.09 165	98.92 179

CR-MVSNet		93.45 187	92.62 188	95.94 186	96.29 214	92.66 205	92.01 319	96.23 292	92.62 157	96.94 129	93.31 298	91.04 138	96.03 291	79.23 297	95.96 169	99.13 173

RPMNet		89.39 260	87.20 271	95.94 186	96.29 214	92.66 205	92.01 319	97.63 201	70.19 322	96.94 129	85.87 320	87.25 172	96.03 291	62.69 324	95.96 169	99.13 173

baseline		96.43 120	95.98 116	97.76 136	97.34 185	95.17 160	99.51 163	97.17 247	93.92 115	96.90 131	99.28 111	85.37 190	98.64 168	97.50 106	96.86 156	99.46 141

Vis-MVSNet		95.72 138	95.15 144	97.45 147	97.62 176	94.28 177	99.28 191	98.24 149	94.27 99	96.84 132	98.94 143	79.39 236	98.76 159	93.25 184	98.49 117	99.30 159
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
VDD-MVS		93.77 179	92.94 183	96.27 180	98.55 124	90.22 254	98.77 237	97.79 193	90.85 206	96.82 133	99.42 103	61.18 315	99.77 102	98.95 48	94.13 193	98.82 185

UGNet		95.33 147	94.57 153	97.62 143	98.55 124	94.85 165	98.67 245	99.32 24	95.75 58	96.80 134	96.27 230	72.18 279	99.96 49	94.58 157	99.05 107	98.04 196
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 90	96.80 93	98.51 102	99.99 1	95.60 148	99.09 203	98.84 46	93.32 132	96.74 135	99.72 71	86.04 183	100.00 1	98.01 90	99.43 99	99.94 70

tpm		93.70 182	93.41 177	94.58 219	95.36 242	87.41 284	97.01 290	96.90 271	90.85 206	96.72 136	94.14 290	90.40 144	96.84 264	90.75 217	88.54 227	99.51 136

tttt0517		96.85 102	96.49 103	97.92 130	97.48 182	95.89 138	99.85 92	98.54 84	90.72 209	96.63 137	98.93 144	97.47 9	99.02 146	93.03 191	95.76 175	98.85 183

mvs-test1		95.53 143	95.97 119	94.20 232	97.77 165	85.44 294	99.95 37	97.06 255	94.92 75	96.58 138	98.72 158	85.81 184	98.98 147	94.80 149	98.11 126	98.18 193

casdiffmvs		96.42 121	95.97 119	97.77 135	97.30 187	94.98 162	99.84 96	97.09 253	93.75 122	96.58 138	99.26 117	85.07 192	98.78 156	97.77 101	97.04 151	99.54 132

CNLPA		97.76 73	97.38 73	98.92 77	99.53 81	96.84 105	99.87 79	98.14 163	93.78 120	96.55 140	99.69 78	92.28 118	99.98 38	97.13 114	99.44 98	99.93 71

PatchMatch-RL		96.04 132	95.40 135	97.95 128	99.59 77	95.22 159	99.52 161	99.07 31	93.96 112	96.49 141	98.35 177	82.28 209	99.82 96	90.15 225	99.22 104	98.81 186

MP-MVS-pluss		98.07 59	97.64 65	99.38 37	99.74 64	98.41 51	99.74 124	98.18 156	93.35 131	96.45 142	99.85 26	92.64 112	99.97 47	98.91 53	99.89 61	99.77 92
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ADS-MVSNet2		93.80 178	93.88 164	93.55 253	97.87 158	85.94 290	94.24 308	96.84 275	90.07 217	96.43 143	94.48 285	90.29 146	95.37 300	87.44 246	97.23 146	99.36 152

ADS-MVSNet		94.79 155	94.02 161	97.11 159	97.87 158	93.79 185	94.24 308	98.16 160	90.07 217	96.43 143	94.48 285	90.29 146	98.19 203	87.44 246	97.23 146	99.36 152

ACMMP		97.74 74	97.44 72	98.66 87	99.92 31	96.13 130	99.18 198	99.45 17	94.84 79	96.41 145	99.71 73	91.40 130	99.99 33	97.99 92	98.03 131	99.87 81
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 88	96.81 92	98.66 87	98.81 117	96.67 109	99.92 60	98.64 62	94.51 88	96.38 146	98.49 171	89.05 156	99.88 81	97.10 116	98.34 120	99.43 146

thres200		96.96 98	96.21 109	99.22 43	98.97 104	98.84 25	99.85 92	99.71 5	93.17 136	96.26 147	98.88 147	89.87 149	99.51 129	94.26 164	94.91 186	99.31 158

HyFIR lowres test		96.66 114	96.43 105	97.36 153	99.05 98	93.91 184	99.70 133	99.80 3	90.54 210	96.26 147	98.08 182	92.15 121	98.23 201	96.84 124	95.46 180	99.93 71

SCA		94.69 159	93.81 166	97.33 155	97.10 190	94.44 174	98.86 233	98.32 140	93.30 133	96.17 149	95.59 245	76.48 255	97.95 214	91.06 209	97.43 139	99.59 119

tfpn200view9		96.79 105	95.99 114	99.19 46	98.94 106	98.82 26	99.78 112	99.71 5	92.86 141	96.02 150	98.87 149	89.33 152	99.50 131	93.84 171	94.57 187	99.27 161

thres400		96.78 106	95.99 114	99.16 51	98.94 106	98.82 26	99.78 112	99.71 5	92.86 141	96.02 150	98.87 149	89.33 152	99.50 131	93.84 171	94.57 187	99.16 168

dp		95.05 151	94.43 155	96.91 161	97.99 152	92.73 203	96.29 299	97.98 175	89.70 223	95.93 152	94.67 280	93.83 87	98.45 179	86.91 258	96.53 159	99.54 132

thres100view900		96.74 109	95.92 124	99.18 47	98.90 113	98.77 30	99.74 124	99.71 5	92.59 160	95.84 153	98.86 151	89.25 154	99.50 131	93.84 171	94.57 187	99.27 161

thres600view7		96.69 112	95.87 127	99.14 55	98.90 113	98.78 29	99.74 124	99.71 5	92.59 160	95.84 153	98.86 151	89.25 154	99.50 131	93.44 183	94.50 190	99.16 168

EPP-MVSNet		96.69 112	96.60 99	96.96 160	97.74 168	93.05 199	99.37 180	98.56 76	88.75 238	95.83 155	99.01 131	96.01 23	98.56 171	96.92 122	97.20 148	99.25 163

