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.

APDe-MVS		95.46 1	95.64 1	94.91 12	98.26 20	86.29 38	97.46 2	97.40 9	89.03 47	96.20 4	98.10 1	89.39 7	99.34 22	95.88 1	99.03 1	99.10 1

MP-MVS-pluss		94.21 24	94.00 26	94.85 16	98.17 24	86.65 24	94.82 95	97.17 24	86.26 109	92.83 38	97.87 2	85.57 36	99.56 2	94.37 6	98.92 6	98.34 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_Plus		94.74 10	94.56 11	95.28 4	98.02 30	87.70 4	95.68 48	97.34 11	88.28 65	95.30 10	97.67 3	85.90 33	99.54 9	93.91 9	98.95 4	98.60 8

ESAPD		95.32 3	95.38 3	95.17 6	98.55 5	87.22 10	95.99 35	97.45 6	88.25 66	96.40 2	97.60 4	91.93 1	99.62 1	93.18 18	99.02 2	98.67 4

MPTG		94.47 12	94.30 14	95.00 9	98.42 14	86.95 12	95.06 81	96.97 34	91.07 14	93.14 34	97.56 5	84.30 49	99.56 2	93.43 13	98.75 16	98.47 14

MTAPA		94.42 17	94.22 17	95.00 9	98.42 14	86.95 12	94.36 135	96.97 34	91.07 14	93.14 34	97.56 5	84.30 49	99.56 2	93.43 13	98.75 16	98.47 14

APD-MVS_3200maxsize		93.78 32	93.77 31	93.80 54	97.92 31	84.19 75	96.30 26	96.87 45	86.96 95	93.92 21	97.47 7	83.88 53	98.96 63	92.71 23	97.87 52	98.26 33

SteuartSystems-ACMMP		95.20 5	95.32 6	94.85 16	96.99 55	86.33 34	97.33 3	97.30 17	91.38 12	95.39 8	97.46 8	88.98 10	99.40 20	94.12 7	98.89 7	98.82 2
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS	88.79 3	93.31 42	92.99 44	94.26 42	96.07 84	85.83 48	94.89 90	96.99 32	89.02 48	89.56 87	97.37 9	82.51 60	99.38 21	92.20 31	98.30 41	97.57 69
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		93.96 29	93.72 32	94.68 27	98.43 13	86.22 39	95.30 60	97.78 1	87.45 84	93.26 31	97.33 10	84.62 47	99.51 13	90.75 59	98.57 34	98.32 25

DeepPCF-MVS	89.96 1	94.20 25	94.77 9	92.49 88	96.52 66	80.00 172	94.00 164	97.08 29	90.05 26	95.65 7	97.29 11	89.66 4	98.97 60	93.95 8	98.71 19	98.50 11

region2R		94.43 15	94.27 16	94.92 11	98.65 1	86.67 23	96.92 14	97.23 21	88.60 58	93.58 27	97.27 12	85.22 39	99.54 9	92.21 30	98.74 18	98.56 10

SD-MVS		94.96 7	95.33 5	93.88 49	97.25 52	86.69 21	96.19 29	97.11 28	90.42 24	96.95 1	97.27 12	89.53 5	96.91 214	94.38 5	98.85 8	98.03 49

ACMMPR		94.43 15	94.28 15	94.91 12	98.63 2	86.69 21	96.94 10	97.32 16	88.63 56	93.53 30	97.26 14	85.04 42	99.54 9	92.35 28	98.78 13	98.50 11

CP-MVS		94.34 18	94.21 19	94.74 26	98.39 16	86.64 25	97.60 1	97.24 19	88.53 60	92.73 43	97.23 15	85.20 40	99.32 26	92.15 33	98.83 10	98.25 34

abl_6		93.18 49	93.05 42	93.57 58	97.52 38	84.27 74	95.53 55	96.67 61	87.85 75	93.20 33	97.22 16	80.35 81	99.18 33	91.91 41	97.21 62	97.26 75

HFP-MVS		94.52 11	94.40 12	94.86 14	98.61 3	86.81 16	96.94 10	97.34 11	88.63 56	93.65 23	97.21 17	86.10 29	99.49 15	92.35 28	98.77 14	98.30 26

#test#		94.32 20	94.14 21	94.86 14	98.61 3	86.81 16	96.43 23	97.34 11	87.51 83	93.65 23	97.21 17	86.10 29	99.49 15	91.68 46	98.77 14	98.30 26

MP-MVS		94.25 21	94.07 24	94.77 23	98.47 11	86.31 36	96.71 20	96.98 33	89.04 46	91.98 60	97.19 19	85.43 37	99.56 2	92.06 36	98.79 11	98.44 19

APD-MVS		94.24 22	94.07 24	94.75 25	98.06 28	86.90 15	95.88 40	96.94 39	85.68 119	95.05 11	97.18 20	87.31 19	99.07 43	91.90 44	98.61 33	98.28 28
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS		93.99 28	93.78 30	94.63 29	98.50 9	85.90 47	96.87 16	96.91 41	88.70 54	91.83 64	97.17 21	83.96 52	99.55 6	91.44 50	98.64 31	98.43 20

XVS		94.45 13	94.32 13	94.85 16	98.54 7	86.60 26	96.93 12	97.19 22	90.66 22	92.85 36	97.16 22	85.02 43	99.49 15	91.99 37	98.56 35	98.47 14

HPM-MVS_fast		93.40 41	93.22 39	93.94 48	98.36 18	84.83 57	97.15 7	96.80 49	85.77 116	92.47 51	97.13 23	82.38 61	99.07 43	90.51 61	98.40 39	97.92 57

CNVR-MVS		95.40 2	95.37 4	95.50 3	98.11 25	88.51 3	95.29 62	96.96 37	92.09 3	95.32 9	97.08 24	89.49 6	99.33 25	95.10 2	98.85 8	98.66 6

ACMMP		93.24 47	92.88 48	94.30 41	98.09 27	85.33 53	96.86 17	97.45 6	88.33 63	90.15 83	97.03 25	81.44 74	99.51 13	90.85 58	95.74 83	98.04 48
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
DeepC-MVS_fast	89.43 2	94.04 26	93.79 29	94.80 22	97.48 41	86.78 18	95.65 52	96.89 42	89.40 38	92.81 39	96.97 26	85.37 38	99.24 30	90.87 57	98.69 21	98.38 22
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		94.85 8	94.94 7	94.58 31	98.25 21	86.33 34	96.11 31	96.62 65	88.14 69	96.10 5	96.96 27	89.09 9	98.94 64	94.48 4	98.68 24	98.48 13

MSLP-MVS++		93.72 33	94.08 23	92.65 82	97.31 46	83.43 91	95.79 43	97.33 14	90.03 27	93.58 27	96.96 27	84.87 45	97.76 137	92.19 32	98.66 28	96.76 94

VDDNet		89.56 103	88.49 114	92.76 80	95.07 120	82.09 123	96.30 26	93.19 228	81.05 231	91.88 61	96.86 29	61.16 292	98.33 98	88.43 76	92.49 137	97.84 60

VDD-MVS		90.74 77	89.92 85	93.20 62	96.27 71	83.02 102	95.73 45	93.86 218	88.42 62	92.53 48	96.84 30	62.09 283	98.64 81	90.95 56	92.62 136	97.93 56

HPM-MVS++		95.14 6	94.91 8	95.83 1	98.25 21	89.65 1	95.92 39	96.96 37	91.75 8	94.02 19	96.83 31	88.12 11	99.55 6	93.41 15	98.94 5	98.28 28

旧先验1					96.79 59	81.81 127		95.67 123			96.81 32	86.69 24			97.66 56	96.97 88

LFMVS		90.08 90	89.13 99	92.95 73	96.71 60	82.32 121	96.08 32	89.91 303	86.79 100	92.15 58	96.81 32	62.60 280	98.34 97	87.18 93	93.90 112	98.19 36

HPM-MVS		94.02 27	93.88 27	94.43 37	98.39 16	85.78 49	97.25 5	97.07 30	86.90 99	92.62 47	96.80 34	84.85 46	99.17 34	92.43 25	98.65 30	98.33 24

Regformer-3		93.68 34	93.64 34	93.81 53	95.36 106	84.61 60	94.68 106	95.83 113	91.27 13	93.60 26	96.71 35	85.75 34	98.86 69	92.87 20	96.65 72	97.96 52

Regformer-4		93.91 30	93.81 28	94.19 44	95.36 106	85.47 51	94.68 106	96.41 75	91.60 11	93.75 22	96.71 35	85.95 32	99.10 42	93.21 17	96.65 72	98.01 51

HSP-MVS		95.30 4	95.48 2	94.76 24	98.49 10	86.52 28	96.91 15	96.73 54	91.73 9	96.10 5	96.69 37	89.90 3	99.30 28	94.70 3	98.04 49	98.45 18

testdata				90.49 161	96.40 67	77.89 241		95.37 153	72.51 303	93.63 25	96.69 37	82.08 68	97.65 142	83.08 136	97.39 60	95.94 118

EI-MVSNet-Vis-set		93.01 51	92.92 46	93.29 59	95.01 121	83.51 90	94.48 117	95.77 117	90.87 16	92.52 49	96.67 39	84.50 48	99.00 57	91.99 37	94.44 107	97.36 74

3Dnovator	86.66 5	91.73 63	90.82 71	94.44 35	94.59 139	86.37 32	97.18 6	97.02 31	89.20 42	84.31 206	96.66 40	73.74 169	99.17 34	86.74 99	97.96 50	97.79 63

