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.

SteuartSystems-ACMMP		97.62 3	97.53 2	97.87 12	98.39 57	94.25 20	98.43 16	98.27 24	95.34 9	98.11 5	98.56 7	94.53 1	99.71 27	96.57 16	99.62 5	99.65 3
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		97.68 2	97.44 5	98.37 2	98.90 30	95.86 2	97.27 97	98.08 48	95.81 3	97.87 9	98.31 31	94.26 2	99.68 35	97.02 4	99.49 21	99.57 11

SD-MVS		97.41 6	97.53 2	97.06 55	98.57 49	94.46 14	97.92 42	98.14 38	94.82 21	99.01 1	98.55 9	94.18 3	97.41 262	96.94 5	99.64 3	99.32 41

HSP-MVS		97.53 4	97.49 4	97.63 33	99.40 5	93.77 38	98.53 9	97.85 86	95.55 5	98.56 3	97.81 59	93.90 4	99.65 39	96.62 13	99.21 48	99.48 26

MCST-MVS		97.18 10	96.84 17	98.20 4	99.30 16	95.35 3	97.12 113	98.07 53	93.54 51	96.08 51	97.69 66	93.86 5	99.71 27	96.50 17	99.39 32	99.55 15

APDe-MVS		97.82 1	97.73 1	98.08 7	99.15 23	94.82 10	98.81 2	98.30 22	94.76 24	98.30 4	98.90 1	93.77 6	99.68 35	97.93 1	99.69 1	99.75 1

TSAR-MVS + MP.		97.42 5	97.33 6	97.69 27	99.25 18	94.24 21	98.07 34	97.85 86	93.72 45	98.57 2	98.35 22	93.69 7	99.40 85	97.06 3	99.46 23	99.44 30

DeepPCF-MVS	93.97 1	96.61 35	97.09 7	95.15 134	98.09 78	86.63 239	96.00 212	98.15 36	95.43 7	97.95 7	98.56 7	93.40 8	99.36 89	96.77 12	99.48 22	99.45 28

NCCC		97.30 8	97.03 9	98.11 6	98.77 33	95.06 8	97.34 91	98.04 62	95.96 2	97.09 25	97.88 52	93.18 9	99.71 27	95.84 35	99.17 51	99.56 13

segment_acmp												92.89 10

TSAR-MVS + GP.		96.69 32	96.49 31	97.27 46	98.31 65	93.39 46	96.79 143	96.72 194	94.17 36	97.44 13	97.66 69	92.76 11	99.33 90	96.86 8	97.76 93	99.08 60

TEST9					98.70 36	94.19 22	96.41 178	98.02 65	88.17 203	96.03 52	97.56 81	92.74 12	99.59 50

train_agg		96.30 43	95.83 46	97.72 24	98.70 36	94.19 22	96.41 178	98.02 65	88.58 188	96.03 52	97.56 81	92.73 13	99.59 50	95.04 51	99.37 37	99.39 34

test_8					98.67 38	94.06 28	96.37 185	98.01 67	88.58 188	95.98 57	97.55 83	92.73 13	99.58 53

agg_prior1		96.22 46	95.77 47	97.56 35	98.67 38	93.79 35	96.28 194	98.00 69	88.76 185	95.68 66	97.55 83	92.70 15	99.57 61	95.01 53	99.32 39	99.32 41

CSCG		96.05 49	95.91 45	96.46 77	99.24 19	90.47 128	98.30 21	98.57 11	89.01 172	93.97 95	97.57 79	92.62 16	99.76 21	94.66 64	99.27 43	99.15 53

Regformer-2		97.16 12	96.99 10	97.67 28	98.32 63	93.84 33	96.83 136	98.10 45	95.24 10	97.49 11	98.25 37	92.57 17	99.61 45	96.80 9	99.29 41	99.56 13

HPM-MVS++		97.34 7	96.97 11	98.47 1	99.08 25	96.16 1	97.55 75	97.97 76	95.59 4	96.61 33	97.89 50	92.57 17	99.84 12	95.95 32	99.51 17	99.40 33

PHI-MVS		96.77 29	96.46 33	97.71 26	98.40 55	94.07 27	98.21 28	98.45 15	89.86 147	97.11 24	98.01 46	92.52 19	99.69 33	96.03 31	99.53 14	99.36 39

Regformer-1		97.10 14	96.96 12	97.54 36	98.32 63	93.48 44	96.83 136	97.99 74	95.20 12	97.46 12	98.25 37	92.48 20	99.58 53	96.79 11	99.29 41	99.55 15

agg_prior3		96.16 47	95.67 48	97.62 34	98.67 38	93.88 31	96.41 178	98.00 69	87.93 207	95.81 62	97.47 85	92.33 21	99.59 50	95.04 51	99.37 37	99.39 34

APD-MVS		96.95 22	96.60 27	98.01 8	99.03 27	94.93 9	97.72 58	98.10 45	91.50 107	98.01 6	98.32 30	92.33 21	99.58 53	94.85 58	99.51 17	99.53 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		96.68 34	96.58 29	96.99 57	98.46 51	92.31 71	96.20 201	98.90 2	94.30 35	95.86 60	97.74 64	92.33 21	99.38 88	96.04 30	99.42 28	99.28 46

MSLP-MVS++		96.94 23	97.06 8	96.59 67	98.72 35	91.86 86	97.67 61	98.49 12	94.66 27	97.24 16	98.41 19	92.31 24	98.94 121	96.61 14	99.46 23	98.96 69

旧先验1					98.38 58	93.38 47		97.75 90			98.09 41	92.30 25			99.01 62	99.16 51

HFP-MVS		97.14 13	96.92 14	97.83 14	99.42 3	94.12 25	98.52 10	98.32 19	93.21 58	97.18 18	98.29 34	92.08 26	99.83 13	95.63 39	99.59 7	99.54 17

#test#		97.02 19	96.75 24	97.83 14	99.42 3	94.12 25	98.15 29	98.32 19	92.57 81	97.18 18	98.29 34	92.08 26	99.83 13	95.12 49	99.59 7	99.54 17

test_prior3		96.46 39	96.20 41	97.23 48	98.67 38	92.99 55	96.35 186	98.00 69	92.80 77	96.03 52	97.59 77	92.01 28	99.41 83	95.01 53	99.38 33	99.29 43

test_prior2							96.35 186		92.80 77	96.03 52	97.59 77	92.01 28		95.01 53	99.38 33

CDPH-MVS		95.97 52	95.38 54	97.77 21	98.93 29	94.44 15	96.35 186	97.88 81	86.98 230	96.65 32	97.89 50	91.99 30	99.47 76	92.26 95	99.46 23	99.39 34

CP-MVS		97.02 19	96.81 20	97.64 31	99.33 14	93.54 42	98.80 3	98.28 23	92.99 67	96.45 42	98.30 33	91.90 31	99.85 9	95.61 41	99.68 2	99.54 17

Regformer-4		96.97 21	96.87 15	97.25 47	98.34 60	92.66 64	96.96 123	98.01 67	95.12 13	97.14 21	98.42 16	91.82 32	99.61 45	96.90 6	99.13 54	99.50 22

Regformer-3		96.85 26	96.80 21	97.01 56	98.34 60	92.02 82	96.96 123	97.76 89	95.01 16	97.08 26	98.42 16	91.71 33	99.54 65	96.80 9	99.13 54	99.48 26

XVS		97.18 10	96.96 12	97.81 16	99.38 8	94.03 29	98.59 7	98.20 30	94.85 17	96.59 35	98.29 34	91.70 34	99.80 18	95.66 37	99.40 30	99.62 5

X-MVStestdata		91.71 167	89.67 220	97.81 16	99.38 8	94.03 29	98.59 7	98.20 30	94.85 17	96.59 35	32.69 336	91.70 34	99.80 18	95.66 37	99.40 30	99.62 5

ACMMP_Plus		97.20 9	96.86 16	98.23 3	99.09 24	95.16 6	97.60 71	98.19 32	92.82 76	97.93 8	98.74 3	91.60 36	99.86 6	96.26 20	99.52 15	99.67 2

region2R		97.07 16	96.84 17	97.77 21	99.46 1	93.79 35	98.52 10	98.24 27	93.19 61	97.14 21	98.34 25	91.59 37	99.87 5	95.46 44	99.59 7	99.64 4

DELS-MVS		96.61 35	96.38 36	97.30 43	97.79 93	93.19 51	95.96 213	98.18 34	95.23 11	95.87 59	97.65 70	91.45 38	99.70 32	95.87 33	99.44 27	99.00 67
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
ACMMPR		97.07 16	96.84 17	97.79 18	99.44 2	93.88 31	98.52 10	98.31 21	93.21 58	97.15 20	98.33 28	91.35 39	99.86 6	95.63 39	99.59 7	99.62 5

DeepC-MVS_fast	93.89 2	96.93 24	96.64 26	97.78 19	98.64 44	94.30 18	97.41 84	98.04 62	94.81 22	96.59 35	98.37 21	91.24 40	99.64 44	95.16 47	99.52 15	99.42 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		96.81 27	96.53 30	97.65 29	99.35 13	93.53 43	97.65 64	98.98 1	92.22 86	97.14 21	98.44 14	91.17 41	99.85 9	94.35 66	99.46 23	99.57 11

MP-MVS-pluss		96.70 31	96.27 38	97.98 9	99.23 21	94.71 11	96.96 123	98.06 55	90.67 129	95.55 72	98.78 2	91.07 42	99.86 6	96.58 15	99.55 12	99.38 37
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mPP-MVS		96.86 25	96.60 27	97.64 31	99.40 5	93.44 45	98.50 13	98.09 47	93.27 57	95.95 58	98.33 28	91.04 43	99.88 3	95.20 46	99.57 11	99.60 8

HPM-MVS		96.69 32	96.45 34	97.40 39	99.36 12	93.11 53	98.87 1	98.06 55	91.17 118	96.40 43	97.99 48	90.99 44	99.58 53	95.61 41	99.61 6	99.49 24

APD-MVS_3200maxsize		96.81 27	96.71 25	97.12 54	99.01 28	92.31 71	97.98 40	98.06 55	93.11 64	97.44 13	98.55 9	90.93 45	99.55 63	96.06 29	99.25 44	99.51 21

test12				97.65 29	98.46 51	94.26 19		97.66 101		95.52 74		90.89 46	99.46 77		99.25 44	99.22 48