TESTMET0.1,1		96.74 109	96.26 108	98.16 119	97.36 184	96.48 114	99.96 23	98.29 143	91.93 178	95.77 156	98.07 183	95.54 36	98.29 196	90.55 218	98.89 109	99.70 100

F-COLMAP		96.93 100	96.95 89	96.87 163	99.71 71	91.74 227	99.85 92	97.95 178	93.11 138	95.72 157	99.16 123	92.35 116	99.94 64	95.32 139	99.35 101	98.92 179

test-LLR		96.47 118	96.04 112	97.78 133	97.02 195	95.44 150	99.96 23	98.21 152	94.07 105	95.55 158	96.38 226	93.90 84	98.27 199	90.42 220	98.83 111	99.64 111

test-mter		96.39 122	95.93 123	97.78 133	97.02 195	95.44 150	99.96 23	98.21 152	91.81 183	95.55 158	96.38 226	95.17 44	98.27 199	90.42 220	98.83 111	99.64 111

IS-MVSNet		96.29 127	95.90 125	97.45 147	98.13 147	94.80 168	99.08 205	97.61 207	92.02 177	95.54 160	98.96 140	90.64 143	98.08 206	93.73 179	97.41 142	99.47 140

CHOSEN 1792x2688		96.81 104	96.53 102	97.64 141	98.91 112	93.07 197	99.65 142	99.80 3	95.64 60	95.39 161	98.86 151	84.35 198	99.90 71	96.98 119	99.16 105	99.95 68

CDS-MVSNet		96.34 123	96.07 111	97.13 157	97.37 183	94.96 163	99.53 160	97.91 183	91.55 188	95.37 162	98.32 178	95.05 49	97.13 246	93.80 175	95.75 176	99.30 159
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Effi-MVS+-dtu		94.53 166	95.30 139	92.22 271	97.77 165	82.54 304	99.59 152	97.06 255	94.92 75	95.29 163	95.37 255	85.81 184	97.89 217	94.80 149	97.07 150	96.23 211

CSCG		97.10 93	97.04 86	97.27 156	99.89 40	91.92 222	99.90 68	99.07 31	88.67 239	95.26 164	99.82 45	93.17 102	99.98 38	98.15 83	99.47 96	99.90 77

Vis-MVSNet (Re-imp)		96.32 124	95.98 116	97.35 154	97.93 155	94.82 167	99.47 168	98.15 162	91.83 181	95.09 165	99.11 124	91.37 131	97.47 229	93.47 182	97.43 139	99.74 95

TAMVS		95.85 135	95.58 132	96.65 171	97.07 191	93.50 190	99.17 199	97.82 192	91.39 196	95.02 166	98.01 184	92.20 119	97.30 235	93.75 178	95.83 173	99.14 171

XVG-OURS-SEG-HR		94.79 155	94.70 152	95.08 202	98.05 149	89.19 265	99.08 205	97.54 215	93.66 125	94.87 167	99.58 91	78.78 241	99.79 99	97.31 110	93.40 200	96.25 209

XVG-OURS		94.82 154	94.74 151	95.06 203	98.00 151	89.19 265	99.08 205	97.55 213	94.10 103	94.71 168	99.62 88	80.51 228	99.74 111	96.04 131	93.06 204	96.25 209

ab-mvs		94.69 159	93.42 175	98.51 102	98.07 148	96.26 122	96.49 296	98.68 55	90.31 214	94.54 169	97.00 209	76.30 257	99.71 115	95.98 132	93.38 201	99.56 127

TAPA-MVS	92.12 8	94.42 168	93.60 169	96.90 162	99.33 91	91.78 226	99.78 112	98.00 172	89.89 221	94.52 170	99.47 99	91.97 124	99.18 141	69.90 315	99.52 93	99.73 96
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		94.54 164	93.56 172	97.49 146	97.96 153	94.34 176	98.71 241	97.51 221	90.30 215	94.51 171	98.69 159	75.56 262	98.77 158	92.82 192	95.99 168	99.35 154

Fast-Effi-MVS+		95.02 152	94.19 158	97.52 145	97.88 157	94.55 173	99.97 16	97.08 254	88.85 237	94.47 172	97.96 186	84.59 195	98.41 182	89.84 227	97.10 149	99.59 119

DeepC-MVS	94.51 4	96.92 101	96.40 106	98.45 106	99.16 95	95.90 137	99.66 139	98.06 169	96.37 42	94.37 173	99.49 98	83.29 205	99.90 71	97.63 104	99.61 88	99.55 128
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPSCF		91.80 215	92.79 186	88.83 297	98.15 145	69.87 323	98.11 272	96.60 285	83.93 289	94.33 174	99.27 114	79.60 235	99.46 136	91.99 197	93.16 203	97.18 205

BH-RMVSNet		95.18 148	94.31 157	97.80 132	98.17 144	95.23 158	99.76 120	97.53 217	92.52 164	94.27 175	99.25 118	76.84 252	98.80 154	90.89 215	99.54 92	99.35 154

CVMVSNet		94.68 161	94.94 147	93.89 246	96.80 205	86.92 286	99.06 210	98.98 34	94.45 89	94.23 176	99.02 129	85.60 186	95.31 302	90.91 214	95.39 182	99.43 146

baseline1		95.78 137	94.86 148	98.54 99	98.47 129	98.07 59	99.06 210	97.99 173	92.68 154	94.13 177	98.62 164	93.28 99	98.69 165	93.79 176	85.76 246	98.84 184

Anonymous202405211		93.10 190	91.99 200	96.40 176	99.10 97	89.65 262	98.88 229	97.93 180	83.71 291	94.00 178	98.75 157	68.79 291	99.88 81	95.08 142	91.71 206	99.68 103

cascas		94.64 162	93.61 167	97.74 138	97.82 162	96.26 122	99.96 23	97.78 194	85.76 274	94.00 178	97.54 191	76.95 251	99.21 140	97.23 112	95.43 181	97.76 202

Anonymous20240529		92.10 208	90.65 217	96.47 172	98.82 116	90.61 246	98.72 240	98.67 58	75.54 315	93.90 180	98.58 167	66.23 301	99.90 71	94.70 154	90.67 207	98.90 182

MVS_0304		89.28 263	88.31 261	92.21 272	97.05 193	86.53 287	97.76 281	99.57 12	85.58 279	93.86 181	92.71 302	51.04 325	96.30 281	84.49 271	92.72 205	93.79 272

LS3D		95.84 136	95.11 145	98.02 127	99.85 47	95.10 161	98.74 238	98.50 97	87.22 257	93.66 182	99.86 22	87.45 170	99.95 56	90.94 213	99.81 74	99.02 177