CDPH-MVS		92.83 52	92.30 54	94.44 35	97.79 33	86.11 42	94.06 159	96.66 62	80.09 237	92.77 40	96.63 41	86.62 25	99.04 48	87.40 89	98.66 28	98.17 37

3Dnovator+	87.14 4	92.42 56	91.37 60	95.55 2	95.63 98	88.73 2	97.07 8	96.77 52	90.84 17	84.02 210	96.62 42	75.95 135	99.34 22	87.77 84	97.68 55	98.59 9

EI-MVSNet-UG-set		92.74 54	92.62 50	93.12 65	94.86 129	83.20 96	94.40 125	95.74 120	90.71 21	92.05 59	96.60 43	84.00 51	98.99 58	91.55 47	93.63 116	97.17 81

NCCC		94.81 9	94.69 10	95.17 6	97.83 32	87.46 9	95.66 50	96.93 40	92.34 2	93.94 20	96.58 44	87.74 14	99.44 19	92.83 21	98.40 39	98.62 7

Vis-MVSNet		91.75 62	91.23 63	93.29 59	95.32 109	83.78 82	96.14 30	95.98 101	89.89 29	90.45 79	96.58 44	75.09 150	98.31 100	84.75 117	96.90 66	97.78 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Regformer-1		94.22 23	94.13 22	94.51 34	95.54 100	86.36 33	94.57 114	96.44 72	91.69 10	94.32 14	96.56 46	87.05 22	99.03 49	93.35 16	97.65 57	98.15 39

Regformer-2		94.33 19	94.22 17	94.68 27	95.54 100	86.75 20	94.57 114	96.70 58	91.84 6	94.41 12	96.56 46	87.19 20	99.13 39	93.50 11	97.65 57	98.16 38

UA-Net		92.83 52	92.54 52	93.68 56	96.10 82	84.71 59	95.66 50	96.39 77	91.92 4	93.22 32	96.49 48	83.16 55	98.87 66	84.47 120	95.47 88	97.45 73

MG-MVS		91.77 61	91.70 58	92.00 106	97.08 54	80.03 171	93.60 188	95.18 167	87.85 75	90.89 76	96.47 49	82.06 69	98.36 94	85.07 111	97.04 65	97.62 66

CPTT-MVS		91.99 58	91.80 57	92.55 85	98.24 23	81.98 126	96.76 19	96.49 71	81.89 211	90.24 81	96.44 50	78.59 101	98.61 84	89.68 65	97.85 53	97.06 86

test_prior3		93.60 36	93.53 35	93.82 51	97.29 48	84.49 64	94.12 148	96.88 43	87.67 80	92.63 45	96.39 51	86.62 25	98.87 66	91.50 48	98.67 26	98.11 43

test_prior2							94.12 148		87.67 80	92.63 45	96.39 51	86.62 25		91.50 48	98.67 26

MCST-MVS		94.45 13	94.20 20	95.19 5	98.46 12	87.50 8	95.00 84	97.12 26	87.13 88	92.51 50	96.30 53	89.24 8	99.34 22	93.46 12	98.62 32	98.73 3

PHI-MVS		93.89 31	93.65 33	94.62 30	96.84 58	86.43 31	96.69 21	97.49 4	85.15 130	93.56 29	96.28 54	85.60 35	99.31 27	92.45 24	98.79 11	98.12 42

新几何1				93.10 66	97.30 47	84.35 73		95.56 131	71.09 313	91.26 72	96.24 55	82.87 58	98.86 69	79.19 204	98.10 47	96.07 114

agg_prior1		93.29 43	92.97 45	94.26 42	97.38 43	85.92 44	93.92 167	96.72 56	81.96 206	92.16 56	96.23 56	87.85 12	98.97 60	91.95 40	98.55 37	97.90 58

1121		90.42 85	89.49 89	93.20 62	97.27 50	84.46 67	92.63 223	95.51 138	71.01 314	91.20 73	96.21 57	82.92 57	99.05 45	80.56 176	98.07 48	96.10 112

TEST9					97.53 36	86.49 29	94.07 156	96.78 50	81.61 223	92.77 40	96.20 58	87.71 15	99.12 40

train_agg		93.44 39	93.08 41	94.52 33	97.53 36	86.49 29	94.07 156	96.78 50	81.86 218	92.77 40	96.20 58	87.63 16	99.12 40	92.14 34	98.69 21	97.94 53

test_8					97.49 39	86.30 37	94.02 162	96.76 53	81.86 218	92.70 44	96.20 58	87.63 16	99.02 52

QAPM		89.51 104	88.15 124	93.59 57	94.92 126	84.58 61	96.82 18	96.70 58	78.43 255	83.41 224	96.19 61	73.18 176	99.30 28	77.11 224	96.54 75	96.89 92

MVS_0304		93.25 46	92.62 50	95.14 8	95.72 95	87.58 7	94.71 105	96.59 67	91.78 7	91.46 69	96.18 62	75.45 146	99.55 6	93.53 10	98.19 44	98.28 28

test222					96.55 65	81.70 128	92.22 237	95.01 172	68.36 320	90.20 82	96.14 63	80.26 84			97.80 54	96.05 116

OMC-MVS		91.23 70	90.62 73	93.08 67	96.27 71	84.07 77	93.52 190	95.93 104	86.95 96	89.51 88	96.13 64	78.50 103	98.35 96	85.84 106	92.90 133	96.83 93

OpenMVS	83.78 11	88.74 128	87.29 139	93.08 67	92.70 198	85.39 52	96.57 22	96.43 74	78.74 252	80.85 254	96.07 65	69.64 220	99.01 54	78.01 215	96.65 72	94.83 161

agg_prior3		93.27 44	92.89 47	94.40 39	97.49 39	86.12 41	94.07 156	96.73 54	81.46 226	92.46 52	96.05 66	86.90 23	99.15 37	92.14 34	98.69 21	97.94 53

IS-MVSNet		91.43 67	91.09 66	92.46 89	95.87 92	81.38 137	96.95 9	93.69 222	89.72 34	89.50 89	95.98 67	78.57 102	97.77 136	83.02 138	96.50 76	98.22 35

LS3D		87.89 149	86.32 178	92.59 84	96.07 84	82.92 106	95.23 70	94.92 180	75.66 277	82.89 229	95.98 67	72.48 186	99.21 31	68.43 285	95.23 94	95.64 131

原ACMM1				92.01 104	97.34 45	81.05 146		96.81 48	78.89 247	90.45 79	95.92 69	82.65 59	98.84 74	80.68 174	98.26 43	96.14 108

VNet		92.24 57	91.91 56	93.24 61	96.59 63	83.43 91	94.84 94	96.44 72	89.19 43	94.08 18	95.90 70	77.85 112	98.17 104	88.90 71	93.38 123	98.13 41

CANet		93.54 37	93.20 40	94.55 32	95.65 97	85.73 50	94.94 87	96.69 60	91.89 5	90.69 77	95.88 71	81.99 71	99.54 9	93.14 19	97.95 51	98.39 21

MVS_111021_HR		93.45 38	93.31 37	93.84 50	96.99 55	84.84 56	93.24 204	97.24 19	88.76 53	91.60 68	95.85 72	86.07 31	98.66 79	91.91 41	98.16 45	98.03 49

DP-MVS Recon		91.95 59	91.28 62	93.96 47	98.33 19	85.92 44	94.66 109	96.66 62	82.69 195	90.03 85	95.82 73	82.30 63	99.03 49	84.57 119	96.48 77	96.91 90

EPNet		91.79 60	91.02 67	94.10 45	90.10 287	85.25 54	96.03 34	92.05 250	92.83 1	87.39 121	95.78 74	79.39 95	99.01 54	88.13 80	97.48 59	98.05 47
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVG-OURS		89.40 112	88.70 108	91.52 124	94.06 155	81.46 134	91.27 257	96.07 96	86.14 112	88.89 95	95.77 75	68.73 239	97.26 187	87.39 90	89.96 166	95.83 124

XVG-OURS-SEG-HR		89.95 94	89.45 90	91.47 126	94.00 161	81.21 142	91.87 244	96.06 98	85.78 115	88.55 97	95.73 76	74.67 154	97.27 185	88.71 73	89.64 171	95.91 119

MVS_111021_LR		92.47 55	92.29 55	92.98 72	95.99 87	84.43 71	93.08 209	96.09 94	88.20 68	91.12 74	95.72 77	81.33 76	97.76 137	91.74 45	97.37 61	96.75 95

CSCG		93.23 48	93.05 42	93.76 55	98.04 29	84.07 77	96.22 28	97.37 10	84.15 150	90.05 84	95.66 78	87.77 13	99.15 37	89.91 64	98.27 42	98.07 45

EPP-MVSNet		91.70 64	91.56 59	92.13 103	95.88 90	80.50 162	97.33 3	95.25 160	86.15 111	89.76 86	95.60 79	83.42 54	98.32 99	87.37 91	93.25 126	97.56 70

TSAR-MVS + GP.		93.66 35	93.41 36	94.41 38	96.59 63	86.78 18	94.40 125	93.93 217	89.77 32	94.21 15	95.59 80	87.35 18	98.61 84	92.72 22	96.15 80	97.83 61