MPTG		97.07 16	96.77 23	97.97 10	99.37 10	94.42 16	97.15 111	98.08 48	95.07 14	96.11 49	98.59 5	90.88 47	99.90 1	96.18 27	99.50 19	99.58 9

MTAPA		97.08 15	96.78 22	97.97 10	99.37 10	94.42 16	97.24 99	98.08 48	95.07 14	96.11 49	98.59 5	90.88 47	99.90 1	96.18 27	99.50 19	99.58 9

EI-MVSNet-Vis-set		96.51 37	96.47 32	96.63 64	98.24 69	91.20 105	96.89 132	97.73 92	94.74 25	96.49 39	98.49 11	90.88 47	99.58 53	96.44 18	98.32 78	99.13 55

MP-MVS		96.77 29	96.45 34	97.72 24	99.39 7	93.80 34	98.41 17	98.06 55	93.37 53	95.54 73	98.34 25	90.59 50	99.88 3	94.83 59	99.54 13	99.49 24

EI-MVSNet-UG-set		96.34 42	96.30 37	96.47 75	98.20 73	90.93 116	96.86 134	97.72 95	94.67 26	96.16 48	98.46 12	90.43 51	99.58 53	96.23 21	97.96 87	98.90 76

原ACMM1				96.38 80	98.59 46	91.09 111		97.89 80	87.41 219	95.22 76	97.68 67	90.25 52	99.54 65	87.95 172	99.12 57	98.49 102

1121		94.71 80	93.83 83	97.34 41	98.57 49	93.64 40	96.04 208	97.73 92	81.56 292	95.68 66	97.85 56	90.23 53	99.65 39	87.68 179	99.12 57	98.73 85

HPM-MVS_fast		96.51 37	96.27 38	97.22 50	99.32 15	92.74 61	98.74 4	98.06 55	90.57 138	96.77 28	98.35 22	90.21 54	99.53 68	94.80 61	99.63 4	99.38 37

testdata				95.46 123	98.18 76	88.90 175		97.66 101	82.73 281	97.03 27	98.07 42	90.06 55	98.85 129	89.67 138	98.98 63	98.64 91

新几何1				97.32 42	98.60 45	93.59 41		97.75 90	81.58 290	95.75 65	97.85 56	90.04 56	99.67 37	86.50 202	99.13 54	98.69 89

DP-MVS Recon		95.68 56	95.12 61	97.37 40	99.19 22	94.19 22	97.03 116	98.08 48	88.35 196	95.09 78	97.65 70	89.97 57	99.48 75	92.08 104	98.59 73	98.44 107

MVS_111021_LR		96.24 45	96.19 42	96.39 79	98.23 72	91.35 100	96.24 199	98.79 4	93.99 39	95.80 63	97.65 70	89.92 58	99.24 95	95.87 33	99.20 49	98.58 92

EPP-MVSNet		95.22 65	95.04 62	95.76 105	97.49 106	89.56 149	98.67 5	97.00 173	90.69 128	94.24 90	97.62 75	89.79 59	98.81 132	93.39 87	96.49 124	98.92 74

PAPR		94.18 87	93.42 100	96.48 74	97.64 99	91.42 99	95.55 231	97.71 98	88.99 173	92.34 127	95.82 154	89.19 60	99.11 106	86.14 207	97.38 102	98.90 76

MG-MVS		95.61 57	95.38 54	96.31 84	98.42 54	90.53 126	96.04 208	97.48 117	93.47 52	95.67 69	98.10 40	89.17 61	99.25 94	91.27 124	98.77 68	99.13 55

PAPM_NR		95.01 69	94.59 70	96.26 89	98.89 31	90.68 123	97.24 99	97.73 92	91.80 101	92.93 119	96.62 122	89.13 62	99.14 104	89.21 149	97.78 91	98.97 68

ACMMP		96.27 44	95.93 44	97.28 45	99.24 19	92.62 65	98.25 25	98.81 3	92.99 67	94.56 84	98.39 20	88.96 63	99.85 9	94.57 65	97.63 94	99.36 39
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
UA-Net		95.95 53	95.53 50	97.20 52	97.67 97	92.98 57	97.65 64	98.13 39	94.81 22	96.61 33	98.35 22	88.87 64	99.51 72	90.36 131	97.35 104	99.11 58

API-MVS		94.84 78	94.49 75	95.90 100	97.90 90	92.00 83	97.80 50	97.48 117	89.19 162	94.81 81	96.71 108	88.84 65	99.17 100	88.91 157	98.76 69	96.53 171

test222					98.24 69	92.21 74	95.33 240	97.60 106	79.22 303	95.25 75	97.84 58	88.80 66			99.15 52	98.72 86

Test By Simon												88.73 67

pcd_1.5k_mvsjas		7.39 320	9.85 321	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	88.65 68	0.00 343	0.00 340	0.00 341	0.00 339

PS-MVSNAJss		93.74 104	93.51 94	94.44 160	93.91 258	89.28 167	97.75 53	97.56 112	92.50 82	89.94 179	96.54 125	88.65 68	98.18 176	93.83 77	90.90 202	95.86 189

PS-MVSNAJ		95.37 60	95.33 56	95.49 119	97.35 107	90.66 124	95.31 242	97.48 117	93.85 42	96.51 38	95.70 165	88.65 68	99.65 39	94.80 61	98.27 79	96.17 178

xiu_mvs_v2_base		95.32 62	95.29 57	95.40 125	97.22 109	90.50 127	95.44 237	97.44 129	93.70 47	96.46 41	96.18 138	88.59 71	99.53 68	94.79 63	97.81 90	96.17 178

PLC	91.00 6	94.11 91	93.43 98	96.13 93	98.58 48	91.15 110	96.69 158	97.39 134	87.29 222	91.37 143	96.71 108	88.39 72	99.52 71	87.33 190	97.13 109	97.73 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
UniMVSNet_NR-MVSNet		93.37 115	92.67 118	95.47 122	95.34 184	92.83 59	97.17 109	98.58 10	92.98 72	90.13 171	95.80 155	88.37 73	97.85 229	91.71 113	83.93 272	95.73 202

PVSNet_BlendedMVS		94.06 93	93.92 81	94.47 159	98.27 66	89.46 156	96.73 148	98.36 16	90.17 142	94.36 87	95.24 187	88.02 74	99.58 53	93.44 84	90.72 205	94.36 269

PVSNet_Blended		94.87 77	94.56 71	95.81 103	98.27 66	89.46 156	95.47 236	98.36 16	88.84 179	94.36 87	96.09 144	88.02 74	99.58 53	93.44 84	98.18 81	98.40 110

TAPA-MVS	90.10 7	92.30 152	91.22 166	95.56 114	98.33 62	89.60 147	96.79 143	97.65 103	81.83 287	91.52 140	97.23 92	87.94 76	98.91 123	71.31 308	98.37 77	98.17 118
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
abl_6		96.40 40	96.21 40	96.98 58	98.89 31	92.20 76	97.89 43	98.03 64	93.34 56	97.22 17	98.42 16	87.93 77	99.72 26	95.10 50	99.07 59	99.02 62

MVS_Test		94.89 76	94.62 69	95.68 110	96.83 125	89.55 150	96.70 156	97.17 150	91.17 118	95.60 70	96.11 143	87.87 78	98.76 137	93.01 92	97.17 108	98.72 86

UniMVSNet (Re)		93.31 117	92.55 123	95.61 112	95.39 181	93.34 50	97.39 87	98.71 5	93.14 63	90.10 175	94.83 199	87.71 79	98.03 202	91.67 117	83.99 271	95.46 209

FC-MVSNet-test		93.94 98	93.57 90	95.04 138	95.48 178	91.45 98	98.12 30	98.71 5	93.37 53	90.23 166	96.70 110	87.66 80	97.85 229	91.49 119	90.39 210	95.83 193

canonicalmvs		96.02 51	95.45 51	97.75 23	97.59 103	95.15 7	98.28 22	97.60 106	94.52 29	96.27 45	96.12 141	87.65 81	99.18 99	96.20 26	94.82 147	98.91 75

FIs		94.09 92	93.70 86	95.27 127	95.70 172	92.03 81	98.10 31	98.68 7	93.36 55	90.39 163	96.70 110	87.63 82	97.94 219	92.25 97	90.50 209	95.84 192

CDS-MVSNet		94.14 90	93.54 92	95.93 99	96.18 154	91.46 97	96.33 189	97.04 169	88.97 175	93.56 98	96.51 126	87.55 83	97.89 227	89.80 135	95.95 131	98.44 107
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Effi-MVS+		94.93 74	94.45 77	96.36 82	96.61 130	91.47 96	96.41 178	97.41 133	91.02 123	94.50 85	95.92 148	87.53 84	98.78 134	93.89 74	96.81 115	98.84 82

PVSNet_Blended_VisFu		95.27 63	94.91 63	96.38 80	98.20 73	90.86 118	97.27 97	98.25 25	90.21 141	94.18 91	97.27 89	87.48 85	99.73 23	93.53 80	97.77 92	98.55 93

mvs_anonymous		93.82 101	93.74 85	94.06 173	96.44 144	85.41 251	95.81 220	97.05 166	89.85 149	90.09 176	96.36 133	87.44 86	97.75 239	93.97 70	96.69 120	99.02 62

CANet		96.39 41	96.02 43	97.50 37	97.62 100	93.38 47	97.02 118	97.96 77	95.42 8	94.86 80	97.81 59	87.38 87	99.82 16	96.88 7	99.20 49	99.29 43

TAMVS		94.01 96	93.46 96	95.64 111	96.16 156	90.45 129	96.71 153	96.89 187	89.27 160	93.46 102	96.92 102	87.29 88	97.94 219	88.70 163	95.74 135	98.53 95

nrg030		94.05 94	93.31 102	96.27 88	95.22 194	94.59 12	98.34 19	97.46 122	92.93 74	91.21 153	96.64 115	87.23 89	98.22 172	94.99 56	85.80 246	95.98 187

CPTT-MVS		95.57 58	95.19 59	96.70 61	99.27 17	91.48 95	98.33 20	98.11 43	87.79 210	95.17 77	98.03 44	87.09 90	99.61 45	93.51 81	99.42 28	99.02 62