HQP-NCC					95.78 225		99.87 79		96.82 25	93.37 183

ACMP_Plane					95.78 225		99.87 79		96.82 25	93.37 183

HQP4-MVS										93.37 183			98.39 186			94.53 215

HQP-MVS		94.61 163	94.50 154	94.92 208	95.78 225	91.85 223	99.87 79	97.89 184	96.82 25	93.37 183	98.65 161	80.65 226	98.39 186	97.92 96	89.60 208	94.53 215

HQP_MVS		94.49 167	94.36 156	94.87 209	95.71 234	91.74 227	99.84 96	97.87 186	96.38 39	93.01 187	98.59 165	80.47 230	98.37 191	97.79 99	89.55 211	94.52 217

plane_prior3						91.64 233			96.63 33	93.01 187

GA-MVS		93.83 175	92.84 184	96.80 164	95.73 231	93.57 189	99.88 76	97.24 242	92.57 162	92.92 189	96.66 219	78.73 242	97.67 223	87.75 244	94.06 195	99.17 167

tpm cat1		93.51 184	92.52 192	96.47 172	97.77 165	91.47 237	96.13 300	98.06 169	80.98 302	92.91 190	93.78 294	89.66 150	98.87 151	87.03 254	96.39 161	99.09 175

1112_ss		96.01 133	95.20 142	98.42 109	97.80 163	96.41 117	99.65 142	96.66 283	92.71 150	92.88 191	99.40 105	92.16 120	99.30 138	91.92 198	93.66 197	99.55 128

Test_1112_low_res		95.72 138	94.83 149	98.42 109	97.79 164	96.41 117	99.65 142	96.65 284	92.70 151	92.86 192	96.13 234	92.15 121	99.30 138	91.88 199	93.64 198	99.55 128

IB-MVS	92.85 6	94.99 153	93.94 162	98.16 119	97.72 172	95.69 147	99.99 4	98.81 47	94.28 98	92.70 193	96.90 211	95.08 47	99.17 142	96.07 130	73.88 312	99.60 117
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		93.72 181	93.86 165	93.29 256	97.06 192	86.16 288	99.80 109	96.83 276	92.66 155	92.58 194	97.83 188	81.39 216	97.67 223	89.75 228	96.87 155	96.05 213

tpmvs		94.28 171	93.57 171	96.40 176	98.55 124	91.50 236	95.70 307	98.55 80	87.47 252	92.15 195	94.26 289	91.42 129	98.95 149	88.15 240	95.85 172	98.76 188

BH-w/o		95.71 140	95.38 137	96.68 169	98.49 128	92.28 213	99.84 96	97.50 222	92.12 173	92.06 196	98.79 156	84.69 194	98.67 166	95.29 140	99.66 83	99.09 175

VPA-MVSNet		92.70 198	91.55 205	96.16 182	95.09 244	96.20 127	98.88 229	99.00 33	91.02 203	91.82 197	95.29 261	76.05 261	97.96 213	95.62 138	81.19 272	94.30 234

baseline2		96.71 111	96.49 103	97.37 152	95.63 238	95.96 136	99.74 124	98.88 42	92.94 140	91.61 198	98.97 138	97.72 5	98.62 169	94.83 148	98.08 130	97.53 204

OPM-MVS		93.21 188	92.80 185	94.44 225	93.12 277	90.85 243	99.77 115	97.61 207	96.19 46	91.56 199	98.65 161	75.16 267	98.47 175	93.78 177	89.39 214	93.99 257

EI-MVSNet		93.73 180	93.40 178	94.74 212	96.80 205	92.69 204	99.06 210	97.67 199	88.96 233	91.39 200	99.02 129	88.75 160	97.30 235	91.07 208	87.85 233	94.22 239

MVSTER		95.53 143	95.22 141	96.45 174	98.56 123	97.72 71	99.91 64	97.67 199	92.38 168	91.39 200	97.14 201	97.24 13	97.30 235	94.80 149	87.85 233	94.34 232

BH-untuned		95.18 148	94.83 149	96.22 181	98.36 132	91.22 238	99.80 109	97.32 238	90.91 204	91.08 202	98.67 160	83.51 202	98.54 173	94.23 165	99.61 88	98.92 179

CLD-MVS		94.06 173	93.90 163	94.55 221	96.02 220	90.69 244	99.98 8	97.72 196	96.62 34	91.05 203	98.85 154	77.21 248	98.47 175	98.11 85	89.51 213	94.48 219
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS		96.60 115	95.56 133	99.72 7	96.85 202	99.22 11	98.31 262	98.94 36	91.57 187	90.90 204	99.61 89	86.66 178	99.96 49	97.36 109	99.88 63	99.99 15

MSDG		94.37 169	93.36 179	97.40 150	98.88 115	93.95 183	99.37 180	97.38 233	85.75 276	90.80 205	99.17 122	84.11 200	99.88 81	86.35 259	98.43 119	98.36 191

VPNet		91.81 212	90.46 220	95.85 190	94.74 250	95.54 149	98.98 219	98.59 71	92.14 172	90.77 206	97.44 193	68.73 293	97.54 227	94.89 147	77.89 296	94.46 220

MIMVSNet		90.30 245	88.67 256	95.17 201	96.45 213	91.64 233	92.39 317	97.15 250	85.99 271	90.50 207	93.19 300	66.95 299	94.86 308	82.01 287	93.43 199	99.01 178

mvs_anonymous		95.65 142	95.03 146	97.53 144	98.19 142	95.74 143	99.33 184	97.49 223	90.87 205	90.47 208	97.10 203	88.23 164	97.16 243	95.92 133	97.66 136	99.68 103

Patchmatch-test		92.65 201	91.50 206	96.10 184	96.85 202	90.49 249	91.50 321	97.19 244	82.76 296	90.23 209	95.59 245	95.02 50	98.00 210	77.41 303	96.98 153	99.82 85

LPG-MVS_test		92.96 192	92.71 187	93.71 249	95.43 240	88.67 271	99.75 121	97.62 204	92.81 144	90.05 210	98.49 171	75.24 265	98.40 184	95.84 136	89.12 215	94.07 249

LGP-MVS_train				93.71 249	95.43 240	88.67 271		97.62 204	92.81 144	90.05 210	98.49 171	75.24 265	98.40 184	95.84 136	89.12 215	94.07 249

DP-MVS		94.54 164	93.42 175	97.91 131	99.46 87	94.04 181	98.93 225	97.48 224	81.15 301	90.04 212	99.55 93	87.02 175	99.95 56	88.97 233	98.11 126	99.73 96

test_djsdf		92.83 195	92.29 195	94.47 224	91.90 294	92.46 210	99.55 157	97.27 240	91.17 197	89.96 213	96.07 236	81.10 219	96.89 261	94.67 155	88.91 217	94.05 251