TAPA-MVS	84.62 6	88.16 140	87.01 151	91.62 122	96.64 61	80.65 156	94.39 127	96.21 89	76.38 270	86.19 143	95.44 81	79.75 88	98.08 121	62.75 313	95.29 92	96.13 109
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		90.12 89	89.56 88	91.82 116	93.14 186	83.90 79	94.16 147	95.74 120	88.96 49	87.86 107	95.43 82	72.48 186	97.91 132	88.10 81	90.18 163	93.65 226

Vis-MVSNet (Re-imp)		89.59 102	89.44 91	90.03 190	95.74 94	75.85 269	95.61 53	90.80 287	87.66 82	87.83 113	95.40 83	76.79 117	96.46 239	78.37 209	96.73 69	97.80 62

EI-MVSNet		89.10 117	88.86 107	89.80 200	91.84 212	78.30 230	93.70 183	95.01 172	85.73 117	87.15 123	95.28 84	79.87 87	97.21 193	83.81 132	87.36 207	93.88 206

CVMVSNet		84.69 246	84.79 214	84.37 302	91.84 212	64.92 326	93.70 183	91.47 269	66.19 326	86.16 144	95.28 84	67.18 257	93.33 309	80.89 171	90.42 158	94.88 159

114514_t		89.51 104	88.50 112	92.54 86	98.11 25	81.99 125	95.16 75	96.36 79	70.19 316	85.81 147	95.25 86	76.70 118	98.63 82	82.07 153	96.86 68	97.00 87

RPSCF		85.07 230	84.27 224	87.48 270	92.91 195	70.62 308	91.69 250	92.46 241	76.20 274	82.67 232	95.22 87	63.94 277	97.29 184	77.51 220	85.80 218	94.53 176

LPG-MVS_test		89.45 107	88.90 105	91.12 135	94.47 143	81.49 132	95.30 60	96.14 90	86.73 101	85.45 168	95.16 88	69.89 216	98.10 113	87.70 85	89.23 178	93.77 216

LGP-MVS_train				91.12 135	94.47 143	81.49 132		96.14 90	86.73 101	85.45 168	95.16 88	69.89 216	98.10 113	87.70 85	89.23 178	93.77 216

CNLPA		89.07 118	87.98 127	92.34 95	96.87 57	84.78 58	94.08 154	93.24 227	81.41 227	84.46 198	95.13 90	75.57 143	96.62 229	77.21 222	93.84 114	95.61 132

DELS-MVS		93.43 40	93.25 38	93.97 46	95.42 105	85.04 55	93.06 211	97.13 25	90.74 20	91.84 62	95.09 91	86.32 28	99.21 31	91.22 51	98.45 38	97.65 65
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
DP-MVS		87.25 181	85.36 201	92.90 75	97.65 34	83.24 95	94.81 96	92.00 252	74.99 283	81.92 243	95.00 92	72.66 182	99.05 45	66.92 294	92.33 138	96.40 101

MVSFormer		91.68 65	91.30 61	92.80 78	93.86 166	83.88 80	95.96 37	95.90 108	84.66 139	91.76 65	94.91 93	77.92 109	97.30 181	89.64 66	97.11 63	97.24 76

jason		90.80 76	90.10 80	92.90 75	93.04 190	83.53 89	93.08 209	94.15 204	80.22 235	91.41 70	94.91 93	76.87 115	97.93 131	90.28 63	96.90 66	97.24 76
jason: jason.
alignmvs		93.08 50	92.50 53	94.81 21	95.62 99	87.61 6	95.99 35	96.07 96	89.77 32	94.12 16	94.87 95	80.56 80	98.66 79	92.42 26	93.10 129	98.15 39

HQP_MVS		90.60 83	90.19 78	91.82 116	94.70 135	82.73 112	95.85 41	96.22 86	90.81 18	86.91 128	94.86 96	74.23 158	98.12 107	88.15 78	89.99 164	94.63 168

plane_prior4											94.86 96

nrg030		91.08 74	90.39 74	93.17 64	93.07 188	86.91 14	96.41 24	96.26 82	88.30 64	88.37 100	94.85 98	82.19 66	97.64 144	91.09 52	82.95 244	94.96 150

BH-RMVSNet		88.37 134	87.48 134	91.02 142	95.28 110	79.45 188	92.89 217	93.07 230	85.45 123	86.91 128	94.84 99	70.35 212	97.76 137	73.97 249	94.59 101	95.85 122

PAPM_NR		91.22 71	90.78 72	92.52 87	97.60 35	81.46 134	94.37 131	96.24 85	86.39 107	87.41 119	94.80 100	82.06 69	98.48 90	82.80 142	95.37 90	97.61 67

FIs		90.51 84	90.35 75	90.99 144	93.99 162	80.98 148	95.73 45	97.54 3	89.15 44	86.72 132	94.68 101	81.83 73	97.24 189	85.18 110	88.31 198	94.76 164

FC-MVSNet-test		90.27 87	90.18 79	90.53 153	93.71 172	79.85 176	95.77 44	97.59 2	89.31 40	86.27 141	94.67 102	81.93 72	97.01 206	84.26 125	88.09 201	94.71 165

AdaColmap		89.89 97	89.07 100	92.37 94	97.41 42	83.03 101	94.42 124	95.92 105	82.81 191	86.34 140	94.65 103	73.89 165	99.02 52	80.69 173	95.51 86	95.05 144

F-COLMAP		87.95 148	86.80 158	91.40 128	96.35 70	80.88 152	94.73 100	95.45 145	79.65 242	82.04 241	94.61 104	71.13 198	98.50 89	76.24 231	91.05 149	94.80 163

canonicalmvs		93.27 44	92.75 49	94.85 16	95.70 96	87.66 5	96.33 25	96.41 75	90.00 28	94.09 17	94.60 105	82.33 62	98.62 83	92.40 27	92.86 134	98.27 31

VPNet		88.20 139	87.47 135	90.39 166	93.56 176	79.46 186	94.04 160	95.54 134	88.67 55	86.96 126	94.58 106	69.33 223	97.15 195	84.05 129	80.53 281	94.56 175

PVSNet_Blended_VisFu		91.38 68	90.91 69	92.80 78	96.39 68	83.17 97	94.87 93	96.66 62	83.29 172	89.27 90	94.46 107	80.29 83	99.17 34	87.57 87	95.37 90	96.05 116

ACMM	84.12 9	89.14 116	88.48 115	91.12 135	94.65 138	81.22 141	95.31 58	96.12 93	85.31 126	85.92 146	94.34 108	70.19 215	98.06 123	85.65 107	88.86 188	94.08 197
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PCF-MVS	84.11 10	87.74 157	86.08 185	92.70 81	94.02 157	84.43 71	89.27 280	95.87 111	73.62 293	84.43 200	94.33 109	78.48 104	98.86 69	70.27 266	94.45 106	94.81 162
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WTY-MVS		89.60 101	88.92 104	91.67 121	95.47 104	81.15 144	92.38 232	94.78 187	83.11 175	89.06 94	94.32 110	78.67 100	96.61 231	81.57 162	90.89 155	97.24 76

ACMP	84.23 8	89.01 123	88.35 116	90.99 144	94.73 132	81.27 138	95.07 79	95.89 110	86.48 104	83.67 218	94.30 111	69.33 223	97.99 127	87.10 98	88.55 190	93.72 220
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cdsmvs_eth3d_5k		22.14 327	29.52 328	0.00 343	0.00 357	0.00 358	0.00 348	95.76 118	0.00 352	0.00 354	94.29 112	75.66 142	0.00 355	0.00 352	0.00 354	0.00 354

PS-MVSNAJss		89.97 93	89.62 87	91.02 142	91.90 210	80.85 153	95.26 69	95.98 101	86.26 109	86.21 142	94.29 112	79.70 90	97.65 142	88.87 72	88.10 199	94.57 174

lupinMVS		90.92 75	90.21 77	93.03 70	93.86 166	83.88 80	92.81 218	93.86 218	79.84 239	91.76 65	94.29 112	77.92 109	98.04 124	90.48 62	97.11 63	97.17 81

API-MVS		90.66 79	90.07 81	92.45 90	96.36 69	84.57 62	96.06 33	95.22 166	82.39 197	89.13 91	94.27 115	80.32 82	98.46 91	80.16 185	96.71 70	94.33 186

CANet_DTU		90.26 88	89.41 92	92.81 77	93.46 178	83.01 103	93.48 191	94.47 194	89.43 37	87.76 116	94.23 116	70.54 211	99.03 49	84.97 112	96.39 78	96.38 102

PLC	84.53 7	89.06 120	88.03 126	92.15 101	97.27 50	82.69 115	94.29 136	95.44 147	79.71 241	84.01 211	94.18 117	76.68 119	98.75 77	77.28 221	93.41 122	95.02 145
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
xiu_mvs_v1_base_debu		90.64 80	90.05 82	92.40 91	93.97 163	84.46 67	93.32 195	95.46 141	85.17 127	92.25 53	94.03 118	70.59 207	98.57 86	90.97 53	94.67 97	94.18 189

xiu_mvs_v1_base		90.64 80	90.05 82	92.40 91	93.97 163	84.46 67	93.32 195	95.46 141	85.17 127	92.25 53	94.03 118	70.59 207	98.57 86	90.97 53	94.67 97	94.18 189

xiu_mvs_v1_base_debi		90.64 80	90.05 82	92.40 91	93.97 163	84.46 67	93.32 195	95.46 141	85.17 127	92.25 53	94.03 118	70.59 207	98.57 86	90.97 53	94.67 97	94.18 189

jajsoiax		88.24 138	87.50 133	90.48 162	90.89 266	80.14 166	95.31 58	95.65 127	84.97 133	84.24 208	94.02 121	65.31 270	97.42 167	88.56 74	88.52 192	93.89 204