OMC-MVS		95.09 68	94.70 68	96.25 90	98.46 51	91.28 101	96.43 176	97.57 109	92.04 96	94.77 82	97.96 49	87.01 91	99.09 114	91.31 123	96.77 116	98.36 114

DeepC-MVS	93.07 3	96.06 48	95.66 49	97.29 44	97.96 85	93.17 52	97.30 96	98.06 55	93.92 40	93.38 103	98.66 4	86.83 92	99.73 23	95.60 43	99.22 47	98.96 69
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IterMVS-LS		92.29 153	91.94 139	93.34 217	96.25 150	86.97 232	96.57 172	97.05 166	90.67 129	89.50 201	94.80 201	86.59 93	97.64 247	89.91 133	86.11 244	95.40 215
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		93.03 126	92.88 110	93.48 210	95.77 170	86.98 231	96.44 174	97.12 157	90.66 131	91.30 148	97.64 73	86.56 94	98.05 198	89.91 133	90.55 207	95.41 211

1112_ss		93.37 115	92.42 129	96.21 91	97.05 118	90.99 112	96.31 191	96.72 194	86.87 236	89.83 185	96.69 112	86.51 95	99.14 104	88.12 168	93.67 163	98.50 100

WTY-MVS		94.71 80	94.02 80	96.79 60	97.71 96	92.05 80	96.59 169	97.35 140	90.61 135	94.64 83	96.93 101	86.41 96	99.39 86	91.20 126	94.71 151	98.94 72

EPNet		95.20 66	94.56 71	97.14 53	92.80 290	92.68 63	97.85 47	94.87 279	96.64 1	92.46 122	97.80 61	86.23 97	99.65 39	93.72 78	98.62 72	99.10 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Fast-Effi-MVS+		93.46 112	92.75 114	95.59 113	96.77 127	90.03 131	96.81 140	97.13 156	88.19 201	91.30 148	94.27 229	86.21 98	98.63 144	87.66 181	96.46 126	98.12 120

MVSFormer		95.37 60	95.16 60	95.99 98	96.34 147	91.21 103	98.22 26	97.57 109	91.42 111	96.22 46	97.32 87	86.20 99	97.92 223	94.07 68	99.05 60	98.85 80

lupinMVS		94.99 73	94.56 71	96.29 87	96.34 147	91.21 103	95.83 219	96.27 213	88.93 177	96.22 46	96.88 103	86.20 99	98.85 129	95.27 45	99.05 60	98.82 83

114514_t		93.95 97	93.06 106	96.63 64	99.07 26	91.61 91	97.46 83	97.96 77	77.99 308	93.00 114	97.57 79	86.14 101	99.33 90	89.22 148	99.15 52	98.94 72

alignmvs		95.87 55	95.23 58	97.78 19	97.56 105	95.19 5	97.86 45	97.17 150	94.39 32	96.47 40	96.40 131	85.89 102	99.20 96	96.21 25	95.11 143	98.95 71

WR-MVS_H		92.00 163	91.35 158	93.95 181	95.09 201	89.47 154	98.04 35	98.68 7	91.46 109	88.34 222	94.68 205	85.86 103	97.56 251	85.77 215	84.24 269	94.82 252

Test_1112_low_res		92.84 135	91.84 141	95.85 102	97.04 119	89.97 137	95.53 233	96.64 202	85.38 251	89.65 195	95.18 188	85.86 103	99.10 111	87.70 177	93.58 168	98.49 102

HY-MVS	89.66 9	93.87 99	92.95 108	96.63 64	97.10 114	92.49 69	95.64 228	96.64 202	89.05 171	93.00 114	95.79 158	85.77 105	99.45 79	89.16 151	94.35 152	97.96 125

IS-MVSNet		94.90 75	94.52 74	96.05 95	97.67 97	90.56 125	98.44 15	96.22 217	93.21 58	93.99 93	97.74 64	85.55 106	98.45 159	89.98 132	97.86 88	99.14 54

MVS		91.71 167	90.44 190	95.51 117	95.20 196	91.59 93	96.04 208	97.45 126	73.44 320	87.36 242	95.60 169	85.42 107	99.10 111	85.97 212	97.46 97	95.83 193

VNet		95.89 54	95.45 51	97.21 51	98.07 79	92.94 58	97.50 78	98.15 36	93.87 41	97.52 10	97.61 76	85.29 108	99.53 68	95.81 36	95.27 141	99.16 51

CNLPA		94.28 85	93.53 93	96.52 69	98.38 58	92.55 67	96.59 169	96.88 188	90.13 143	91.91 135	97.24 91	85.21 109	99.09 114	87.64 182	97.83 89	97.92 127

F-COLMAP		93.58 109	92.98 107	95.37 126	98.40 55	88.98 173	97.18 108	97.29 144	87.75 212	90.49 160	97.10 98	85.21 109	99.50 74	86.70 199	96.72 119	97.63 139

LCM-MVSNet-Re		92.50 144	92.52 126	92.44 241	96.82 126	81.89 281	96.92 130	93.71 304	92.41 84	84.30 268	94.60 208	85.08 111	97.03 275	91.51 118	97.36 103	98.40 110

NR-MVSNet		92.34 149	91.27 163	95.53 116	94.95 207	93.05 54	97.39 87	98.07 53	92.65 80	84.46 266	95.71 163	85.00 112	97.77 238	89.71 137	83.52 279	95.78 196

PAPM		91.52 182	90.30 194	95.20 128	95.30 188	89.83 141	93.38 280	96.85 190	86.26 243	88.59 219	95.80 155	84.88 113	98.15 178	75.67 297	95.93 132	97.63 139

diffmvs		93.43 114	92.75 114	95.48 121	96.47 142	89.61 146	96.09 205	97.14 154	85.97 247	93.09 112	95.35 182	84.87 114	98.55 152	89.51 142	96.26 128	98.28 116

MAR-MVS		94.22 86	93.46 96	96.51 72	98.00 80	92.19 77	97.67 61	97.47 120	88.13 205	93.00 114	95.84 152	84.86 115	99.51 72	87.99 171	98.17 82	97.83 133
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
jason		94.84 78	94.39 79	96.18 92	95.52 176	90.93 116	96.09 205	96.52 206	89.28 159	96.01 56	97.32 87	84.70 116	98.77 136	95.15 48	98.91 66	98.85 80
jason: jason.
sss		94.51 82	93.80 84	96.64 62	97.07 115	91.97 84	96.32 190	98.06 55	88.94 176	94.50 85	96.78 105	84.60 117	99.27 93	91.90 107	96.02 129	98.68 90

LS3D		93.57 110	92.61 121	96.47 75	97.59 103	91.61 91	97.67 61	97.72 95	85.17 255	90.29 165	98.34 25	84.60 117	99.73 23	83.85 247	98.27 79	98.06 124

Vis-MVSNet (Re-imp)		94.15 88	93.88 82	94.95 144	97.61 101	87.92 211	98.10 31	95.80 238	92.22 86	93.02 113	97.45 86	84.53 119	97.91 226	88.24 166	97.97 86	99.02 62

cdsmvs_eth3d_5k		23.24 316	30.99 316	0.00 331	0.00 344	0.00 345	0.00 336	97.63 105	0.00 340	0.00 341	96.88 103	84.38 120	0.00 343	0.00 340	0.00 341	0.00 339

CHOSEN 280x420		93.12 122	92.72 117	94.34 165	96.71 129	87.27 222	90.29 310	97.72 95	86.61 240	91.34 145	95.29 184	84.29 121	98.41 160	93.25 88	98.94 65	97.35 151

PCF-MVS	89.48 11	91.56 179	89.95 209	96.36 82	96.60 131	92.52 68	92.51 294	97.26 145	79.41 301	88.90 212	96.56 124	84.04 122	99.55 63	77.01 294	97.30 105	97.01 154
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
1314		92.81 136	92.03 135	95.14 135	95.33 187	89.52 153	96.04 208	97.44 129	87.72 213	86.25 256	95.33 183	83.84 123	98.79 133	89.26 146	97.05 110	97.11 153

DP-MVS		92.76 137	91.51 156	96.52 69	98.77 33	90.99 112	97.38 89	96.08 222	82.38 283	89.29 207	97.87 53	83.77 124	99.69 33	81.37 273	96.69 120	98.89 78

3Dnovator+	91.43 4	95.40 59	94.48 76	98.16 5	96.90 121	95.34 4	98.48 14	97.87 83	94.65 28	88.53 220	98.02 45	83.69 125	99.71 27	93.18 89	98.96 64	99.44 30

AdaColmap		94.34 84	93.68 88	96.31 84	98.59 46	91.68 90	96.59 169	97.81 88	89.87 146	92.15 131	97.06 99	83.62 126	99.54 65	89.34 144	98.07 84	97.70 138

DU-MVS		92.90 131	92.04 134	95.49 119	94.95 207	92.83 59	97.16 110	98.24 27	93.02 66	90.13 171	95.71 163	83.47 127	97.85 229	91.71 113	83.93 272	95.78 196

Baseline_NR-MVSNet		91.20 195	90.62 186	92.95 229	93.83 261	88.03 205	97.01 120	95.12 266	88.42 193	89.70 192	95.13 191	83.47 127	97.44 259	89.66 139	83.24 281	93.37 284

EPNet_dtu		91.71 167	91.28 162	92.99 228	93.76 263	83.71 268	96.69 158	95.28 257	93.15 62	87.02 250	95.95 147	83.37 129	97.38 265	79.46 283	96.84 113	97.88 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-untuned		92.94 129	92.62 120	93.92 185	97.22 109	86.16 243	96.40 182	96.25 215	90.06 144	89.79 187	96.17 140	83.19 130	98.35 165	87.19 193	97.27 106	97.24 152

TranMVSNet+NR-MVSNet		92.50 144	91.63 151	95.14 135	94.76 216	92.07 79	97.53 76	98.11 43	92.90 75	89.56 198	96.12 141	83.16 131	97.60 250	89.30 145	83.20 282	95.75 200

v18		88.71 246	87.52 245	92.27 243	94.16 238	88.11 201	96.82 139	95.96 224	87.03 226	80.76 290	89.81 291	83.15 132	96.22 286	84.69 229	75.31 308	92.49 294

CHOSEN 1792x2688		94.15 88	93.51 94	96.06 94	98.27 66	89.38 162	95.18 248	98.48 14	85.60 250	93.76 97	97.11 97	83.15 132	99.61 45	91.33 122	98.72 70	99.19 49