ACMM	91.95 10	92.88 194	92.52 192	93.98 243	95.75 230	89.08 268	99.77 115	97.52 219	93.00 139	89.95 214	97.99 185	76.17 259	98.46 178	93.63 181	88.87 219	94.39 227
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
1314		96.84 103	95.96 121	99.48 28	96.74 209	98.52 46	98.31 262	98.86 44	95.82 51	89.91 215	98.98 136	87.49 169	99.96 49	97.80 98	99.73 78	99.96 62

XVG-ACMP-BASELINE		91.22 225	90.75 215	92.63 268	93.73 266	85.61 291	98.52 254	97.44 226	92.77 148	89.90 216	96.85 215	66.64 300	98.39 186	92.29 195	88.61 224	93.89 265

nrg030		93.51 184	92.53 191	96.45 174	94.36 255	97.20 93	99.81 105	97.16 249	91.60 186	89.86 217	97.46 192	86.37 181	97.68 222	95.88 134	80.31 282	94.46 220

V42		91.28 223	90.12 231	94.74 212	93.42 272	93.46 191	99.68 136	97.02 258	87.36 254	89.85 218	95.05 266	81.31 218	97.34 233	87.34 249	80.07 284	93.40 284

v144192		90.79 233	89.52 240	94.59 218	93.11 278	92.77 201	99.56 155	96.99 262	86.38 267	89.82 219	94.95 273	80.50 229	97.10 249	83.98 275	80.41 280	93.90 264

GBi-Net		90.88 230	89.82 234	94.08 236	97.53 178	91.97 218	98.43 257	96.95 266	87.05 258	89.68 220	94.72 276	71.34 282	96.11 286	87.01 255	85.65 247	94.17 241

test1		90.88 230	89.82 234	94.08 236	97.53 178	91.97 218	98.43 257	96.95 266	87.05 258	89.68 220	94.72 276	71.34 282	96.11 286	87.01 255	85.65 247	94.17 241

FMVSNet3		92.69 199	91.58 204	95.99 185	98.29 134	97.42 88	99.26 193	97.62 204	89.80 222	89.68 220	95.32 257	81.62 215	96.27 282	87.01 255	85.65 247	94.29 235

IterMVS-LS		92.69 199	92.11 197	94.43 227	96.80 205	92.74 202	99.45 171	96.89 272	88.98 231	89.65 223	95.38 254	88.77 159	96.34 278	90.98 212	82.04 267	94.22 239
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1144		91.09 226	89.83 233	94.87 209	93.25 274	93.69 188	99.62 150	96.98 264	86.83 262	89.64 224	94.99 271	80.94 221	97.05 252	85.08 268	81.16 273	93.87 267

v1921920		90.46 240	89.12 247	94.50 222	92.96 282	92.46 210	99.49 165	96.98 264	86.10 270	89.61 225	95.30 258	78.55 244	97.03 255	82.17 286	80.89 279	94.01 254

v1192		90.62 238	89.25 245	94.72 214	93.13 275	93.07 197	99.50 164	97.02 258	86.33 268	89.56 226	95.01 268	79.22 237	97.09 251	82.34 285	81.16 273	94.01 254

PCF-MVS	94.20 5	95.18 148	94.10 160	98.43 108	98.55 124	95.99 135	97.91 278	97.31 239	90.35 213	89.48 227	99.22 120	85.19 191	99.89 75	90.40 222	98.47 118	99.41 148
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
3Dnovator	91.47 12	96.28 128	95.34 138	99.08 63	96.82 204	97.47 85	99.45 171	98.81 47	95.52 63	89.39 228	99.00 133	81.97 210	99.95 56	97.27 111	99.83 68	99.84 83

v1240		90.20 248	88.79 254	94.44 225	93.05 280	92.27 214	99.38 178	96.92 270	85.89 272	89.36 229	94.87 275	77.89 247	97.03 255	80.66 293	81.08 275	94.01 254

FIs		94.10 172	93.43 174	96.11 183	94.70 251	96.82 106	99.58 153	98.93 39	92.54 163	89.34 230	97.31 197	87.62 168	97.10 249	94.22 166	86.58 242	94.40 226

ITE_SJBPF				92.38 269	95.69 236	85.14 295		95.71 301	92.81 144	89.33 231	98.11 181	70.23 288	98.42 181	85.91 263	88.16 231	93.59 281

v2v482		91.30 221	90.07 232	95.01 204	93.13 275	93.79 185	99.77 115	97.02 258	88.05 247	89.25 232	95.37 255	80.73 224	97.15 244	87.28 250	80.04 285	94.09 248

UniMVSNet (Re)		93.07 191	92.13 196	95.88 188	94.84 248	96.24 126	99.88 76	98.98 34	92.49 166	89.25 232	95.40 251	87.09 174	97.14 245	93.13 189	78.16 294	94.26 236

UniMVSNet_NR-MVSNet		92.95 193	92.11 197	95.49 193	94.61 253	95.28 156	99.83 102	99.08 30	91.49 189	89.21 234	96.86 214	87.14 173	96.73 268	93.20 185	77.52 299	94.46 220

DU-MVS		92.46 204	91.45 208	95.49 193	94.05 260	95.28 156	99.81 105	98.74 51	92.25 171	89.21 234	96.64 221	81.66 213	96.73 268	93.20 185	77.52 299	94.46 220

Baseline_NR-MVSNet		90.33 244	89.51 241	92.81 266	92.84 283	89.95 258	99.77 115	93.94 322	84.69 286	89.04 236	95.66 243	81.66 213	96.52 273	90.99 211	76.98 304	91.97 303

FC-MVSNet-test		93.81 177	93.15 182	95.80 191	94.30 257	96.20 127	99.42 173	98.89 41	92.33 169	89.03 237	97.27 199	87.39 171	96.83 265	93.20 185	86.48 243	94.36 228

QAPM		95.40 146	94.17 159	99.10 61	96.92 198	97.71 72	99.40 174	98.68 55	89.31 225	88.94 238	98.89 145	82.48 208	99.96 49	93.12 190	99.83 68	99.62 113

AllTest		92.48 202	91.64 203	95.00 205	99.01 100	88.43 275	98.94 224	96.82 278	86.50 265	88.71 239	98.47 175	74.73 269	99.88 81	85.39 265	96.18 163	96.71 207

TestCases				95.00 205	99.01 100	88.43 275		96.82 278	86.50 265	88.71 239	98.47 175	74.73 269	99.88 81	85.39 265	96.18 163	96.71 207

pmmvs4		92.10 208	91.07 213	95.18 200	92.82 284	94.96 163	99.48 167	96.83 276	87.45 253	88.66 241	96.56 224	83.78 201	96.83 265	89.29 230	84.77 256	93.75 274