XXY-MVS		87.65 159	86.85 155	90.03 190	92.14 206	80.60 159	93.76 176	95.23 164	82.94 187	84.60 194	94.02 121	74.27 157	95.49 277	81.04 166	83.68 237	94.01 201

NP-MVS					94.37 147	82.42 119					93.98 123

HQP-MVS		89.80 98	89.28 96	91.34 129	94.17 152	81.56 129	94.39 127	96.04 99	88.81 50	85.43 171	93.97 124	73.83 167	97.96 128	87.11 96	89.77 169	94.50 179

mvs_tets		88.06 144	87.28 140	90.38 168	90.94 262	79.88 174	95.22 71	95.66 125	85.10 131	84.21 209	93.94 125	63.53 278	97.40 174	88.50 75	88.40 197	93.87 207

CHOSEN 1792x2688		88.84 125	87.69 131	92.30 96	96.14 77	81.42 136	90.01 269	95.86 112	74.52 288	87.41 119	93.94 125	75.46 145	98.36 94	80.36 180	95.53 85	97.12 84

UGNet		89.95 94	88.95 103	92.95 73	94.51 142	83.31 94	95.70 47	95.23 164	89.37 39	87.58 118	93.94 125	64.00 276	98.78 76	83.92 130	96.31 79	96.74 96
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
TAMVS		89.21 115	88.29 121	91.96 108	93.71 172	82.62 117	93.30 199	94.19 202	82.22 200	87.78 115	93.94 125	78.83 97	96.95 211	77.70 217	92.98 131	96.32 103

sss		88.93 124	88.26 123	90.94 146	94.05 156	80.78 155	91.71 248	95.38 151	81.55 224	88.63 96	93.91 129	75.04 151	95.47 278	82.47 147	91.61 141	96.57 99

1112_ss		88.42 132	87.33 138	91.72 119	94.92 126	80.98 148	92.97 215	94.54 192	78.16 260	83.82 214	93.88 130	78.78 98	97.91 132	79.45 199	89.41 173	96.26 105

ab-mvs-re		7.82 331	10.43 332	0.00 343	0.00 357	0.00 358	0.00 348	0.00 359	0.00 352	0.00 354	93.88 130	0.00 361	0.00 355	0.00 352	0.00 354	0.00 354

TranMVSNet+NR-MVSNet		88.84 125	87.95 128	91.49 125	92.68 199	83.01 103	94.92 89	96.31 80	89.88 30	85.53 162	93.85 132	76.63 120	96.96 210	81.91 157	79.87 291	94.50 179

mvs_anonymous		89.37 113	89.32 94	89.51 211	93.47 177	74.22 275	91.65 251	94.83 185	82.91 189	85.45 168	93.79 133	81.23 77	96.36 244	86.47 105	94.09 110	97.94 53

MVS_Test		91.31 69	91.11 64	91.93 110	94.37 147	80.14 166	93.46 193	95.80 115	86.46 105	91.35 71	93.77 134	82.21 65	98.09 120	87.57 87	94.95 95	97.55 71

COLMAP_ROB	80.39 16	83.96 252	82.04 258	89.74 201	95.28 110	79.75 178	94.25 138	92.28 244	75.17 281	78.02 278	93.77 134	58.60 303	97.84 134	65.06 306	85.92 216	91.63 284
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PAPR		90.02 91	89.27 97	92.29 97	95.78 93	80.95 150	92.68 222	96.22 86	81.91 209	86.66 133	93.75 136	82.23 64	98.44 93	79.40 203	94.79 96	97.48 72

ab-mvs		89.41 110	88.35 116	92.60 83	95.15 119	82.65 116	92.20 238	95.60 129	83.97 152	88.55 97	93.70 137	74.16 162	98.21 103	82.46 148	89.37 174	96.94 89

BH-untuned		88.60 130	88.13 125	90.01 192	95.24 118	78.50 225	93.29 200	94.15 204	84.75 137	84.46 198	93.40 138	75.76 140	97.40 174	77.59 218	94.52 103	94.12 193

AllTest		83.42 257	81.39 261	89.52 209	95.01 121	77.79 244	93.12 206	90.89 285	77.41 263	76.12 293	93.34 139	54.08 317	97.51 150	68.31 286	84.27 231	93.26 243

TestCases				89.52 209	95.01 121	77.79 244		90.89 285	77.41 263	76.12 293	93.34 139	54.08 317	97.51 150	68.31 286	84.27 231	93.26 243

UniMVSNet_NR-MVSNet		89.92 96	89.29 95	91.81 118	93.39 179	83.72 83	94.43 123	97.12 26	89.80 31	86.46 135	93.32 141	83.16 55	97.23 191	84.92 113	81.02 272	94.49 181

VPA-MVSNet		89.62 100	88.96 102	91.60 123	93.86 166	82.89 107	95.46 56	97.33 14	87.91 72	88.43 99	93.31 142	74.17 161	97.40 174	87.32 92	82.86 246	94.52 177

ITE_SJBPF				88.24 254	91.88 211	77.05 259		92.92 231	85.54 121	80.13 265	93.30 143	57.29 307	96.20 249	72.46 257	84.71 227	91.49 286

DU-MVS		89.34 114	88.50 112	91.85 114	93.04 190	83.72 83	94.47 120	96.59 67	89.50 36	86.46 135	93.29 144	77.25 113	97.23 191	84.92 113	81.02 272	94.59 172

NR-MVSNet		88.58 131	87.47 135	91.93 110	93.04 190	84.16 76	94.77 99	96.25 84	89.05 45	80.04 266	93.29 144	79.02 96	97.05 204	81.71 161	80.05 286	94.59 172

CDS-MVSNet		89.45 107	88.51 111	92.29 97	93.62 174	83.61 88	93.01 212	94.68 189	81.95 207	87.82 114	93.24 146	78.69 99	96.99 207	80.34 181	93.23 127	96.28 104
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM		86.68 195	85.39 200	90.53 153	93.05 189	79.33 201	89.79 273	94.77 188	78.82 249	81.95 242	93.24 146	76.81 116	97.30 181	66.94 292	93.16 128	94.95 157

OurMVSNet-221017-0		85.35 225	84.64 218	87.49 269	90.77 269	72.59 293	94.01 163	94.40 196	84.72 138	79.62 270	93.17 148	61.91 285	96.72 224	81.99 155	81.16 267	93.16 247

PEN-MVS		86.80 191	86.27 180	88.40 249	92.32 204	75.71 270	95.18 73	96.38 78	87.97 70	82.82 230	93.15 149	73.39 174	95.92 259	76.15 232	79.03 294	93.59 233

xiu_mvs_v2_base		91.13 73	90.89 70	91.86 113	94.97 124	82.42 119	92.24 236	95.64 128	86.11 113	91.74 67	93.14 150	79.67 93	98.89 65	89.06 70	95.46 89	94.28 188

MVSTER		88.84 125	88.29 121	90.51 160	92.95 194	80.44 163	93.73 179	95.01 172	84.66 139	87.15 123	93.12 151	72.79 180	97.21 193	87.86 83	87.36 207	93.87 207

Effi-MVS+		91.59 66	91.11 64	93.01 71	94.35 150	83.39 93	94.60 111	95.10 169	87.10 89	90.57 78	93.10 152	81.43 75	98.07 122	89.29 68	94.48 104	97.59 68

PS-CasMVS		87.32 178	86.88 153	88.63 236	92.99 193	76.33 266	95.33 57	96.61 66	88.22 67	83.30 226	93.07 153	73.03 178	95.79 266	78.36 210	81.00 274	93.75 218

DTE-MVSNet		86.11 204	85.48 198	87.98 259	91.65 220	74.92 273	94.93 88	95.75 119	87.36 85	82.26 235	93.04 154	72.85 179	95.82 264	74.04 248	77.46 299	93.20 245

view600		87.62 165	86.65 166	90.53 153	96.19 73	78.52 220	95.29 62	91.09 274	87.08 90	87.84 109	93.03 155	68.86 234	98.11 109	69.44 276	91.02 151	94.96 150

view800		87.62 165	86.65 166	90.53 153	96.19 73	78.52 220	95.29 62	91.09 274	87.08 90	87.84 109	93.03 155	68.86 234	98.11 109	69.44 276	91.02 151	94.96 150

conf0.05thres1000		87.62 165	86.65 166	90.53 153	96.19 73	78.52 220	95.29 62	91.09 274	87.08 90	87.84 109	93.03 155	68.86 234	98.11 109	69.44 276	91.02 151	94.96 150

tfpn		87.62 165	86.65 166	90.53 153	96.19 73	78.52 220	95.29 62	91.09 274	87.08 90	87.84 109	93.03 155	68.86 234	98.11 109	69.44 276	91.02 151	94.96 150

CP-MVSNet		87.63 163	87.26 141	88.74 233	93.12 187	76.59 263	95.29 62	96.58 69	88.43 61	83.49 223	92.98 159	75.28 147	95.83 263	78.97 205	81.15 269	93.79 212

test_djsdf		89.03 121	88.64 109	90.21 173	90.74 271	79.28 202	95.96 37	95.90 108	84.66 139	85.33 180	92.94 160	74.02 164	97.30 181	89.64 66	88.53 191	94.05 198