PMMVS		92.86 133	92.34 130	94.42 162	94.92 209	86.73 235	94.53 257	96.38 209	84.78 262	94.27 89	95.12 192	83.13 134	98.40 161	91.47 120	96.49 124	98.12 120

v17		88.67 248	87.47 248	92.26 245	94.13 241	88.09 203	96.81 140	95.95 225	87.02 227	80.72 291	89.75 293	83.11 135	96.20 287	84.61 232	75.15 310	92.49 294

v1neww		91.70 170	91.01 169	93.75 193	94.19 235	88.14 197	97.20 105	96.98 174	89.18 164	89.87 183	94.44 215	83.10 136	98.06 195	89.06 153	85.09 256	95.06 237

v7new		91.70 170	91.01 169	93.75 193	94.19 235	88.14 197	97.20 105	96.98 174	89.18 164	89.87 183	94.44 215	83.10 136	98.06 195	89.06 153	85.09 256	95.06 237

Effi-MVS+-dtu		93.08 123	93.21 104	92.68 238	96.02 162	83.25 273	97.14 112	96.72 194	93.85 42	91.20 154	93.44 256	83.08 138	98.30 169	91.69 115	95.73 136	96.50 173

mvs-test1		93.63 107	93.69 87	93.46 212	96.02 162	84.61 261	97.24 99	96.72 194	93.85 42	92.30 128	95.76 160	83.08 138	98.89 126	91.69 115	96.54 123	96.87 165

v16		88.69 247	87.50 246	92.26 245	94.19 235	88.11 201	96.81 140	95.95 225	87.01 228	80.71 292	89.80 292	83.08 138	96.20 287	84.61 232	75.34 307	92.48 296

v8		91.29 193	90.53 189	93.57 207	94.15 239	88.12 199	97.34 91	97.06 165	88.99 173	88.32 223	94.26 231	83.08 138	98.01 206	87.62 183	83.92 274	94.57 263

v6		91.69 172	91.00 171	93.75 193	94.14 240	88.12 199	97.20 105	96.98 174	89.19 162	89.90 180	94.42 217	83.04 142	98.07 190	89.07 152	85.10 255	95.07 234

V14		88.52 251	87.30 251	92.17 250	94.12 243	87.99 206	96.72 151	95.91 228	86.98 230	80.50 296	89.63 294	83.03 143	96.12 291	84.23 238	74.60 313	92.40 301

divwei89l23v2f112		91.61 174	90.89 172	93.78 190	94.01 253	88.22 190	96.96 123	96.96 178	89.17 166	89.75 189	94.28 227	83.02 144	98.03 202	88.86 158	84.98 263	95.08 232

v15		88.53 250	87.31 250	92.20 248	94.09 247	88.05 204	96.72 151	95.90 229	87.01 228	80.53 295	89.60 297	83.02 144	96.13 289	84.29 237	74.64 311	92.41 300

v1141		91.61 174	90.89 172	93.78 190	94.01 253	88.24 188	96.96 123	96.96 178	89.17 166	89.75 189	94.29 225	82.99 146	98.03 202	88.85 159	85.00 261	95.07 234

V9		88.49 254	87.26 252	92.18 249	94.12 243	87.97 209	96.73 148	95.90 229	86.95 232	80.40 298	89.61 295	82.98 147	96.13 289	84.14 239	74.55 314	92.44 298

v1		91.61 174	90.89 172	93.78 190	94.01 253	88.21 191	96.96 123	96.96 178	89.17 166	89.78 188	94.29 225	82.97 148	98.05 198	88.85 159	84.99 262	95.08 232

BH-w/o		92.14 160	91.75 143	93.31 218	96.99 120	85.73 246	95.67 225	95.69 240	88.73 186	89.26 209	94.82 200	82.97 148	98.07 190	85.26 223	96.32 127	96.13 182

v148		90.99 202	90.38 192	92.81 233	93.83 261	85.80 245	96.78 145	96.68 199	89.45 156	88.75 216	93.93 239	82.96 150	97.82 233	87.83 174	83.25 280	94.80 254

v13		88.45 256	87.22 256	92.16 252	94.08 249	87.95 210	96.71 153	95.90 229	86.86 237	80.27 302	89.55 299	82.92 151	96.12 291	84.02 242	74.63 312	92.40 301

v12		88.46 255	87.23 255	92.17 250	94.10 246	87.99 206	96.71 153	95.90 229	86.91 233	80.34 300	89.58 298	82.92 151	96.11 293	84.09 240	74.50 316	92.42 299

HyFIR lowres test		93.66 106	92.92 109	95.87 101	98.24 69	89.88 140	94.58 255	98.49 12	85.06 257	93.78 96	95.78 159	82.86 153	98.67 142	91.77 111	95.71 137	99.07 61

test_djsdf		93.07 124	92.76 112	94.00 176	93.49 271	88.70 177	98.22 26	97.57 109	91.42 111	90.08 177	95.55 172	82.85 154	97.92 223	94.07 68	91.58 191	95.40 215

PatchmatchNet		91.91 164	91.35 158	93.59 204	95.38 182	84.11 265	93.15 285	95.39 250	89.54 153	92.10 132	93.68 245	82.82 155	98.13 179	84.81 227	95.32 140	98.52 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												82.76 156

xiu_mvs_v1_base_debu		95.01 69	94.76 65	95.75 106	96.58 132	91.71 87	96.25 196	97.35 140	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 183

xiu_mvs_v1_base		95.01 69	94.76 65	95.75 106	96.58 132	91.71 87	96.25 196	97.35 140	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 183

xiu_mvs_v1_base_debi		95.01 69	94.76 65	95.75 106	96.58 132	91.71 87	96.25 196	97.35 140	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 183

patchmatchnet-post											90.45 287	82.65 160	98.10 183

V42		91.58 178	90.87 175	93.73 196	94.05 252	88.50 181	97.32 94	96.97 177	88.80 184	89.71 191	94.33 222	82.54 161	98.05 198	89.01 155	85.07 258	94.64 262

WR-MVS		92.34 149	91.53 154	94.77 151	95.13 199	90.83 119	96.40 182	97.98 75	91.88 100	89.29 207	95.54 173	82.50 162	97.80 234	89.79 136	85.27 252	95.69 203

v11		88.41 257	87.19 259	92.08 255	94.08 249	87.77 215	96.75 146	95.85 235	86.74 238	80.50 296	89.50 300	82.49 163	96.08 294	83.55 248	75.20 309	92.38 303

tpmrst		91.44 185	91.32 160	91.79 263	95.15 198	79.20 303	93.42 279	95.37 252	88.55 190	93.49 101	93.67 246	82.49 163	98.27 170	90.41 130	89.34 219	97.90 128

MDTV_nov1_ep13_2view						70.35 319	93.10 287		83.88 271	93.55 99		82.47 165		86.25 205		98.38 113

XVG-OURS-SEG-HR		93.86 100	93.55 91	94.81 148	97.06 117	88.53 180	95.28 243	97.45 126	91.68 104	94.08 92	97.68 67	82.41 166	98.90 124	93.84 76	92.47 175	96.98 155

QAPM		93.45 113	92.27 131	96.98 58	96.77 127	92.62 65	98.39 18	98.12 40	84.50 265	88.27 226	97.77 62	82.39 167	99.81 17	85.40 221	98.81 67	98.51 98

Patchmatch-test		89.42 238	87.99 242	93.70 199	95.27 189	85.11 253	88.98 317	94.37 291	81.11 293	87.10 248	93.69 244	82.28 168	97.50 255	74.37 299	94.76 148	98.48 104

Vis-MVSNet		95.23 64	94.81 64	96.51 72	97.18 111	91.58 94	98.26 24	98.12 40	94.38 33	94.90 79	98.15 39	82.28 168	98.92 122	91.45 121	98.58 74	99.01 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v7		91.47 184	90.73 183	93.68 201	94.13 241	88.16 195	97.09 114	97.05 166	88.38 194	89.80 186	94.52 209	82.21 170	98.01 206	88.00 170	85.42 249	94.87 246

3Dnovator	91.36 5	95.19 67	94.44 78	97.44 38	96.56 135	93.36 49	98.65 6	98.36 16	94.12 37	89.25 210	98.06 43	82.20 171	99.77 20	93.41 86	99.32 39	99.18 50

v10		91.04 201	90.23 199	93.49 209	94.12 243	88.16 195	97.32 94	97.08 162	88.26 198	88.29 225	94.22 232	82.17 172	97.97 213	86.45 203	84.12 270	94.33 270

v1144		91.37 189	90.60 187	93.68 201	93.89 259	88.23 189	96.84 135	97.03 171	88.37 195	89.69 193	94.39 218	82.04 173	97.98 210	87.80 175	85.37 250	94.84 248

MVSTER		93.20 120	92.81 111	94.37 163	96.56 135	89.59 148	97.06 115	97.12 157	91.24 117	91.30 148	95.96 146	82.02 174	98.05 198	93.48 83	90.55 207	95.47 208

CP-MVSNet		91.89 165	91.24 164	93.82 187	95.05 202	88.57 179	97.82 49	98.19 32	91.70 103	88.21 227	95.76 160	81.96 175	97.52 254	87.86 173	84.65 266	95.37 218

Patchmatch-RL test		87.38 265	86.24 264	90.81 279	88.74 312	78.40 306	88.12 320	93.17 310	87.11 225	82.17 279	89.29 301	81.95 176	95.60 302	88.64 164	77.02 301	98.41 109

sam_mvs												81.94 177

pmmvs4		90.93 204	89.85 213	94.17 169	93.34 275	90.79 121	94.60 254	96.02 223	84.62 263	87.45 238	95.15 189	81.88 178	97.45 258	87.70 177	87.87 232	94.27 273

test_post											17.58 339	81.76 179	98.08 186

XVG-OURS		93.72 105	93.35 101	94.80 149	97.07 115	88.61 178	94.79 252	97.46 122	91.97 99	93.99 93	97.86 55	81.74 180	98.88 128	92.64 94	92.67 174	96.92 163

v2v482		91.59 177	90.85 177	93.80 188	93.87 260	88.17 194	96.94 129	96.88 188	89.54 153	89.53 199	94.90 196	81.70 181	98.02 205	89.25 147	85.04 260	95.20 229

v144192		91.06 200	90.28 195	93.39 214	93.66 266	87.23 225	96.83 136	97.07 163	87.43 218	89.69 193	94.28 227	81.48 182	98.00 209	87.18 194	84.92 264	94.93 244

pcd1.5k->3k		38.37 314	40.51 315	31.96 327	94.29 233	0.00 345	0.00 336	97.69 99	0.00 340	0.00 341	0.00 342	81.45 183	0.00 343	0.00 340	91.11 199	95.89 188