PS-MVSNAJss		93.64 183	93.31 180	94.61 217	92.11 291	92.19 215	99.12 201	97.38 233	92.51 165	88.45 242	96.99 210	91.20 133	97.29 238	94.36 160	87.71 235	94.36 228

UniMVSNet_ETH3D		90.06 252	88.58 257	94.49 223	94.67 252	88.09 280	97.81 280	97.57 212	83.91 290	88.44 243	97.41 194	57.44 319	97.62 225	91.41 203	88.59 226	97.77 201

TranMVSNet+NR-MVSNet		91.68 219	90.61 218	94.87 209	93.69 267	93.98 182	99.69 134	98.65 59	91.03 202	88.44 243	96.83 218	80.05 233	96.18 285	90.26 224	76.89 306	94.45 225

FMVSNet2		91.02 227	89.56 238	95.41 196	97.53 178	95.74 143	98.98 219	97.41 231	87.05 258	88.43 245	95.00 270	71.34 282	96.24 284	85.12 267	85.21 252	94.25 238

COLMAP_ROB	90.47 14	92.18 207	91.49 207	94.25 231	99.00 102	88.04 281	98.42 260	96.70 282	82.30 298	88.43 245	99.01 131	76.97 250	99.85 86	86.11 262	96.50 160	94.86 214
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	96.31 125	95.24 140	99.52 22	96.88 201	98.64 42	99.72 131	98.24 149	95.27 70	88.42 247	98.98 136	82.76 207	99.94 64	97.10 116	99.83 68	99.96 62

v148		90.70 234	89.63 236	93.92 244	92.97 281	90.97 240	99.75 121	96.89 272	87.51 251	88.27 248	95.01 268	81.67 212	97.04 253	87.40 248	77.17 303	93.75 274

DSMNet-mixed		88.28 269	88.24 263	88.42 300	89.64 312	75.38 320	98.06 274	89.86 329	85.59 278	88.20 249	92.14 306	76.15 260	91.95 320	78.46 299	96.05 166	97.92 197

WR-MVS		92.31 205	91.25 210	95.48 195	94.45 254	95.29 155	99.60 151	98.68 55	90.10 216	88.07 250	96.89 212	80.68 225	96.80 267	93.14 188	79.67 286	94.36 228

test0.0.03 1		93.86 174	93.61 167	94.64 216	95.02 247	92.18 216	99.93 57	98.58 72	94.07 105	87.96 251	98.50 170	93.90 84	94.96 306	81.33 290	93.17 202	96.78 206

XXY-MVS		91.82 211	90.46 220	95.88 188	93.91 263	95.40 153	98.87 232	97.69 198	88.63 241	87.87 252	97.08 204	74.38 272	97.89 217	91.66 201	84.07 259	94.35 231

Patchmtry		89.70 256	88.49 258	93.33 255	96.24 216	89.94 260	91.37 322	96.23 292	78.22 308	87.69 253	93.31 298	91.04 138	96.03 291	80.18 296	82.10 266	94.02 252

D2MVS		92.76 196	92.59 190	93.27 257	95.13 243	89.54 264	99.69 134	99.38 21	92.26 170	87.59 254	94.61 282	85.05 193	97.79 219	91.59 202	88.01 232	92.47 298

v8		90.54 239	89.17 246	94.66 215	93.43 271	93.40 194	99.20 196	96.94 269	85.76 274	87.56 255	94.51 283	81.96 211	97.19 241	84.94 269	78.25 293	93.38 286

miper_lstm_enhance		91.81 212	91.39 209	93.06 263	97.34 185	89.18 267	99.38 178	96.79 280	86.70 263	87.47 256	95.22 263	90.00 148	95.86 296	88.26 238	81.37 271	94.15 244

anonymousdsp		91.79 217	90.92 214	94.41 228	90.76 306	92.93 200	98.93 225	97.17 247	89.08 227	87.46 257	95.30 258	78.43 246	96.92 260	92.38 194	88.73 222	93.39 285

jajsoiax		91.92 210	91.18 211	94.15 233	91.35 301	90.95 241	99.00 218	97.42 229	92.61 158	87.38 258	97.08 204	72.46 278	97.36 231	94.53 158	88.77 221	94.13 246

mvs_tets		91.81 212	91.08 212	94.00 241	91.63 299	90.58 247	98.67 245	97.43 227	92.43 167	87.37 259	97.05 207	71.76 280	97.32 234	94.75 152	88.68 223	94.11 247

v10		90.25 247	88.82 253	94.57 220	93.53 269	93.43 192	99.08 205	96.87 274	85.00 283	87.34 260	94.51 283	80.93 222	97.02 257	82.85 282	79.23 287	93.26 288

pmmvs5		90.17 250	89.09 248	93.40 254	92.10 292	89.77 261	99.74 124	95.58 305	85.88 273	87.24 261	95.74 240	73.41 276	96.48 274	88.54 235	83.56 262	93.95 260

ACMP	92.05 9	92.74 197	92.42 194	93.73 247	95.91 224	88.72 270	99.81 105	97.53 217	94.13 101	87.00 262	98.23 179	74.07 273	98.47 175	96.22 129	88.86 220	93.99 257
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVS-HIRNet		86.22 275	83.19 285	95.31 197	96.71 211	90.29 253	92.12 318	97.33 237	62.85 323	86.82 263	70.37 326	69.37 290	97.49 228	75.12 310	97.99 132	98.15 194

Anonymous20231211		89.86 254	88.44 259	94.13 235	98.93 108	90.68 245	98.54 252	98.26 148	76.28 311	86.73 264	95.54 247	70.60 286	97.56 226	90.82 216	80.27 283	94.15 244

v7n		89.65 257	88.29 262	93.72 248	92.22 290	90.56 248	99.07 209	97.10 252	85.42 282	86.73 264	94.72 276	80.06 232	97.13 246	81.14 291	78.12 295	93.49 282

IterMVS-SCA-FT		90.85 232	90.16 230	92.93 264	96.72 210	89.96 257	98.89 227	96.99 262	88.95 234	86.63 266	95.67 242	76.48 255	95.00 305	87.04 253	84.04 261	93.84 269

EU-MVSNet		90.14 251	90.34 224	89.54 294	92.55 287	81.06 313	98.69 243	98.04 171	91.41 195	86.59 267	96.84 217	80.83 223	93.31 319	86.20 260	81.91 268	94.26 236