MAR-MVS		90.30 86	89.37 93	93.07 69	96.61 62	84.48 66	95.68 48	95.67 123	82.36 199	87.85 108	92.85 161	76.63 120	98.80 75	80.01 186	96.68 71	95.91 119
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
testgi		80.94 282	80.20 272	83.18 306	87.96 312	66.29 322	91.28 256	90.70 290	83.70 158	78.12 276	92.84 162	51.37 321	90.82 324	63.34 310	82.46 249	92.43 268

EU-MVSNet		81.32 277	80.95 265	82.42 310	88.50 305	63.67 327	93.32 195	91.33 271	64.02 330	80.57 259	92.83 163	61.21 291	92.27 317	76.34 229	80.38 284	91.32 289

ACMH+	81.04 14	85.05 231	83.46 243	89.82 197	94.66 137	79.37 196	94.44 122	94.12 206	82.19 201	78.04 277	92.82 164	58.23 304	97.54 148	73.77 251	82.90 245	92.54 264

mvs-test1		89.45 107	89.14 98	90.38 168	93.33 180	77.63 250	94.95 86	94.36 197	87.70 78	87.10 125	92.81 165	73.45 172	98.03 125	85.57 108	93.04 130	95.48 134

WR-MVS		88.38 133	87.67 132	90.52 159	93.30 182	80.18 164	93.26 202	95.96 103	88.57 59	85.47 167	92.81 165	76.12 124	96.91 214	81.24 164	82.29 250	94.47 184

HY-MVS	83.01 12	89.03 121	87.94 129	92.29 97	94.86 129	82.77 108	92.08 243	94.49 193	81.52 225	86.93 127	92.79 167	78.32 106	98.23 101	79.93 189	90.55 156	95.88 121

LTVRE_ROB	82.13 13	86.26 203	84.90 211	90.34 171	94.44 146	81.50 131	92.31 234	94.89 181	83.03 182	79.63 269	92.67 168	69.69 219	97.79 135	71.20 262	86.26 215	91.72 283
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
ACMH	80.38 17	85.36 224	83.68 236	90.39 166	94.45 145	80.63 157	94.73 100	94.85 183	82.09 202	77.24 283	92.65 169	60.01 298	97.58 145	72.25 258	84.87 226	92.96 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		86.61 196	85.54 194	89.82 197	91.44 224	80.18 164	95.28 68	94.85 183	83.84 154	81.66 245	92.62 170	72.45 188	96.48 237	79.67 196	78.06 296	92.82 259

PVSNet_Blended		90.73 78	90.32 76	91.98 107	96.12 78	81.25 139	92.55 227	96.83 46	82.04 205	89.10 92	92.56 171	81.04 78	98.85 72	86.72 102	95.91 81	95.84 123

PS-MVSNAJ		91.18 72	90.92 68	91.96 108	95.26 112	82.60 118	92.09 242	95.70 122	86.27 108	91.84 62	92.46 172	79.70 90	98.99 58	89.08 69	95.86 82	94.29 187

diffmvs		89.07 118	88.32 119	91.34 129	93.24 183	79.79 177	92.29 235	94.98 175	80.24 234	87.38 122	92.45 173	78.02 107	97.33 179	83.29 135	92.93 132	96.91 90

CLD-MVS		89.47 106	88.90 105	91.18 134	94.22 151	82.07 124	92.13 240	96.09 94	87.90 73	85.37 178	92.45 173	74.38 156	97.56 147	87.15 94	90.43 157	93.93 202
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TR-MVS		86.78 192	85.76 192	89.82 197	94.37 147	78.41 227	92.47 229	92.83 233	81.11 230	86.36 139	92.40 175	68.73 239	97.48 152	73.75 252	89.85 168	93.57 234

Test_1112_low_res		87.65 159	86.51 174	91.08 138	94.94 125	79.28 202	91.77 245	94.30 200	76.04 275	83.51 222	92.37 176	77.86 111	97.73 141	78.69 208	89.13 185	96.22 106

EPNet_dtu		86.49 200	85.94 189	88.14 257	90.24 285	72.82 287	94.11 150	92.20 246	86.66 103	79.42 271	92.36 177	73.52 170	95.81 265	71.26 261	93.66 115	95.80 126
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet (Re)		89.80 98	89.07 100	92.01 104	93.60 175	84.52 63	94.78 98	97.47 5	89.26 41	86.44 138	92.32 178	82.10 67	97.39 177	84.81 116	80.84 276	94.12 193

thres600view7		87.65 159	86.67 165	90.59 150	96.08 83	78.72 213	94.88 92	91.58 263	87.06 94	88.08 103	92.30 179	68.91 231	98.10 113	70.05 274	91.10 144	94.96 150

conf200view11		87.65 159	86.71 162	90.46 164	96.12 78	78.55 217	95.03 82	91.58 263	87.15 86	88.06 104	92.29 180	68.91 231	98.10 113	70.13 270	91.10 144	94.71 165

thres100view900		87.63 163	86.71 162	90.38 168	96.12 78	78.55 217	95.03 82	91.58 263	87.15 86	88.06 104	92.29 180	68.91 231	98.10 113	70.13 270	91.10 144	94.48 182

PVSNet_BlendedMVS		89.98 92	89.70 86	90.82 147	96.12 78	81.25 139	93.92 167	96.83 46	83.49 166	89.10 92	92.26 182	81.04 78	98.85 72	86.72 102	87.86 203	92.35 272

XVG-ACMP-BASELINE		86.00 208	84.84 213	89.45 213	91.20 249	78.00 237	91.70 249	95.55 132	85.05 132	82.97 228	92.25 183	54.49 315	97.48 152	82.93 139	87.45 206	92.89 255

MVS		87.44 175	86.10 184	91.44 127	92.61 200	83.62 87	92.63 223	95.66 125	67.26 324	81.47 246	92.15 184	77.95 108	98.22 102	79.71 195	95.48 87	92.47 267

anonymousdsp		87.84 151	87.09 146	90.12 181	89.13 298	80.54 160	94.67 108	95.55 132	82.05 203	83.82 214	92.12 185	71.47 196	97.15 195	87.15 94	87.80 204	92.67 261

TransMVSNet (Re)		84.43 249	83.06 250	88.54 245	91.72 216	78.44 226	95.18 73	92.82 234	82.73 193	79.67 268	92.12 185	73.49 171	95.96 258	71.10 265	68.73 328	91.21 291

SixPastTwentyTwo		83.91 253	82.90 252	86.92 281	90.99 258	70.67 307	93.48 191	91.99 253	85.54 121	77.62 281	92.11 187	60.59 294	96.87 216	76.05 233	77.75 297	93.20 245

HyFIR lowres test		88.09 143	86.81 157	91.93 110	96.00 86	80.63 157	90.01 269	95.79 116	73.42 294	87.68 117	92.10 188	73.86 166	97.96 128	80.75 172	91.70 140	97.19 80

Baseline_NR-MVSNet		87.07 187	86.63 172	88.40 249	91.44 224	77.87 242	94.23 140	92.57 240	84.12 151	85.74 153	92.08 189	77.25 113	96.04 253	82.29 151	79.94 289	91.30 290

USDC		82.76 262	81.26 263	87.26 273	91.17 252	74.55 274	89.27 280	93.39 226	78.26 258	75.30 300	92.08 189	54.43 316	96.63 228	71.64 259	85.79 219	90.61 305

v2v482		87.84 151	87.06 149	90.17 174	90.99 258	79.23 208	94.00 164	95.13 168	84.87 134	85.53 162	92.07 191	74.45 155	97.45 156	84.71 118	81.75 262	93.85 210

FMVSNet2		87.19 185	85.82 191	91.30 131	94.01 158	83.67 85	94.79 97	94.94 176	83.57 162	83.88 212	92.05 192	66.59 260	96.51 235	77.56 219	85.01 225	93.73 219

WR-MVS_H		87.80 155	87.37 137	89.10 228	93.23 184	78.12 235	95.61 53	97.30 17	87.90 73	83.72 216	92.01 193	79.65 94	96.01 256	76.36 228	80.54 280	93.16 247

LCM-MVSNet-Re		88.30 137	88.32 119	88.27 252	94.71 134	72.41 295	93.15 205	90.98 282	87.77 77	79.25 272	91.96 194	78.35 105	95.75 267	83.04 137	95.62 84	96.65 97

tfpn1000		86.06 206	84.92 210	89.49 212	95.54 100	77.79 244	94.72 103	89.07 317	82.05 203	85.36 179	91.94 195	68.32 252	96.65 227	67.04 291	90.24 161	94.02 200

MSDG		84.86 238	83.09 249	90.14 180	93.80 169	80.05 169	89.18 283	93.09 229	78.89 247	78.19 275	91.91 196	65.86 269	97.27 185	68.47 283	88.45 194	93.11 250

IterMVS-LS		88.36 135	87.91 130	89.70 204	93.80 169	78.29 231	93.73 179	95.08 171	85.73 117	84.75 192	91.90 197	79.88 86	96.92 213	83.83 131	82.51 248	93.89 204
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet3		87.40 177	86.11 183	91.30 131	93.79 171	83.64 86	94.20 146	94.81 186	83.89 153	84.37 201	91.87 198	68.45 245	96.56 232	78.23 212	85.36 221	93.70 221

tfpn200view9		87.58 171	86.64 170	90.41 165	95.99 87	78.64 215	94.58 112	91.98 254	86.94 97	88.09 101	91.77 199	69.18 228	98.10 113	70.13 270	91.10 144	94.48 182

thres400		87.62 165	86.64 170	90.57 151	95.99 87	78.64 215	94.58 112	91.98 254	86.94 97	88.09 101	91.77 199	69.18 228	98.10 113	70.13 270	91.10 144	94.96 150

pmmvs4		85.43 223	83.86 230	90.16 175	90.02 290	82.97 105	90.27 264	92.67 238	75.93 276	80.73 255	91.74 201	71.05 199	95.73 268	78.85 206	83.46 241	91.78 280