MDTV_nov1_ep13			90.76 181		95.22 194	80.33 293	93.03 288	95.28 257	88.14 204	92.84 120	93.83 241	81.34 184	98.08 186	82.86 255	94.34 153

HQP_MVS		93.78 103	93.43 98	94.82 146	96.21 151	89.99 134	97.74 54	97.51 115	94.85 17	91.34 145	96.64 115	81.32 185	98.60 147	93.02 90	92.23 178	95.86 189

plane_prior6					96.10 161	90.00 132						81.32 185

v7n		90.76 208	89.86 212	93.45 213	93.54 268	87.60 219	97.70 60	97.37 137	88.85 178	87.65 236	94.08 235	81.08 187	98.10 183	84.68 230	83.79 277	94.66 261

v748		90.34 221	89.54 223	92.75 235	93.25 278	85.71 247	97.61 70	97.17 150	88.54 191	87.20 245	93.54 250	81.02 188	98.01 206	85.73 217	81.80 287	94.52 264

MVS_0304		96.05 49	95.45 51	97.85 13	97.75 95	94.50 13	96.87 133	97.95 79	95.46 6	95.60 70	98.01 46	80.96 189	99.83 13	97.23 2	99.25 44	99.23 47

HQP2-MVS												80.95 190

HQP-MVS		93.19 121	92.74 116	94.54 158	95.86 165	89.33 163	96.65 161	97.39 134	93.55 48	90.14 167	95.87 150	80.95 190	98.50 156	92.13 101	92.10 183	95.78 196

V4		90.71 213	90.00 207	92.82 230	93.21 282	87.03 229	97.59 73	97.16 153	88.21 199	87.69 234	93.92 240	80.93 192	98.06 195	87.39 187	83.90 275	93.39 283

v52		90.70 214	90.00 207	92.82 230	93.24 279	87.03 229	97.60 71	97.14 154	88.21 199	87.69 234	93.94 238	80.91 193	98.07 190	87.39 187	83.87 276	93.36 285

CR-MVSNet		90.82 207	89.77 216	93.95 181	94.45 227	87.19 226	90.23 311	95.68 241	86.89 235	92.40 123	92.36 274	80.91 193	97.05 273	81.09 275	93.95 161	97.60 144

Patchmtry		88.64 249	87.25 253	92.78 234	94.09 247	86.64 236	89.82 314	95.68 241	80.81 297	87.63 237	92.36 274	80.91 193	97.03 275	78.86 286	85.12 254	94.67 260

v1192		91.07 199	90.23 199	93.58 206	93.70 264	87.82 214	96.73 148	97.07 163	87.77 211	89.58 196	94.32 223	80.90 196	97.97 213	86.52 201	85.48 247	94.95 240

PatchFormer-LS_test		91.68 173	91.18 168	93.19 224	95.24 193	83.63 271	95.53 233	95.44 249	89.82 150	91.37 143	92.58 268	80.85 197	98.52 154	89.65 140	90.16 212	97.42 150

anonymousdsp		92.16 158	91.55 153	93.97 179	92.58 294	89.55 150	97.51 77	97.42 132	89.42 157	88.40 221	94.84 198	80.66 198	97.88 228	91.87 109	91.28 197	94.48 265

CLD-MVS		92.98 127	92.53 125	94.32 166	96.12 160	89.20 169	95.28 243	97.47 120	92.66 79	89.90 180	95.62 168	80.58 199	98.40 161	92.73 93	92.40 176	95.38 217
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_post1							92.81 291				16.58 340	80.53 200	97.68 243	86.20 206

VPA-MVSNet		93.24 119	92.48 128	95.51 117	95.70 172	92.39 70	97.86 45	98.66 9	92.30 85	92.09 133	95.37 181	80.49 201	98.40 161	93.95 71	85.86 245	95.75 200

tpmvs		89.83 234	89.15 230	91.89 259	94.92 209	80.30 294	93.11 286	95.46 248	86.28 242	88.08 228	92.65 265	80.44 202	98.52 154	81.47 271	89.92 215	96.84 166

PatchMatch-RL		92.90 131	92.02 136	95.56 114	98.19 75	90.80 120	95.27 245	97.18 148	87.96 206	91.86 137	95.68 166	80.44 202	98.99 119	84.01 243	97.54 96	96.89 164

PEN-MVS		91.20 195	90.44 190	93.48 210	94.49 225	87.91 213	97.76 52	98.18 34	91.29 114	87.78 232	95.74 162	80.35 204	97.33 267	85.46 220	82.96 283	95.19 230

Fast-Effi-MVS+-dtu		92.29 153	91.99 137	93.21 223	95.27 189	85.52 250	97.03 116	96.63 204	92.09 90	89.11 211	95.14 190	80.33 205	98.08 186	87.54 185	94.74 150	96.03 186

MSDG		91.42 186	90.24 198	94.96 143	97.15 113	88.91 174	93.69 274	96.32 211	85.72 249	86.93 251	96.47 128	80.24 206	98.98 120	80.57 276	95.05 144	96.98 155

v1921920		90.85 206	90.03 206	93.29 219	93.55 267	86.96 233	96.74 147	97.04 169	87.36 220	89.52 200	94.34 221	80.23 207	97.97 213	86.27 204	85.21 253	94.94 242

RPMNet		88.52 251	86.72 263	93.95 181	94.45 227	87.19 226	90.23 311	94.99 272	77.87 310	92.40 123	87.55 315	80.17 208	97.05 273	68.84 312	93.95 161	97.60 144

PatchT		88.87 244	87.42 249	93.22 222	94.08 249	85.10 254	89.51 315	94.64 282	81.92 286	92.36 126	88.15 310	80.05 209	97.01 277	72.43 304	93.65 164	97.54 147

DTE-MVSNet		90.56 217	89.75 218	93.01 227	93.95 256	87.25 223	97.64 68	97.65 103	90.74 126	87.12 246	95.68 166	79.97 210	97.00 278	83.33 251	81.66 290	94.78 257

TransMVSNet (Re)		88.94 241	87.56 244	93.08 226	94.35 230	88.45 183	97.73 56	95.23 261	87.47 217	84.26 269	95.29 184	79.86 211	97.33 267	79.44 284	74.44 317	93.45 282

ACMM	89.79 8	92.96 128	92.50 127	94.35 164	96.30 149	88.71 176	97.58 74	97.36 139	91.40 113	90.53 159	96.65 114	79.77 212	98.75 138	91.24 125	91.64 189	95.59 205
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XXY-MVS		92.16 158	91.23 165	94.95 144	94.75 217	90.94 115	97.47 82	97.43 131	89.14 169	88.90 212	96.43 130	79.71 213	98.24 171	89.56 141	87.68 233	95.67 204

PS-CasMVS		91.55 180	90.84 179	93.69 200	94.96 206	88.28 185	97.84 48	98.24 27	91.46 109	88.04 229	95.80 155	79.67 214	97.48 256	87.02 196	84.54 267	95.31 221

ab-mvs		93.57 110	92.55 123	96.64 62	97.28 108	91.96 85	95.40 238	97.45 126	89.81 151	93.22 107	96.28 135	79.62 215	99.46 77	90.74 128	93.11 169	98.50 100

v1240		90.70 214	89.85 213	93.23 221	93.51 270	86.80 234	96.61 166	97.02 172	87.16 224	89.58 196	94.31 224	79.55 216	97.98 210	85.52 219	85.44 248	94.90 245

CostFormer		91.18 198	90.70 184	92.62 239	94.84 213	81.76 282	94.09 268	94.43 288	84.15 267	92.72 121	93.77 243	79.43 217	98.20 173	90.70 129	92.18 181	97.90 128

CANet_DTU		94.37 83	93.65 89	96.55 68	96.46 143	92.13 78	96.21 200	96.67 201	94.38 33	93.53 100	97.03 100	79.34 218	99.71 27	90.76 127	98.45 76	97.82 134

OPM-MVS		93.28 118	92.76 112	94.82 146	94.63 221	90.77 122	96.65 161	97.18 148	93.72 45	91.68 138	97.26 90	79.33 219	98.63 144	92.13 101	92.28 177	95.07 234

JIA-IIPM		88.26 259	87.04 260	91.91 258	93.52 269	81.42 284	89.38 316	94.38 290	80.84 296	90.93 156	80.74 322	79.22 220	97.92 223	82.76 257	91.62 190	96.38 174

CVMVSNet		91.23 194	91.75 143	89.67 292	95.77 170	74.69 311	96.44 174	94.88 277	85.81 248	92.18 130	97.64 73	79.07 221	95.58 303	88.06 169	95.86 134	98.74 84

LPG-MVS_test		92.94 129	92.56 122	94.10 171	96.16 156	88.26 186	97.65 64	97.46 122	91.29 114	90.12 173	97.16 94	79.05 222	98.73 139	92.25 97	91.89 186	95.31 221

LGP-MVS_train				94.10 171	96.16 156	88.26 186		97.46 122	91.29 114	90.12 173	97.16 94	79.05 222	98.73 139	92.25 97	91.89 186	95.31 221

test-LLR		91.42 186	91.19 167	92.12 253	94.59 222	80.66 288	94.29 262	92.98 311	91.11 120	90.76 157	92.37 271	79.02 224	98.07 190	88.81 161	96.74 117	97.63 139

test0.0.03 1		89.37 239	88.70 234	91.41 272	92.47 295	85.63 248	95.22 247	92.70 314	91.11 120	86.91 252	93.65 247	79.02 224	93.19 316	78.00 289	89.18 220	95.41 211

ADS-MVSNet2		89.45 237	88.59 236	92.03 256	95.86 165	82.26 279	90.93 306	94.32 293	83.23 278	91.28 151	91.81 281	79.01 226	95.99 295	79.52 281	91.39 195	97.84 131