OpenMVS	90.15 15	94.77 157	93.59 170	98.33 113	96.07 218	97.48 84	99.56 155	98.57 74	90.46 211	86.51 268	98.95 142	78.57 243	99.94 64	93.86 170	99.74 77	97.57 203

IterMVS		90.91 229	90.17 229	93.12 260	96.78 208	90.42 252	98.89 227	97.05 257	89.03 229	86.49 269	95.42 250	76.59 254	95.02 304	87.22 251	84.09 258	93.93 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WR-MVS_H		91.30 221	90.35 223	94.15 233	94.17 259	92.62 208	99.17 199	98.94 36	88.87 236	86.48 270	94.46 287	84.36 197	96.61 271	88.19 239	78.51 292	93.21 290

MS-PatchMatch		90.65 235	90.30 225	91.71 278	94.22 258	85.50 293	98.24 266	97.70 197	88.67 239	86.42 271	96.37 228	67.82 297	98.03 209	83.62 278	99.62 85	91.60 306

CP-MVSNet		91.23 224	90.22 227	94.26 230	93.96 262	92.39 212	99.09 203	98.57 74	88.95 234	86.42 271	96.57 223	79.19 238	96.37 276	90.29 223	78.95 289	94.02 252

LF4IMVS		89.25 264	88.85 252	90.45 288	92.81 285	81.19 312	98.12 271	94.79 315	91.44 192	86.29 273	97.11 202	65.30 305	98.11 205	88.53 236	85.25 251	92.07 300

PVSNet_0	88.03 19	91.80 215	90.27 226	96.38 178	98.27 137	90.46 250	99.94 52	99.61 11	93.99 110	86.26 274	97.39 196	71.13 285	99.89 75	98.77 61	67.05 321	98.79 187

PS-CasMVS		90.63 237	89.51 241	93.99 242	93.83 264	91.70 231	98.98 219	98.52 87	88.48 242	86.15 275	96.53 225	75.46 263	96.31 280	88.83 234	78.86 291	93.95 260

FMVSNet1		88.50 267	86.64 272	94.08 236	95.62 239	91.97 218	98.43 257	96.95 266	83.00 294	86.08 276	94.72 276	59.09 317	96.11 286	81.82 289	84.07 259	94.17 241

PEN-MVS		90.19 249	89.06 249	93.57 252	93.06 279	90.90 242	99.06 210	98.47 99	88.11 246	85.91 277	96.30 229	76.67 253	95.94 295	87.07 252	76.91 305	93.89 265

ppachtmachnet_test		89.58 258	88.35 260	93.25 258	92.40 288	90.44 251	99.33 184	96.73 281	85.49 280	85.90 278	95.77 239	81.09 220	96.00 294	76.00 309	82.49 265	93.30 287

OurMVSNet-221017-0		89.81 255	89.48 243	90.83 284	91.64 298	81.21 311	98.17 270	95.38 308	91.48 190	85.65 279	97.31 197	72.66 277	97.29 238	88.15 240	84.83 255	93.97 259

our_test_3		90.39 241	89.48 243	93.12 260	92.40 288	89.57 263	99.33 184	96.35 291	87.84 249	85.30 280	94.99 271	84.14 199	96.09 289	80.38 294	84.56 257	93.71 279

testgi		89.01 265	88.04 265	91.90 276	93.49 270	84.89 297	99.73 129	95.66 303	93.89 118	85.14 281	98.17 180	59.68 316	94.66 310	77.73 302	88.88 218	96.16 212

DTE-MVSNet		89.40 259	88.24 263	92.88 265	92.66 286	89.95 258	99.10 202	98.22 151	87.29 255	85.12 282	96.22 231	76.27 258	95.30 303	83.56 279	75.74 309	93.41 283

FMVSNet5		88.32 268	87.47 269	90.88 282	96.90 200	88.39 277	97.28 287	95.68 302	82.60 297	84.67 283	92.40 305	79.83 234	91.16 321	76.39 308	81.51 270	93.09 291

tfpnnormal		89.29 262	87.61 268	94.34 229	94.35 256	94.13 180	98.95 223	98.94 36	83.94 288	84.47 284	95.51 248	74.84 268	97.39 230	77.05 306	80.41 280	91.48 307

MVP-Stereo		90.93 228	90.45 222	92.37 270	91.25 303	88.76 269	98.05 275	96.17 294	87.27 256	84.04 285	95.30 258	78.46 245	97.27 240	83.78 277	99.70 81	91.09 308
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LTVRE_ROB	88.28 18	90.29 246	89.05 250	94.02 239	95.08 245	90.15 256	97.19 288	97.43 227	84.91 284	83.99 286	97.06 206	74.00 274	98.28 198	84.08 273	87.71 235	93.62 280
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		89.36 261	87.81 267	94.01 240	93.40 273	91.93 221	98.62 249	96.48 290	86.25 269	83.86 287	96.14 233	73.68 275	97.04 253	86.16 261	75.73 310	93.04 293

USDC		90.00 253	88.96 251	93.10 262	94.81 249	88.16 279	98.71 241	95.54 306	93.66 125	83.75 288	97.20 200	65.58 303	98.31 195	83.96 276	87.49 239	92.85 295

ACMH+	89.98 16	90.35 243	89.54 239	92.78 267	95.99 221	86.12 289	98.81 235	97.18 246	89.38 224	83.14 289	97.76 189	68.42 295	98.43 180	89.11 232	86.05 245	93.78 273

Anonymous20231206		86.32 274	85.42 275	89.02 296	89.11 314	80.53 316	99.05 214	95.28 309	85.43 281	82.82 290	93.92 291	74.40 271	93.44 318	66.99 319	81.83 269	93.08 292

SixPastTwentyTwo		88.73 266	88.01 266	90.88 282	91.85 296	82.24 306	98.22 268	95.18 313	88.97 232	82.26 291	96.89 212	71.75 281	96.67 270	84.00 274	82.98 263	93.72 278

TinyColmap		87.87 271	86.51 273	91.94 275	95.05 246	85.57 292	97.65 282	94.08 320	84.40 287	81.82 292	96.85 215	62.14 312	98.33 193	80.25 295	86.37 244	91.91 304

ACMH	89.72 17	90.64 236	89.63 236	93.66 251	95.64 237	88.64 273	98.55 250	97.45 225	89.03 229	81.62 293	97.61 190	69.75 289	98.41 182	89.37 229	87.62 237	93.92 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		85.69 276	83.84 281	91.26 281	90.00 311	84.41 299	97.82 279	96.15 295	75.86 313	81.29 294	95.39 253	61.21 314	96.87 263	83.52 280	73.29 313	92.50 297