GBi-Net		87.26 179	85.98 187	91.08 138	94.01 158	83.10 98	95.14 76	94.94 176	83.57 162	84.37 201	91.64 202	66.59 260	96.34 245	78.23 212	85.36 221	93.79 212

test1		87.26 179	85.98 187	91.08 138	94.01 158	83.10 98	95.14 76	94.94 176	83.57 162	84.37 201	91.64 202	66.59 260	96.34 245	78.23 212	85.36 221	93.79 212

FMVSNet1		85.85 211	84.11 226	91.08 138	92.81 196	83.10 98	95.14 76	94.94 176	81.64 221	82.68 231	91.64 202	59.01 302	96.34 245	75.37 237	83.78 234	93.79 212

MVP-Stereo		85.97 209	84.86 212	89.32 221	90.92 264	82.19 122	92.11 241	94.19 202	78.76 251	78.77 274	91.63 205	68.38 251	96.56 232	75.01 242	93.95 111	89.20 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		87.51 173	86.57 173	90.34 171	92.42 202	79.74 179	92.63 223	95.35 155	78.35 256	80.14 264	91.62 206	74.05 163	97.15 195	81.05 165	93.53 118	94.12 193

MS-PatchMatch		85.05 231	84.16 225	87.73 263	91.42 228	78.51 224	91.25 258	93.53 223	77.50 262	80.15 263	91.58 207	61.99 284	95.51 274	75.69 234	94.35 109	89.16 316

TDRefinement		79.81 287	77.34 289	87.22 277	79.24 336	75.48 272	93.12 206	92.03 251	76.45 269	75.01 301	91.58 207	49.19 326	96.44 240	70.22 269	69.18 325	89.75 311

PatchMatch-RL		86.77 194	85.54 194	90.47 163	95.88 90	82.71 114	90.54 262	92.31 243	79.82 240	84.32 205	91.57 209	68.77 238	96.39 242	73.16 254	93.48 121	92.32 273

v7		87.75 156	86.96 152	90.12 181	91.20 249	79.50 181	94.28 137	95.46 141	83.45 167	85.75 151	91.56 210	75.13 148	97.43 165	83.60 133	82.18 252	93.42 240

v1neww		87.98 145	87.25 142	90.16 175	91.38 231	79.41 190	94.37 131	95.28 156	84.48 142	85.77 149	91.53 211	76.12 124	97.45 156	84.45 122	81.89 257	93.61 231

v7new		87.98 145	87.25 142	90.16 175	91.38 231	79.41 190	94.37 131	95.28 156	84.48 142	85.77 149	91.53 211	76.12 124	97.45 156	84.45 122	81.89 257	93.61 231

conf0.002		85.83 213	84.54 219	89.71 203	95.26 112	77.63 250	94.21 141	89.33 311	81.89 211	84.94 186	91.51 213	68.43 246	96.80 219	66.05 297	89.23 178	94.71 165

thresconf0.02		85.75 216	84.54 219	89.38 217	95.26 112	77.63 250	94.21 141	89.33 311	81.89 211	84.94 186	91.51 213	68.43 246	96.80 219	66.05 297	89.23 178	93.70 221

tfpn_n400		85.75 216	84.54 219	89.38 217	95.26 112	77.63 250	94.21 141	89.33 311	81.89 211	84.94 186	91.51 213	68.43 246	96.80 219	66.05 297	89.23 178	93.70 221

tfpnconf		85.75 216	84.54 219	89.38 217	95.26 112	77.63 250	94.21 141	89.33 311	81.89 211	84.94 186	91.51 213	68.43 246	96.80 219	66.05 297	89.23 178	93.70 221

tfpnview11		85.75 216	84.54 219	89.38 217	95.26 112	77.63 250	94.21 141	89.33 311	81.89 211	84.94 186	91.51 213	68.43 246	96.80 219	66.05 297	89.23 178	93.70 221

v6		87.98 145	87.25 142	90.16 175	91.36 234	79.39 195	94.37 131	95.27 159	84.48 142	85.78 148	91.51 213	76.15 123	97.46 154	84.46 121	81.88 259	93.62 230

BH-w/o		87.57 172	87.05 150	89.12 226	94.90 128	77.90 240	92.41 230	93.51 224	82.89 190	83.70 217	91.34 219	75.75 141	97.07 202	75.49 235	93.49 119	92.39 270

v8		87.50 174	86.71 162	89.89 195	91.37 233	79.40 194	94.50 116	95.38 151	84.81 136	83.60 220	91.33 220	76.05 128	97.42 167	82.84 141	80.51 283	92.84 257

V42		87.68 158	86.86 154	90.15 179	90.58 276	80.14 166	94.24 139	95.28 156	83.66 159	85.67 157	91.33 220	74.73 153	97.41 172	84.43 124	81.83 260	92.89 255

Fast-Effi-MVS+-dtu		87.44 175	86.72 161	89.63 206	92.04 209	77.68 249	94.03 161	93.94 216	85.81 114	82.42 233	91.32 222	70.33 213	97.06 203	80.33 182	90.23 162	94.14 192

v1144		87.61 170	86.79 159	90.06 189	91.01 257	79.34 198	93.95 166	95.42 150	83.36 171	85.66 158	91.31 223	74.98 152	97.42 167	83.37 134	82.06 253	93.42 240

v1141		87.84 151	87.09 146	90.11 186	91.23 246	79.25 204	94.08 154	95.24 161	84.44 146	85.69 156	91.31 223	75.91 136	97.44 163	84.17 127	81.74 263	93.63 229

divwei89l23v2f112		87.84 151	87.09 146	90.10 188	91.23 246	79.24 206	94.09 152	95.24 161	84.44 146	85.70 154	91.31 223	75.91 136	97.44 163	84.17 127	81.73 264	93.64 227

v1		87.85 150	87.10 145	90.11 186	91.21 248	79.24 206	94.09 152	95.24 161	84.44 146	85.70 154	91.31 223	75.96 134	97.45 156	84.18 126	81.73 264	93.64 227

tfpnnormal		84.72 244	83.23 248	89.20 225	92.79 197	80.05 169	94.48 117	95.81 114	82.38 198	81.08 252	91.21 227	69.01 230	96.95 211	61.69 315	80.59 279	90.58 308

v10		87.25 181	86.38 175	89.85 196	91.19 251	79.50 181	94.48 117	95.45 145	83.79 157	83.62 219	91.19 228	75.13 148	97.42 167	81.94 156	80.60 278	92.63 263

pmmvs5		84.21 250	82.84 254	88.34 251	88.95 301	76.94 260	92.41 230	91.91 258	75.63 278	80.28 261	91.18 229	64.59 274	95.57 271	77.09 225	83.47 240	92.53 265

v1192		87.25 181	86.33 177	90.00 193	90.76 270	79.04 210	93.80 173	95.48 140	82.57 196	85.48 166	91.18 229	73.38 175	97.42 167	82.30 150	82.06 253	93.53 235

v1240		86.78 192	85.85 190	89.56 207	90.45 281	77.79 244	93.61 187	95.37 153	81.65 220	85.43 171	91.15 231	71.50 195	97.43 165	81.47 163	82.05 255	93.47 239

CMPMVS	59.16 21	80.52 283	79.20 281	84.48 301	83.98 324	67.63 320	89.95 271	93.84 220	64.79 329	66.81 327	91.14 232	57.93 306	95.17 289	76.25 230	88.10 199	90.65 304
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
thres200		87.21 184	86.24 181	90.12 181	95.36 106	78.53 219	93.26 202	92.10 247	86.42 106	88.00 106	91.11 233	69.24 227	98.00 126	69.58 275	91.04 150	93.83 211

pmmvs6		83.42 257	81.60 260	88.87 230	88.01 311	77.87 242	94.96 85	94.24 201	74.67 287	78.80 273	91.09 234	60.17 297	96.49 236	77.06 226	75.40 304	92.23 275

v144192		87.19 185	86.35 176	89.74 201	90.64 275	78.24 233	93.92 167	95.43 148	81.93 208	85.51 164	91.05 235	74.21 160	97.45 156	82.86 140	81.56 266	93.53 235

v1921920		86.97 189	86.06 186	89.69 205	90.53 280	78.11 236	93.80 173	95.43 148	81.90 210	85.33 180	91.05 235	72.66 182	97.41 172	82.05 154	81.80 261	93.53 235

v7n		86.81 190	85.76 192	89.95 194	90.72 272	79.25 204	95.07 79	95.92 105	84.45 145	82.29 234	90.86 237	72.60 184	97.53 149	79.42 202	80.52 282	93.08 252

v148		87.04 188	86.32 178	89.21 224	90.94 262	77.26 257	93.71 182	94.43 195	84.84 135	84.36 204	90.80 238	76.04 130	97.05 204	82.12 152	79.60 292	93.31 242

PMMVS		85.71 221	84.96 208	87.95 260	88.90 302	77.09 258	88.68 288	90.06 299	72.32 304	86.47 134	90.76 239	72.15 189	94.40 298	81.78 160	93.49 119	92.36 271