ADS-MVSNet		89.89 231	88.68 235	93.53 208	95.86 165	84.89 258	90.93 306	95.07 269	83.23 278	91.28 151	91.81 281	79.01 226	97.85 229	79.52 281	91.39 195	97.84 131

OpenMVS	89.19 12	92.86 133	91.68 146	96.40 78	95.34 184	92.73 62	98.27 23	98.12 40	84.86 260	85.78 258	97.75 63	78.89 228	99.74 22	87.50 186	98.65 71	96.73 168

LTVRE_ROB	88.41 13	90.99 202	89.92 210	94.19 168	96.18 154	89.55 150	96.31 191	97.09 160	87.88 209	85.67 259	95.91 149	78.79 229	98.57 150	81.50 270	89.98 213	94.44 267
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		90.72 212	89.65 222	93.96 180	94.29 233	89.63 145	97.79 51	96.82 191	89.07 170	86.12 257	95.48 179	78.61 230	97.78 236	86.97 197	81.67 289	94.46 266

PVSNet	86.66 18	92.24 155	91.74 145	93.73 196	97.77 94	83.69 270	92.88 289	96.72 194	87.91 208	93.00 114	94.86 197	78.51 231	99.05 117	86.53 200	97.45 101	98.47 105

ACMP	89.59 10	92.62 139	92.14 132	94.05 174	96.40 145	88.20 192	97.36 90	97.25 147	91.52 106	88.30 224	96.64 115	78.46 232	98.72 141	91.86 110	91.48 193	95.23 228
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
BH-RMVSNet		92.72 138	91.97 138	94.97 142	97.16 112	87.99 206	96.15 202	95.60 243	90.62 133	91.87 136	97.15 96	78.41 233	98.57 150	83.16 252	97.60 95	98.36 114

thres200		92.23 156	91.39 157	94.75 152	97.61 101	89.03 172	96.60 168	95.09 267	92.08 95	93.28 105	94.00 236	78.39 234	99.04 118	81.26 274	94.18 154	96.19 177

MDA-MVSNet_test_wron		85.87 277	84.23 279	90.80 281	92.38 296	82.57 275	93.17 283	95.15 264	82.15 284	67.65 320	92.33 277	78.20 235	95.51 304	77.33 291	79.74 295	94.31 272

thres400		92.42 147	91.52 155	95.12 137	97.85 92	89.29 166	97.41 84	94.88 277	92.19 88	93.27 106	94.46 214	78.17 236	99.08 116	81.40 272	94.08 155	96.98 155

YYNet1		85.87 277	84.23 279	90.78 282	92.38 296	82.46 277	93.17 283	95.14 265	82.12 285	67.69 319	92.36 274	78.16 237	95.50 305	77.31 292	79.73 296	94.39 268

view600		92.55 140	91.68 146	95.18 129	97.98 81	89.44 158	98.00 36	94.57 283	92.09 90	93.17 108	95.52 174	78.14 238	99.11 106	81.61 265	94.04 157	96.98 155

view800		92.55 140	91.68 146	95.18 129	97.98 81	89.44 158	98.00 36	94.57 283	92.09 90	93.17 108	95.52 174	78.14 238	99.11 106	81.61 265	94.04 157	96.98 155

conf0.05thres1000		92.55 140	91.68 146	95.18 129	97.98 81	89.44 158	98.00 36	94.57 283	92.09 90	93.17 108	95.52 174	78.14 238	99.11 106	81.61 265	94.04 157	96.98 155

tfpn		92.55 140	91.68 146	95.18 129	97.98 81	89.44 158	98.00 36	94.57 283	92.09 90	93.17 108	95.52 174	78.14 238	99.11 106	81.61 265	94.04 157	96.98 155

thres600view7		92.49 146	91.60 152	95.18 129	97.91 89	89.47 154	97.65 64	94.66 281	92.18 89	93.33 104	94.91 195	78.06 242	99.10 111	81.61 265	94.06 156	96.98 155

tpm cat1		88.36 258	87.21 257	91.81 262	95.13 199	80.55 291	92.58 293	95.70 239	74.97 316	87.45 238	91.96 279	78.01 243	98.17 177	80.39 278	88.74 225	96.72 169

MVP-Stereo		90.74 211	90.08 203	92.71 236	93.19 284	88.20 192	95.86 217	96.27 213	86.07 246	84.86 264	94.76 202	77.84 244	97.75 239	83.88 246	98.01 85	92.17 307
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EPMVS		90.70 214	89.81 215	93.37 216	94.73 218	84.21 263	93.67 275	88.02 328	89.50 155	92.38 125	93.49 253	77.82 245	97.78 236	86.03 211	92.68 173	98.11 123

tpm		90.25 223	89.74 219	91.76 266	93.92 257	79.73 299	93.98 269	93.54 308	88.28 197	91.99 134	93.25 259	77.51 246	97.44 259	87.30 191	87.94 231	98.12 120

FMVSNet3		91.78 166	90.69 185	95.03 139	96.53 137	92.27 73	97.02 118	96.93 183	89.79 152	89.35 204	94.65 207	77.01 247	97.47 257	86.12 208	88.82 222	95.35 219

TR-MVS		91.48 183	90.59 188	94.16 170	96.40 145	87.33 220	95.67 225	95.34 256	87.68 214	91.46 141	95.52 174	76.77 248	98.35 165	82.85 256	93.61 166	96.79 167

RPSCF		90.75 210	90.86 176	90.42 286	96.84 123	76.29 309	95.61 230	96.34 210	83.89 270	91.38 142	97.87 53	76.45 249	98.78 134	87.16 195	92.23 178	96.20 176

tpm2		89.96 229	89.21 228	92.23 247	94.91 211	81.25 285	93.78 272	94.42 289	80.62 298	91.56 139	93.44 256	76.44 250	97.94 219	85.60 218	92.08 185	97.49 148

EU-MVSNet		88.72 245	88.90 232	88.20 295	93.15 285	74.21 312	96.63 165	94.22 297	85.18 254	87.32 243	95.97 145	76.16 251	94.98 308	85.27 222	86.17 242	95.41 211

dp		88.90 243	88.26 241	90.81 279	94.58 224	76.62 308	92.85 290	94.93 275	85.12 256	90.07 178	93.07 260	75.81 252	98.12 181	80.53 277	87.42 237	97.71 137

Patchmatch-test1		91.54 181	90.85 177	93.59 204	95.59 174	84.95 257	94.72 253	95.58 245	90.82 124	92.25 129	93.58 249	75.80 253	97.41 262	83.35 249	95.98 130	98.40 110

IterMVS		90.15 227	89.67 220	91.61 268	95.48 178	83.72 267	94.33 261	96.12 221	89.99 145	87.31 244	94.15 233	75.78 254	96.27 285	86.97 197	86.89 240	94.83 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
semantic-postprocess				91.82 261	95.52 176	84.20 264		96.15 220	90.61 135	87.39 241	94.27 229	75.63 255	96.44 282	87.34 189	86.88 241	94.82 252

jajsoiax		92.42 147	91.89 140	94.03 175	93.33 277	88.50 181	97.73 56	97.53 113	92.00 98	88.85 214	96.50 127	75.62 256	98.11 182	93.88 75	91.56 192	95.48 206

cascas		91.20 195	90.08 203	94.58 157	94.97 205	89.16 171	93.65 276	97.59 108	79.90 300	89.40 202	92.92 262	75.36 257	98.36 164	92.14 100	94.75 149	96.23 175

tpmp4_e23		89.58 235	88.59 236	92.54 240	95.16 197	81.53 283	94.11 267	95.09 267	81.66 288	88.60 218	93.44 256	75.11 258	98.33 168	82.45 261	91.72 188	97.75 135

VPNet		92.23 156	91.31 161	94.99 140	95.56 175	90.96 114	97.22 104	97.86 85	92.96 73	90.96 155	96.62 122	75.06 259	98.20 173	91.90 107	83.65 278	95.80 195

test_normal		92.01 161	90.75 182	95.80 104	93.24 279	89.97 137	95.93 215	96.24 216	90.62 133	81.63 284	93.45 255	74.98 260	98.89 126	93.61 79	97.04 111	98.55 93

DI_MVS_plusplus_test		92.01 161	90.77 180	95.73 109	93.34 275	89.78 143	96.14 203	96.18 219	90.58 137	81.80 283	93.50 252	74.95 261	98.90 124	93.51 81	96.94 112	98.51 98

N_pmnet		78.73 295	78.71 294	78.79 313	92.80 290	46.50 339	94.14 266	43.71 343	78.61 306	80.83 287	91.66 284	74.94 262	96.36 283	67.24 313	84.45 268	93.50 280

mvs_tets		92.31 151	91.76 142	93.94 184	93.41 273	88.29 184	97.63 69	97.53 113	92.04 96	88.76 215	96.45 129	74.62 263	98.09 185	93.91 73	91.48 193	95.45 210

DSMNet-mixed		86.34 273	86.12 267	87.00 300	89.88 308	70.43 317	94.93 251	90.08 325	77.97 309	85.42 263	92.78 264	74.44 264	93.96 312	74.43 298	95.14 142	96.62 170

pmmvs5		89.86 233	88.87 233	92.82 230	92.86 288	86.23 242	96.26 195	95.39 250	84.24 266	87.12 246	94.51 210	74.27 265	97.36 266	87.61 184	87.57 234	94.86 247

OurMVSNet-221017-0		90.51 219	90.19 202	91.44 271	93.41 273	81.25 285	96.98 122	96.28 212	91.68 104	86.55 254	96.30 134	74.20 266	97.98 210	88.96 156	87.40 238	95.09 231

GBi-Net		91.35 190	90.27 196	94.59 153	96.51 138	91.18 107	97.50 78	96.93 183	88.82 181	89.35 204	94.51 210	73.87 267	97.29 269	86.12 208	88.82 222	95.31 221

test1		91.35 190	90.27 196	94.59 153	96.51 138	91.18 107	97.50 78	96.93 183	88.82 181	89.35 204	94.51 210	73.87 267	97.29 269	86.12 208	88.82 222	95.31 221

FMVSNet2		91.31 192	90.08 203	94.99 140	96.51 138	92.21 74	97.41 84	96.95 181	88.82 181	88.62 217	94.75 203	73.87 267	97.42 261	85.20 224	88.55 228	95.35 219