TransMVSNet (Re)		87.25 272	85.28 276	93.16 259	93.56 268	91.03 239	98.54 252	94.05 321	83.69 292	81.09 295	96.16 232	75.32 264	96.40 275	76.69 307	68.41 318	92.06 301

NR-MVSNet		91.56 220	90.22 227	95.60 192	94.05 260	95.76 142	98.25 265	98.70 53	91.16 199	80.78 296	96.64 221	83.23 206	96.57 272	91.41 203	77.73 298	94.46 220

LCM-MVSNet-Re		92.31 205	92.60 189	91.43 279	97.53 178	79.27 318	99.02 217	91.83 326	92.07 174	80.31 297	94.38 288	83.50 203	95.48 298	97.22 113	97.58 137	99.54 132

TDRefinement		84.76 283	82.56 287	91.38 280	74.58 327	84.80 298	97.36 286	94.56 318	84.73 285	80.21 298	96.12 235	63.56 309	98.39 186	87.92 242	63.97 322	90.95 311

N_pmnet		80.06 294	80.78 292	77.89 309	91.94 293	45.28 334	98.80 236	56.82 338	78.10 309	80.08 299	93.33 296	77.03 249	95.76 297	68.14 318	82.81 264	92.64 296

test_0402		85.58 277	83.94 280	90.50 286	93.81 265	85.04 296	98.55 250	95.20 312	76.01 312	79.72 300	95.13 264	64.15 308	96.26 283	66.04 322	86.88 241	90.21 315

test20.03		84.72 285	83.99 278	86.91 302	88.19 316	80.62 315	98.88 229	95.94 298	88.36 244	78.87 301	94.62 281	68.75 292	89.11 325	66.52 320	75.82 308	91.00 309

pmmvs3		80.27 293	77.77 296	87.76 301	80.32 324	82.43 305	98.23 267	91.97 325	72.74 320	78.75 302	87.97 313	57.30 320	90.99 322	70.31 314	62.37 324	89.87 316

MIMVSNet1		82.58 290	80.51 293	88.78 298	86.68 317	84.20 300	96.65 294	95.41 307	78.75 307	78.59 303	92.44 304	51.88 323	89.76 324	65.26 323	78.95 289	92.38 299

DeepMVS_CX				82.92 308	95.98 223	58.66 328		96.01 297	92.72 149	78.34 304	95.51 248	58.29 318	98.08 206	82.57 283	85.29 250	92.03 302

Patchmatch-RL test		86.90 273	85.98 274	89.67 293	84.45 319	75.59 319	89.71 324	92.43 324	86.89 261	77.83 305	90.94 309	94.22 73	93.63 316	87.75 244	69.61 315	99.79 88

lessismore_v0				90.53 285	90.58 307	80.90 314		95.80 300		77.01 306	95.84 237	66.15 302	96.95 258	83.03 281	75.05 311	93.74 277

K. test v3		88.05 270	87.24 270	90.47 287	91.82 297	82.23 307	98.96 222	97.42 229	89.05 228	76.93 307	95.60 244	68.49 294	95.42 299	85.87 264	81.01 277	93.75 274

ambc				83.23 307	77.17 326	62.61 325	87.38 326	94.55 319		76.72 308	86.65 317	30.16 330	96.36 277	84.85 270	69.86 314	90.73 312

PM-MVS		80.47 292	78.88 295	85.26 305	83.79 321	72.22 321	95.89 305	91.08 327	85.71 277	76.56 309	88.30 312	36.64 328	93.90 313	82.39 284	69.57 316	89.66 318

DI_MVS_plusplus_test		92.48 202	90.60 219	98.11 123	91.88 295	96.13 130	99.64 146	97.73 195	92.69 152	76.02 310	93.79 293	70.49 287	99.07 144	95.88 134	97.26 145	99.14 171

OpenMVS_ROB	79.82 20	83.77 289	81.68 290	90.03 291	88.30 315	82.82 302	98.46 255	95.22 311	73.92 319	76.00 311	91.29 308	55.00 321	96.94 259	68.40 317	88.51 228	90.34 313

UnsupCasMVSNet_eth		85.52 278	83.99 278	90.10 290	89.36 313	83.51 301	96.65 294	97.99 173	89.14 226	75.89 312	93.83 292	63.25 310	93.92 312	81.92 288	67.90 320	92.88 294

new_pmnet		84.49 286	82.92 286	89.21 295	90.03 310	82.60 303	96.89 293	95.62 304	80.59 303	75.77 313	89.17 311	65.04 306	94.79 309	72.12 312	81.02 276	90.23 314

EG-PatchMatch MVS		85.35 281	83.81 282	89.99 292	90.39 308	81.89 309	98.21 269	96.09 296	81.78 300	74.73 314	93.72 295	51.56 324	97.12 248	79.16 298	88.61 224	90.96 310

pmmvs-eth3d		84.03 288	81.97 288	90.20 289	84.15 320	87.09 285	98.10 273	94.73 317	83.05 293	74.10 315	87.77 314	65.56 304	94.01 311	81.08 292	69.24 317	89.49 319

new-patchmatchnet		81.19 291	79.34 294	86.76 303	82.86 322	80.36 317	97.92 277	95.27 310	82.09 299	72.02 316	86.87 316	62.81 311	90.74 323	71.10 313	63.08 323	89.19 321

ET-MVSNet_ETH3D		94.37 169	93.28 181	97.64 141	98.30 133	97.99 63	99.99 4	97.61 207	94.35 94	71.57 317	99.45 102	96.23 22	95.34 301	96.91 123	85.14 254	99.59 119

UnsupCasMVSNet_bld		79.97 295	77.03 297	88.78 298	85.62 318	81.98 308	93.66 313	97.35 235	75.51 316	70.79 318	83.05 321	48.70 326	94.91 307	78.31 300	60.29 325	89.46 320

CMPMVS	61.59 21	84.75 284	85.14 277	83.57 306	90.32 309	62.54 326	96.98 291	97.59 211	74.33 318	69.95 319	96.66 219	64.17 307	98.32 194	87.88 243	88.41 229	89.84 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testmvs		40.60 307	44.45 309	29.05 321	19.49 339	14.11 340	99.68 136	18.47 339	20.74 333	64.59 320	98.48 174	10.95 339	17.09 338	56.66 327	11.01 333	55.94 331

testing_2		85.10 282	81.72 289	95.22 199	82.25 323	94.16 178	97.54 283	97.01 261	88.15 245	62.23 321	86.43 318	44.43 327	97.18 242	92.28 196	85.20 253	94.31 233

LCM-MVSNet		67.77 298	64.73 301	76.87 310	62.95 333	56.25 330	89.37 325	93.74 323	44.53 327	61.99 322	80.74 322	20.42 336	86.53 327	69.37 316	59.50 326	87.84 322