Fast-Effi-MVS+		89.41 110	88.64 109	91.71 120	94.74 131	80.81 154	93.54 189	95.10 169	83.11 175	86.82 131	90.67 240	79.74 89	97.75 140	80.51 178	93.55 117	96.57 99

DI_MVS_plusplus_test		88.15 141	86.82 156	92.14 102	90.67 274	81.07 145	93.01 212	94.59 191	83.83 156	77.78 279	90.63 241	68.51 242	98.16 105	88.02 82	94.37 108	97.17 81

V4		86.50 198	85.54 194	89.39 215	89.13 298	78.99 211	94.73 100	95.54 134	83.59 160	82.10 238	90.61 242	71.60 192	97.45 156	82.52 144	80.01 287	91.74 281

v52		86.50 198	85.53 197	89.39 215	89.17 297	78.99 211	94.72 103	95.54 134	83.59 160	82.10 238	90.60 243	71.59 193	97.45 156	82.52 144	79.99 288	91.73 282

tfpn_ndepth		86.10 205	84.98 206	89.43 214	95.52 103	78.29 231	94.62 110	89.60 309	81.88 217	85.43 171	90.54 244	68.47 244	96.85 218	68.46 284	90.34 160	93.15 249

semantic-postprocess				88.18 256	91.71 217	76.87 261		92.65 239	85.40 124	81.44 247	90.54 244	66.21 264	95.00 294	81.04 166	81.05 270	92.66 262

PVSNet	78.82 18	85.55 222	84.65 217	88.23 255	94.72 133	71.93 296	87.12 301	92.75 236	78.80 250	84.95 185	90.53 246	64.43 275	96.71 226	74.74 243	93.86 113	96.06 115

test_normal		88.13 142	86.78 160	92.18 100	90.55 279	81.19 143	92.74 220	94.64 190	83.84 154	77.49 282	90.51 247	68.49 243	98.16 105	88.22 77	94.55 102	97.21 79

v748		86.27 202	85.28 202	89.25 223	90.26 284	77.58 256	94.89 90	95.50 139	84.28 149	81.41 248	90.46 248	72.57 185	97.32 180	79.81 194	78.36 295	92.84 257

IterMVS		84.88 237	83.98 229	87.60 265	91.44 224	76.03 268	90.18 267	92.41 242	83.24 174	81.06 253	90.42 249	66.60 259	94.28 299	79.46 198	80.98 275	92.48 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_0402		81.30 278	79.17 282	87.67 264	93.19 185	78.17 234	92.98 214	91.71 259	75.25 280	76.02 296	90.31 250	59.23 301	96.37 243	50.22 331	83.63 238	88.47 326

TinyColmap		79.76 288	77.69 288	85.97 290	91.71 217	73.12 284	89.55 274	90.36 293	75.03 282	72.03 316	90.19 251	46.22 331	96.19 250	63.11 311	81.03 271	88.59 322

EG-PatchMatch MVS		82.37 267	80.34 269	88.46 247	90.27 283	79.35 197	92.80 219	94.33 199	77.14 267	73.26 311	90.18 252	47.47 329	96.72 224	70.25 267	87.32 209	89.30 313

lessismore_v0				86.04 289	88.46 306	68.78 316		80.59 342		73.01 312	90.11 253	55.39 312	96.43 241	75.06 241	65.06 330	92.90 254

LF4IMVS		80.37 284	79.07 284	84.27 304	86.64 316	69.87 313	89.39 279	91.05 280	76.38 270	74.97 302	90.00 254	47.85 328	94.25 300	74.55 246	80.82 277	88.69 321

CostFormer		85.77 215	84.94 209	88.26 253	91.16 254	72.58 294	89.47 278	91.04 281	76.26 273	86.45 137	89.97 255	70.74 205	96.86 217	82.35 149	87.07 212	95.34 140

test20.03		79.95 286	79.08 283	82.55 309	85.79 318	67.74 319	91.09 261	91.08 278	81.23 229	74.48 305	89.96 256	61.63 286	90.15 325	60.08 319	76.38 301	89.76 310

tpm		84.73 243	84.02 227	86.87 284	90.33 282	68.90 315	89.06 284	89.94 302	80.85 232	85.75 151	89.86 257	68.54 241	95.97 257	77.76 216	84.05 233	95.75 128

test0.0.03 1		82.41 266	81.69 259	84.59 300	88.23 307	72.89 286	90.24 265	87.83 325	83.41 168	79.86 267	89.78 258	67.25 255	88.99 327	65.18 305	83.42 242	91.90 279

K. test v3		81.59 272	80.15 273	85.91 291	89.89 293	69.42 314	92.57 226	87.71 326	85.56 120	73.44 309	89.71 259	55.58 310	95.52 273	77.17 223	69.76 324	92.78 260

CHOSEN 280x420		85.15 229	83.99 228	88.65 235	92.47 201	78.40 228	79.68 333	92.76 235	74.90 285	81.41 248	89.59 260	69.85 218	95.51 274	79.92 190	95.29 92	92.03 277

GA-MVS		86.61 196	85.27 203	90.66 149	91.33 239	78.71 214	90.40 263	93.81 221	85.34 125	85.12 182	89.57 261	61.25 289	97.11 199	80.99 169	89.59 172	96.15 107

Effi-MVS+-dtu		88.65 129	88.35 116	89.54 208	93.33 180	76.39 264	94.47 120	94.36 197	87.70 78	85.43 171	89.56 262	73.45 172	97.26 187	85.57 108	91.28 143	94.97 147

tpm2		84.08 251	82.94 251	87.48 270	91.39 230	71.27 300	89.23 282	90.37 292	71.95 307	84.64 193	89.33 263	67.30 254	96.55 234	75.17 239	87.09 211	94.63 168

Anonymous20231206		81.03 280	79.77 276	84.82 299	87.85 314	70.26 310	91.42 254	92.08 249	73.67 292	77.75 280	89.25 264	62.43 282	93.08 313	61.50 316	82.00 256	91.12 293

tpmp4_e23		83.87 255	82.33 256	88.48 246	91.46 223	72.82 287	89.82 272	91.57 266	73.02 299	81.86 244	89.05 265	66.20 265	96.97 209	71.57 260	86.39 214	95.66 130

gm-plane-assit					89.60 296	68.00 317			77.28 266		88.99 266		97.57 146	79.44 200

MDTV_nov1_ep13			83.56 239		91.69 219	69.93 312	87.75 297	91.54 267	78.60 253	84.86 191	88.90 267	69.54 221	96.03 254	70.25 267	88.93 187

Patchmatch-test		81.37 276	79.30 280	87.58 266	90.92 264	74.16 277	80.99 330	87.68 327	70.52 315	76.63 285	88.81 268	71.21 197	92.76 315	60.01 321	86.93 213	95.83 124

tpmrst		85.35 225	84.99 205	86.43 287	90.88 267	67.88 318	88.71 287	91.43 270	80.13 236	86.08 145	88.80 269	73.05 177	96.02 255	82.48 146	83.40 243	95.40 137

DSMNet-mixed		76.94 296	76.29 295	78.89 313	83.10 327	56.11 339	87.78 296	79.77 343	60.65 334	75.64 299	88.71 270	61.56 287	88.34 329	60.07 320	89.29 177	92.21 276

PatchmatchNet		85.85 211	84.70 216	89.29 222	91.76 215	75.54 271	88.49 290	91.30 272	81.63 222	85.05 183	88.70 271	71.71 190	96.24 248	74.61 245	89.05 186	96.08 113
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet		82.59 265	80.53 268	88.76 232	91.51 222	78.32 229	86.57 304	90.13 297	79.32 243	80.70 256	88.69 272	52.98 319	93.07 314	66.03 302	88.86 188	94.90 158

Test4		85.75 216	83.72 234	91.83 115	88.08 310	81.03 147	92.48 228	95.54 134	83.38 170	73.40 310	88.57 273	50.99 322	97.37 178	86.61 104	94.47 105	97.09 85

Patchmatch-test1		85.81 214	84.71 215	89.12 226	92.15 205	76.60 262	91.12 260	91.69 261	83.53 165	85.50 165	88.56 274	66.79 258	95.00 294	72.69 256	90.35 159	95.76 127

IB-MVS	80.51 15	85.24 228	83.26 247	91.19 133	92.13 207	79.86 175	91.75 246	91.29 273	83.28 173	80.66 257	88.49 275	61.28 288	98.46 91	80.99 169	79.46 293	95.25 141
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
cascas		86.43 201	84.98 206	90.80 148	92.10 208	80.92 151	90.24 265	95.91 107	73.10 297	83.57 221	88.39 276	65.15 271	97.46 154	84.90 115	91.43 142	94.03 199

EPMVS		83.90 254	82.70 255	87.51 267	90.23 286	72.67 290	88.62 289	81.96 340	81.37 228	85.01 184	88.34 277	66.31 263	94.45 297	75.30 238	87.12 210	95.43 136

MDA-MVSNet-bldmvs		78.85 293	76.31 294	86.46 286	89.76 294	73.88 280	88.79 286	90.42 291	79.16 245	59.18 334	88.33 278	60.20 296	94.04 301	62.00 314	68.96 326	91.48 287

OpenMVS_ROB	74.94 19	79.51 289	77.03 293	86.93 280	87.00 315	76.23 267	92.33 233	90.74 289	68.93 319	74.52 304	88.23 279	49.58 324	96.62 229	57.64 323	84.29 230	87.94 328