COLMAP_ROB	87.81 15	90.40 220	89.28 227	93.79 189	97.95 86	87.13 228	96.92 130	95.89 234	82.83 280	86.88 253	97.18 93	73.77 270	99.29 92	78.44 288	93.62 165	94.95 240
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DWT-MVSNet_test		90.76 208	89.89 211	93.38 215	95.04 203	83.70 269	95.85 218	94.30 294	88.19 201	90.46 161	92.80 263	73.61 271	98.50 156	88.16 167	90.58 206	97.95 126

Anonymous20231206		87.09 268	86.14 266	89.93 291	91.22 302	80.35 292	96.11 204	95.35 253	83.57 275	84.16 270	93.02 261	73.54 272	95.61 301	72.16 305	86.14 243	93.84 278

UGNet		94.04 95	93.28 103	96.31 84	96.85 122	91.19 106	97.88 44	97.68 100	94.40 31	93.00 114	96.18 138	73.39 273	99.61 45	91.72 112	98.46 75	98.13 119
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
LP		84.13 284	81.85 289	90.97 276	93.20 283	82.12 280	87.68 321	94.27 296	76.80 311	81.93 281	88.52 305	72.97 274	95.95 296	59.53 323	81.73 288	94.84 248

ACMH	87.59 16	90.53 218	89.42 225	93.87 186	96.21 151	87.92 211	97.24 99	96.94 182	88.45 192	83.91 274	96.27 136	71.92 275	98.62 146	84.43 235	89.43 218	95.05 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		91.38 188	90.31 193	94.59 153	94.65 220	87.62 218	94.34 260	96.19 218	90.73 127	90.35 164	93.83 241	71.84 276	97.96 217	87.22 192	93.61 166	98.21 117

SixPastTwentyTwo		89.15 240	88.54 238	90.98 275	93.49 271	80.28 295	96.70 156	94.70 280	90.78 125	84.15 271	95.57 170	71.78 277	97.71 242	84.63 231	85.07 258	94.94 242

gg-mvs-nofinetune		87.82 262	85.61 269	94.44 160	94.46 226	89.27 168	91.21 305	84.61 334	80.88 295	89.89 182	74.98 325	71.50 278	97.53 253	85.75 216	97.21 107	96.51 172

test20.03		86.14 275	85.40 271	88.35 293	90.12 305	80.06 297	95.90 216	95.20 262	88.59 187	81.29 286	93.62 248	71.43 279	92.65 317	71.26 309	81.17 292	92.34 304

MS-PatchMatch		90.27 222	89.77 216	91.78 264	94.33 231	84.72 260	95.55 231	96.73 193	86.17 245	86.36 255	95.28 186	71.28 280	97.80 234	84.09 240	98.14 83	92.81 290

PVSNet_0	82.17 19	85.46 280	83.64 281	90.92 277	95.27 189	79.49 300	90.55 309	95.60 243	83.76 273	83.00 277	89.95 288	71.09 281	97.97 213	82.75 258	60.79 327	95.31 221

GG-mvs-BLEND				93.62 203	93.69 265	89.20 169	92.39 297	83.33 335		87.98 231	89.84 290	71.00 282	96.87 279	82.08 264	95.40 139	94.80 254

ITE_SJBPF				92.43 242	95.34 184	85.37 252		95.92 227	91.47 108	87.75 233	96.39 132	71.00 282	97.96 217	82.36 262	89.86 216	93.97 276

IB-MVS	87.33 17	89.91 230	88.28 240	94.79 150	95.26 192	87.70 217	95.12 249	93.95 302	89.35 158	87.03 249	92.49 269	70.74 284	99.19 97	89.18 150	81.37 291	97.49 148
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
MDA-MVSNet-bldmvs		85.00 281	82.95 283	91.17 274	93.13 286	83.33 272	94.56 256	95.00 271	84.57 264	65.13 324	92.65 265	70.45 285	95.85 297	73.57 302	77.49 300	94.33 270

AllTest		90.23 224	88.98 231	93.98 177	97.94 87	86.64 236	96.51 173	95.54 246	85.38 251	85.49 261	96.77 106	70.28 286	99.15 102	80.02 279	92.87 170	96.15 180

TestCases				93.98 177	97.94 87	86.64 236		95.54 246	85.38 251	85.49 261	96.77 106	70.28 286	99.15 102	80.02 279	92.87 170	96.15 180

ACMH+	87.92 14	90.20 225	89.18 229	93.25 220	96.48 141	86.45 240	96.99 121	96.68 199	88.83 180	84.79 265	96.22 137	70.16 288	98.53 153	84.42 236	88.04 230	94.77 258

pmmvs-eth3d		86.22 274	84.45 277	91.53 269	88.34 313	87.25 223	94.47 258	95.01 270	83.47 276	79.51 306	89.61 295	69.75 289	95.71 300	83.13 253	76.73 303	91.64 309

LFMVS		93.60 108	92.63 119	96.52 69	98.13 77	91.27 102	97.94 41	93.39 309	90.57 138	96.29 44	98.31 31	69.00 290	99.16 101	94.18 67	95.87 133	99.12 57

TESTMET0.1,1		90.06 228	89.42 225	91.97 257	94.41 229	80.62 290	94.29 262	91.97 318	87.28 223	90.44 162	92.47 270	68.79 291	97.67 244	88.50 165	96.60 122	97.61 143

XVG-ACMP-BASELINE		90.93 204	90.21 201	93.09 225	94.31 232	85.89 244	95.33 240	97.26 145	91.06 122	89.38 203	95.44 180	68.61 292	98.60 147	89.46 143	91.05 200	94.79 256

MVS-HIRNet		82.47 290	81.21 291	86.26 303	95.38 182	69.21 322	88.96 318	89.49 327	66.28 324	80.79 289	74.08 327	68.48 293	97.39 264	71.93 306	95.47 138	92.18 306

VDD-MVS		93.82 101	93.08 105	96.02 96	97.88 91	89.96 139	97.72 58	95.85 235	92.43 83	95.86 60	98.44 14	68.42 294	99.39 86	96.31 19	94.85 145	98.71 88

test_0402		86.46 272	84.79 275	91.45 270	95.02 204	85.55 249	96.29 193	94.89 276	80.90 294	82.21 278	93.97 237	68.21 295	97.29 269	62.98 318	88.68 227	91.51 311

test-mter		90.19 226	89.54 223	92.12 253	94.59 222	80.66 288	94.29 262	92.98 311	87.68 214	90.76 157	92.37 271	67.67 296	98.07 190	88.81 161	96.74 117	97.63 139

VDDNet		93.05 125	92.07 133	96.02 96	96.84 123	90.39 130	98.08 33	95.85 235	86.22 244	95.79 64	98.46 12	67.59 297	99.19 97	94.92 57	94.85 145	98.47 105

USDC		88.94 241	87.83 243	92.27 243	94.66 219	84.96 256	93.86 271	95.90 229	87.34 221	83.40 276	95.56 171	67.43 298	98.19 175	82.64 260	89.67 217	93.66 279

pmmvs6		87.81 263	86.19 265	92.69 237	91.32 301	86.30 241	97.34 91	96.41 208	80.59 299	84.05 273	94.37 220	67.37 299	97.67 244	84.75 228	79.51 297	94.09 275

K. test v3		87.64 264	86.75 262	90.32 287	93.02 287	79.48 301	96.61 166	92.08 317	90.66 131	80.25 303	94.09 234	67.21 300	96.65 281	85.96 213	80.83 294	94.83 250

CMPMVS	62.92 21	85.62 279	84.92 274	87.74 297	89.14 311	73.12 315	94.17 265	96.80 192	73.98 318	73.65 314	94.93 194	66.36 301	97.61 249	83.95 245	91.28 197	92.48 296
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
lessismore_v0				90.45 285	91.96 299	79.09 304		87.19 331		80.32 301	94.39 218	66.31 302	97.55 252	84.00 244	76.84 302	94.70 259

new-patchmatchnet		83.18 286	81.87 287	87.11 299	86.88 318	75.99 310	93.70 273	95.18 263	85.02 258	77.30 310	88.40 307	65.99 303	93.88 313	74.19 301	70.18 321	91.47 313

FMVSNet1		89.88 232	88.31 239	94.59 153	95.41 180	91.18 107	97.50 78	96.93 183	86.62 239	87.41 240	94.51 210	65.94 304	97.29 269	83.04 254	87.43 236	95.31 221

TDRefinement		86.53 271	84.76 276	91.85 260	82.23 326	84.25 262	96.38 184	95.35 253	84.97 259	84.09 272	94.94 193	65.76 305	98.34 167	84.60 234	74.52 315	92.97 286

UnsupCasMVSNet_eth		85.99 276	84.45 277	90.62 283	89.97 307	82.40 278	93.62 277	97.37 137	89.86 147	78.59 308	92.37 271	65.25 306	95.35 306	82.27 263	70.75 320	94.10 274

LF4IMVS		87.94 261	87.25 253	89.98 290	92.38 296	80.05 298	94.38 259	95.25 260	87.59 216	84.34 267	94.74 204	64.31 307	97.66 246	84.83 226	87.45 235	92.23 305

MIMVSNet		88.50 253	86.76 261	93.72 198	94.84 213	87.77 215	91.39 301	94.05 299	86.41 241	87.99 230	92.59 267	63.27 308	95.82 299	77.44 290	92.84 172	97.57 146

FMVSNet5		87.29 267	85.79 268	91.78 264	94.80 215	87.28 221	95.49 235	95.28 257	84.09 268	83.85 275	91.82 280	62.95 309	94.17 311	78.48 287	85.34 251	93.91 277

testgi		87.97 260	87.21 257	90.24 288	92.86 288	80.76 287	96.67 160	94.97 273	91.74 102	85.52 260	95.83 153	62.66 310	94.47 310	76.25 295	88.36 229	95.48 206

TinyColmap		86.82 270	85.35 272	91.21 273	94.91 211	82.99 274	93.94 270	94.02 301	83.58 274	81.56 285	94.68 205	62.34 311	98.13 179	75.78 296	87.35 239	92.52 293

testpf		80.97 292	81.40 290	79.65 311	91.53 300	72.43 316	73.47 332	89.55 326	78.63 305	80.81 288	89.06 302	61.36 312	91.36 322	83.34 250	84.89 265	75.15 327

new_pmnet		82.89 287	81.12 292	88.18 296	89.63 309	80.18 296	91.77 300	92.57 315	76.79 312	75.56 312	88.23 309	61.22 313	94.48 309	71.43 307	82.92 284	89.87 316

test2356		82.77 288	82.14 286	84.65 304	85.77 320	70.36 318	91.22 304	93.69 307	81.58 290	81.82 282	89.00 303	60.63 314	90.77 323	64.74 316	90.80 204	92.82 288