PMMVS2		67.15 299	64.15 302	76.14 311	70.56 330	62.07 327	93.89 311	87.52 333	58.09 324	60.02 323	78.32 323	22.38 335	84.54 328	59.56 326	47.03 327	81.80 324

Gipuma		66.95 300	65.00 300	72.79 312	91.52 300	67.96 324	66.16 331	95.15 314	47.89 326	58.54 324	67.99 328	29.74 331	87.54 326	50.20 328	77.83 297	62.87 329
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		85.50 280	83.33 283	92.00 274	90.89 305	88.38 278	99.22 195	96.55 286	79.60 306	57.26 325	92.72 301	79.09 240	93.78 315	77.25 304	77.37 302	93.84 269

MDA-MVSNet_test_wron		85.51 279	83.32 284	92.10 273	90.96 304	88.58 274	99.20 196	96.52 287	79.70 305	57.12 326	92.69 303	79.11 239	93.86 314	77.10 305	77.46 301	93.86 268

MDA-MVSNet-bldmvs		84.09 287	81.52 291	91.81 277	91.32 302	88.00 282	98.67 245	95.92 299	80.22 304	55.60 327	93.32 297	68.29 296	93.60 317	73.76 311	76.61 307	93.82 271

FPMVS		68.72 297	68.72 299	68.71 314	65.95 331	44.27 336	95.97 304	94.74 316	51.13 325	53.26 328	90.50 310	25.11 334	83.00 329	60.80 325	80.97 278	78.87 325

test123		37.68 308	39.14 310	33.31 320	19.94 338	24.83 339	98.36 261	9.75 340	15.53 334	51.31 329	87.14 315	19.62 337	17.74 337	47.10 329	3.47 335	57.36 330

tmp_tt		65.23 301	62.94 303	72.13 313	44.90 336	50.03 332	81.05 328	89.42 332	38.45 328	48.51 330	99.90 15	54.09 322	78.70 331	91.84 200	18.26 332	87.64 323

test_normal		73.75 296	69.52 298	86.45 304	78.71 325	71.20 322	52.76 332	96.50 288	86.56 264	46.73 331	66.99 329	34.02 329	96.33 279	84.42 272	78.96 288	91.81 305

E-PMN		52.30 304	52.18 305	52.67 318	71.51 328	45.40 333	93.62 314	76.60 336	36.01 330	43.50 332	64.13 331	27.11 333	67.31 334	31.06 333	26.06 329	45.30 333

EMVS		51.44 306	51.22 307	52.11 319	70.71 329	44.97 335	94.04 310	75.66 337	35.34 332	42.40 333	61.56 334	28.93 332	65.87 335	27.64 334	24.73 330	45.49 332

MVE	53.74 22	51.54 305	47.86 308	62.60 316	59.56 334	50.93 331	79.41 329	77.69 335	35.69 331	36.27 334	61.76 333	5.79 342	69.63 332	37.97 332	36.61 328	67.24 327
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 302	52.24 304	67.66 315	49.27 335	56.82 329	83.94 327	82.02 334	70.47 321	33.28 335	64.54 330	17.23 338	69.16 333	45.59 330	23.85 331	77.02 326

PMVS	49.05 23	53.75 303	51.34 306	60.97 317	40.80 337	34.68 337	74.82 330	89.62 331	37.55 329	28.67 336	72.12 325	7.09 340	81.63 330	43.17 331	68.21 319	66.59 328
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		20.37 310	20.84 312	18.99 322	65.34 332	27.73 338	50.43 333	7.67 341	9.50 335	8.01 337	6.34 337	6.13 341	26.24 336	23.40 335	10.69 334	2.99 334

cdsmvs_eth3d_5k		23.43 309	31.24 311	0.00 323	0.00 340	0.00 341	0.00 334	98.09 167	0.00 336	0.00 338	99.67 82	83.37 204	0.00 339	0.00 336	0.00 336	0.00 335

pcd_1.5k_mvsjas		7.60 312	10.13 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	91.20 133	0.00 339	0.00 336	0.00 336	0.00 335

sosnet-low-res		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sosnet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uncertanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

Regformer		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

ab-mvs-re		8.28 311	11.04 313	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	99.40 105	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

save fliter					99.82 56	98.79 28	99.96 23	98.40 123	97.66 9

test_0728_SECOND				99.82 4	99.94 14	99.47 4	99.95 37	98.43 110					100.00 1	99.99 1	100.00 1	100.00 1

GSMVS																99.59 119

test_part1				0.00 323		0.00 341	0.00 334	98.41 122				0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sam_mvs1												94.72 59				99.59 119

sam_mvs												94.25 72

MTGPA								98.28 144

test_post1							95.78 306				59.23 335	93.20 101	97.74 221	91.06 209

test_post											63.35 332	94.43 62	98.13 204

patchmatchnet-post											91.70 307	95.12 45	97.95 214

MTMP							99.87 79	96.49 289

gm-plane-assit					96.97 197	93.76 187			91.47 191		98.96 140		98.79 155	94.92 144

test9_res														99.71 21	99.99 17	100.00 1

agg_prior2														99.48 27	100.00 1	100.00 1

test_prior4						98.05 60	99.94 52

test_prior				99.43 29	99.94 14	98.49 48		98.65 59					99.80 97			99.99 15

新几何2							99.40 174

旧先验1					99.76 61	97.52 79		98.64 62			99.85 26	95.63 35			99.94 49	99.99 15

无先验							99.49 165	98.71 52	93.46 129				100.00 1	94.36 160		99.99 15

原ACMM2							99.90 68

testdata2													99.99 33	90.54 219

segment_acmp												96.68 18

testdata1							99.28 191		96.35 43

plane_prior7					95.71 234	91.59 235

plane_prior6					95.76 229	91.72 230						80.47 230

plane_prior5								97.87 186					98.37 191	97.79 99	89.55 211	94.52 217

plane_prior4											98.59 165

plane_prior2							99.84 96		96.38 39

plane_prior1					95.73 231

plane_prior						91.74 227	99.86 90		96.76 29						89.59 210

n2								0.00 342

nn								0.00 342

door-mid								89.69 330

test11								98.44 103

door								90.31 328

HQP5-MVS						91.85 223

BP-MVS														97.92 96

HQP3-MVS								97.89 184							89.60 208

HQP2-MVS												80.65 226

NP-MVS					95.77 228	91.79 225					98.65 161

ACMMP++_ref															87.04 240

ACMMP++															88.23 230

Test By Simon												92.82 108

High-resolution multi-view benchmark