OpenMVS_ROB	81.14 20	84.42 283	82.28 284	90.83 278	90.06 306	84.05 266	95.73 224	94.04 300	73.89 319	80.17 304	91.53 285	59.15 315	97.64 247	66.92 314	89.05 221	90.80 314

test1235678		79.82 294	78.53 295	83.69 306	82.55 325	67.55 324	92.50 295	94.13 298	79.28 302	72.10 317	86.45 318	57.27 316	90.68 324	61.60 321	80.90 293	92.82 288

MIMVSNet1		84.93 282	83.05 282	90.56 284	89.56 310	84.84 259	95.40 238	95.35 253	83.91 269	80.38 299	92.21 278	57.23 317	93.34 315	70.69 311	82.75 286	93.50 280

EG-PatchMatch MVS		87.02 269	85.44 270	91.76 266	92.67 292	85.00 255	96.08 207	96.45 207	83.41 277	79.52 305	93.49 253	57.10 318	97.72 241	79.34 285	90.87 203	92.56 292

UnsupCasMVSNet_bld		82.13 291	79.46 293	90.14 289	88.00 314	82.47 276	90.89 308	96.62 205	78.94 304	75.61 311	84.40 320	56.63 319	96.31 284	77.30 293	66.77 326	91.63 310

1111		78.29 296	77.55 296	80.50 309	83.89 321	59.98 331	91.89 298	93.71 304	75.06 314	73.60 315	87.67 313	55.66 320	92.60 318	58.54 325	77.92 299	88.93 318

.test1245		65.38 305	69.22 303	53.86 325	83.89 321	59.98 331	91.89 298	93.71 304	75.06 314	73.60 315	87.67 313	55.66 320	92.60 318	58.54 325	2.96 338	9.00 336

Test4		89.48 236	87.50 246	95.44 124	90.76 304	89.72 144	95.78 223	97.09 160	90.28 140	77.67 309	91.74 283	55.42 322	98.08 186	91.92 106	96.83 114	98.52 96

testing_2		87.33 266	85.03 273	94.22 167	87.77 316	89.32 165	94.97 250	97.11 159	89.22 161	71.64 318	88.73 304	55.16 323	97.94 219	91.95 105	88.73 226	95.41 211

testus		82.63 289	82.15 285	84.07 305	87.31 317	67.67 323	93.18 281	94.29 295	82.47 282	82.14 280	90.69 286	53.01 324	91.94 320	66.30 315	89.96 214	92.62 291

tmp_tt		51.94 313	53.82 310	46.29 326	33.73 341	45.30 341	78.32 331	67.24 342	18.02 336	50.93 330	87.05 317	52.99 325	53.11 339	70.76 310	25.29 336	40.46 334

pmmvs3		79.97 293	77.50 297	87.39 298	82.80 324	79.38 302	92.70 292	90.75 323	70.69 322	78.66 307	87.47 316	51.34 326	93.40 314	73.39 303	69.65 322	89.38 317

DeepMVS_CX				74.68 318	90.84 303	64.34 328		81.61 338	65.34 325	67.47 322	88.01 311	48.60 327	80.13 334	62.33 320	73.68 319	79.58 325

test12356		74.97 298	74.13 299	77.49 314	78.81 327	56.23 335	88.53 319	92.75 313	75.14 313	67.50 321	85.07 319	44.88 328	89.96 325	58.71 324	75.75 305	86.26 319

PM-MVS		83.48 285	81.86 288	88.31 294	87.83 315	77.59 307	93.43 278	91.75 319	86.91 233	80.63 293	89.91 289	44.42 329	95.84 298	85.17 225	76.73 303	91.50 312

Anonymous20231211		78.22 297	75.30 298	86.99 301	86.14 319	74.16 313	95.62 229	93.88 303	66.43 323	74.44 313	87.86 312	41.39 330	95.11 307	62.49 319	69.46 323	91.71 308

ambc				86.56 302	83.60 323	70.00 321	85.69 324	94.97 273		80.60 294	88.45 306	37.42 331	96.84 280	82.69 259	75.44 306	92.86 287

testmv		72.22 300	70.02 300	78.82 312	73.06 334	61.75 329	91.24 303	92.31 316	74.45 317	61.06 326	80.51 323	34.21 332	88.63 328	55.31 328	68.07 325	86.06 320

Gipuma		67.86 304	65.41 305	75.18 317	92.66 293	73.45 314	66.50 334	94.52 287	53.33 329	57.80 328	66.07 331	30.81 333	89.20 327	48.15 332	78.88 298	62.90 331
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		68.12 303	63.78 306	81.13 308	74.01 331	70.22 320	87.61 322	90.71 324	72.63 321	53.13 329	71.89 328	30.29 334	91.45 321	61.53 322	32.21 332	81.72 324

EMVS		52.08 312	51.31 312	54.39 324	72.62 335	45.39 340	83.84 326	75.51 340	41.13 334	40.77 334	59.65 334	30.08 335	73.60 337	28.31 336	29.90 335	44.18 333

FPMVS		71.27 301	69.85 301	75.50 316	74.64 329	59.03 333	91.30 302	91.50 320	58.80 327	57.92 327	88.28 308	29.98 336	85.53 331	53.43 329	82.84 285	81.95 323

E-PMN		53.28 310	52.56 311	55.43 323	74.43 330	47.13 338	83.63 327	76.30 339	42.23 333	42.59 332	62.22 333	28.57 337	74.40 336	31.53 335	31.51 333	44.78 332

PMMVS2		70.19 302	66.92 304	80.01 310	76.35 328	65.67 326	86.22 323	87.58 330	64.83 326	62.38 325	80.29 324	26.78 338	88.49 329	63.79 317	54.07 328	85.88 321

ANet_high		63.94 306	59.58 307	77.02 315	61.24 339	66.06 325	85.66 325	87.93 329	78.53 307	42.94 331	71.04 329	25.42 339	80.71 333	52.60 330	30.83 334	84.28 322

LCM-MVSNet		72.55 299	69.39 302	82.03 307	70.81 336	65.42 327	90.12 313	94.36 292	55.02 328	65.88 323	81.72 321	24.16 340	89.96 325	74.32 300	68.10 324	90.71 315

PNet_i23d		59.01 307	55.87 308	68.44 320	73.98 332	51.37 336	81.36 328	82.41 336	52.37 330	42.49 333	70.39 330	11.39 341	79.99 335	49.77 331	38.71 330	73.97 328

PMVS	53.92 22	58.58 308	55.40 309	68.12 321	51.00 340	48.64 337	78.86 330	87.10 332	46.77 332	35.84 336	74.28 326	8.76 342	86.34 330	42.07 333	73.91 318	69.38 329
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		25.11 315	24.57 317	26.74 328	73.98 332	39.89 342	57.88 335	9.80 344	12.27 337	10.39 338	6.97 341	7.03 343	36.44 340	25.43 337	17.39 337	3.89 338

wuykxyi23d		56.92 309	51.11 313	74.38 319	62.30 338	61.47 330	80.09 329	84.87 333	49.62 331	30.80 337	57.20 335	7.03 343	82.94 332	55.69 327	32.36 331	78.72 326

MVE	50.73 23	53.25 311	48.81 314	66.58 322	65.34 337	57.50 334	72.49 333	70.94 341	40.15 335	39.28 335	63.51 332	6.89 345	73.48 338	38.29 334	42.38 329	68.76 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		13.04 318	15.66 319	5.18 329	4.51 343	3.45 343	92.50 295	1.81 346	2.50 339	7.58 340	20.15 338	3.67 346	2.18 342	7.13 339	1.07 340	9.90 335

testmvs		13.36 317	16.33 318	4.48 330	5.04 342	2.26 344	93.18 281	3.28 345	2.70 338	8.24 339	21.66 337	2.29 347	2.19 341	7.58 338	2.96 338	9.00 336

sosnet-low-res		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

sosnet		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

uncertanet		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

Regformer		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

ab-mvs-re		8.06 319	10.74 320	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	96.69 112	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

uanet		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

ESAPD								98.25 25

MTGPA								98.08 48

MTMP								82.03 337

gm-plane-assit					93.22 281	78.89 305			84.82 261		93.52 251		98.64 143	87.72 176

test9_res														94.81 60	99.38 33	99.45 28

agg_prior2														93.94 72	99.38 33	99.50 22

agg_prior					98.67 38	93.79 35		98.00 69		95.68 66			99.57 61

test_prior4						93.66 39	96.42 177

test_prior				97.23 48	98.67 38	92.99 55		98.00 69					99.41 83			99.29 43

旧先验2							95.94 214		81.66 288	97.34 15			98.82 131	92.26 95

新几何2							95.79 221

无先验							95.79 221	97.87 83	83.87 272				99.65 39	87.68 179		98.89 78

原ACMM2							95.67 225

testdata2													99.67 37	85.96 213

testdata1							95.26 246		93.10 65

plane_prior7					96.21 151	89.98 136

plane_prior5								97.51 115					98.60 147	93.02 90	92.23 178	95.86 189

plane_prior4											96.64 115

plane_prior3						90.00 132			94.46 30	91.34 145

plane_prior2							97.74 54		94.85 17

plane_prior1					96.14 159

plane_prior						89.99 134	97.24 99		94.06 38						92.16 182

n2								0.00 347

nn								0.00 347

door-mid								91.06 322

test11								97.88 81

door								91.13 321

HQP5-MVS						89.33 163

HQP-NCC					95.86 165		96.65 161		93.55 48	90.14 167

ACMP_Plane					95.86 165		96.65 161		93.55 48	90.14 167

BP-MVS														92.13 101

HQP4-MVS										90.14 167			98.50 156			95.78 196

HQP3-MVS								97.39 134							92.10 183

NP-MVS					95.99 164	89.81 142					95.87 150

ACMMP++_ref															90.30 211

ACMMP++															91.02 201

High-resolution multi-view benchmark