MIMVSNet1		79.38 290	77.28 290	85.69 292	86.35 317	73.67 282	91.61 252	92.75 236	78.11 261	72.64 314	88.12 280	48.16 327	91.97 320	60.32 318	77.49 298	91.43 288

UnsupCasMVSNet_eth		80.07 285	78.27 287	85.46 294	85.24 320	72.63 292	88.45 291	94.87 182	82.99 186	71.64 318	88.07 281	56.34 309	91.75 321	73.48 253	63.36 335	92.01 278

test-LLR		85.87 210	85.41 199	87.25 274	90.95 260	71.67 298	89.55 274	89.88 304	83.41 168	84.54 196	87.95 282	67.25 255	95.11 291	81.82 158	93.37 124	94.97 147

test-mter		84.54 248	83.64 238	87.25 274	90.95 260	71.67 298	89.55 274	89.88 304	79.17 244	84.54 196	87.95 282	55.56 311	95.11 291	81.82 158	93.37 124	94.97 147

FMVSNet5		81.52 274	79.60 278	87.27 272	91.17 252	77.95 238	91.49 253	92.26 245	76.87 268	76.16 292	87.91 284	51.67 320	92.34 316	67.74 290	81.16 267	91.52 285

CR-MVSNet		85.35 225	83.76 231	90.12 181	90.58 276	79.34 198	85.24 313	91.96 256	78.27 257	85.55 160	87.87 285	71.03 200	95.61 269	73.96 250	89.36 175	95.40 137

Patchmtry		82.71 263	80.93 266	88.06 258	90.05 289	76.37 265	84.74 315	91.96 256	72.28 305	81.32 250	87.87 285	71.03 200	95.50 276	68.97 281	80.15 285	92.32 273

YYNet1		79.22 291	77.20 291	85.28 296	88.20 309	72.66 291	85.87 308	90.05 301	74.33 290	62.70 332	87.61 287	66.09 267	92.03 318	66.94 292	72.97 308	91.15 292

MDA-MVSNet_test_wron		79.21 292	77.19 292	85.29 295	88.22 308	72.77 289	85.87 308	90.06 299	74.34 289	62.62 333	87.56 288	66.14 266	91.99 319	66.90 295	73.01 307	91.10 294

DWT-MVSNet_test		84.95 235	83.68 236	88.77 231	91.43 227	73.75 281	91.74 247	90.98 282	80.66 233	83.84 213	87.36 289	62.44 281	97.11 199	78.84 207	85.81 217	95.46 135

TESTMET0.1,1		83.74 256	82.85 253	86.42 288	89.96 291	71.21 302	89.55 274	87.88 324	77.41 263	83.37 225	87.31 290	56.71 308	93.65 305	80.62 175	92.85 135	94.40 185

PatchFormer-LS_test		86.02 207	85.13 204	88.70 234	91.52 221	74.12 278	91.19 259	92.09 248	82.71 194	84.30 207	87.24 291	70.87 202	96.98 208	81.04 166	85.17 224	95.00 146

tpmvs		83.35 260	82.07 257	87.20 278	91.07 256	71.00 305	88.31 292	91.70 260	78.91 246	80.49 260	87.18 292	69.30 226	97.08 201	68.12 289	83.56 239	93.51 238

dp		81.47 275	80.23 271	85.17 297	89.92 292	65.49 325	86.74 302	90.10 298	76.30 272	81.10 251	87.12 293	62.81 279	95.92 259	68.13 288	79.88 290	94.09 196

v18		84.97 233	83.76 231	88.60 239	91.36 234	79.41 190	93.82 172	94.04 207	83.00 185	76.61 286	86.60 294	76.19 122	95.43 279	80.39 179	71.79 313	90.96 295

v16		84.96 234	83.74 233	88.62 237	91.40 229	79.48 184	93.83 170	94.04 207	83.03 182	76.54 287	86.59 295	76.11 127	95.42 280	80.33 182	71.80 312	90.95 297

v17		84.93 236	83.70 235	88.62 237	91.36 234	79.48 184	93.83 170	94.03 209	83.04 181	76.51 288	86.57 296	76.05 128	95.42 280	80.31 184	71.65 314	90.96 295

V14		84.79 239	83.52 241	88.57 243	91.32 241	79.43 189	93.72 181	94.01 210	83.06 178	76.22 290	86.43 297	76.01 132	95.37 283	79.96 188	70.99 318	90.91 299

V9		84.77 241	83.50 242	88.58 240	91.33 239	79.46 186	93.75 177	94.00 213	83.07 177	76.07 295	86.43 297	75.97 133	95.37 283	79.91 191	70.93 320	90.91 299

PM-MVS		78.11 294	76.12 296	84.09 305	83.54 326	70.08 311	88.97 285	85.27 334	79.93 238	74.73 303	86.43 297	34.70 340	93.48 307	79.43 201	72.06 311	88.72 320

v15		84.79 239	83.53 240	88.57 243	91.30 245	79.41 190	93.70 183	94.01 210	83.06 178	76.27 289	86.42 300	76.03 131	95.38 282	80.01 186	71.00 317	90.92 298

v12		84.74 242	83.46 243	88.58 240	91.32 241	79.50 181	93.75 177	94.01 210	83.06 178	75.98 297	86.41 301	75.82 139	95.36 285	79.87 192	70.89 321	90.89 301

v13		84.72 244	83.44 245	88.58 240	91.31 244	79.52 180	93.77 175	94.00 213	83.03 182	75.85 298	86.38 302	75.84 138	95.35 286	79.83 193	70.95 319	90.87 302

tpm cat1		81.96 268	80.27 270	87.01 279	91.09 255	71.02 304	87.38 300	91.53 268	66.25 325	80.17 262	86.35 303	68.22 253	96.15 251	69.16 280	82.29 250	93.86 209

v11		84.67 247	83.41 246	88.44 248	91.32 241	79.13 209	93.69 186	93.99 215	82.81 191	76.20 291	86.24 304	75.48 144	95.35 286	79.53 197	71.48 316	90.85 303

pmmvs-eth3d		80.97 281	78.72 286	87.74 262	84.99 322	79.97 173	90.11 268	91.65 262	75.36 279	73.51 308	86.03 305	59.45 300	93.96 302	75.17 239	72.21 310	89.29 314

ADS-MVSNet2		81.66 271	79.71 277	87.50 268	91.35 237	74.19 276	83.33 324	88.48 321	72.90 300	82.24 236	85.77 306	64.98 272	93.20 311	64.57 307	83.74 235	95.12 142

ADS-MVSNet		81.56 273	79.78 275	86.90 282	91.35 237	71.82 297	83.33 324	89.16 316	72.90 300	82.24 236	85.77 306	64.98 272	93.76 303	64.57 307	83.74 235	95.12 142

testing_2		83.40 259	81.02 264	90.56 152	85.06 321	80.51 161	91.37 255	95.57 130	82.92 188	67.06 326	85.54 308	49.47 325	97.24 189	86.74 99	85.44 220	93.93 202

N_pmnet		68.89 309	68.44 310	70.23 325	89.07 300	28.79 354	88.06 293	19.50 355	69.47 318	71.86 317	84.93 309	61.24 290	91.75 321	54.70 325	77.15 300	90.15 309

ambc				83.06 307	79.99 333	63.51 328	77.47 336	92.86 232		74.34 306	84.45 310	28.74 342	95.06 293	73.06 255	68.89 327	90.61 305

testus		74.41 301	73.35 299	77.59 318	82.49 331	57.08 335	86.02 306	90.21 295	72.28 305	72.89 313	84.32 311	37.08 338	86.96 333	52.24 327	82.65 247	88.73 319

GG-mvs-BLEND				87.94 261	89.73 295	77.91 239	87.80 295	78.23 346		80.58 258	83.86 312	59.88 299	95.33 288	71.20 262	92.22 139	90.60 307

patchmatchnet-post											83.76 313	71.53 194	96.48 237

PatchT		82.68 264	81.27 262	86.89 283	90.09 288	70.94 306	84.06 320	90.15 296	74.91 284	85.63 159	83.57 314	69.37 222	94.87 296	65.19 304	88.50 193	94.84 160

Anonymous20231211		72.97 303	69.63 308	83.00 308	83.05 328	66.91 321	92.69 221	89.45 310	61.06 333	67.50 325	83.46 315	34.34 341	93.61 306	51.11 328	63.97 333	88.48 325

new-patchmatchnet		76.41 297	75.17 297	80.13 312	82.65 330	59.61 332	87.66 298	91.08 278	78.23 259	69.85 320	83.22 316	54.76 314	91.63 323	64.14 309	64.89 331	89.16 316

PVSNet_0	73.20 20	77.22 295	74.83 298	84.37 302	90.70 273	71.10 303	83.09 326	89.67 307	72.81 302	73.93 307	83.13 317	60.79 293	93.70 304	68.54 282	50.84 340	88.30 327

RPMNet		83.18 261	80.87 267	90.12 181	90.58 276	79.34 198	85.24 313	90.78 288	71.44 309	85.55 160	82.97 318	70.87 202	95.61 269	61.01 317	89.36 175	95.40 137

Patchmatch-RL test		81.67 270	79.96 274	86.81 285	85.42 319	71.23 301	82.17 328	87.50 329	78.47 254	77.19 284	82.50 319	70.81 204	93.48 307	82.66 143	72.89 309	95.71 129

1111		70.54 308	69.71 307	73.04 322	79.30 334	44.83 347	84.23 318	88.96 318	67.33 322	65.42 328	82.28 320	41.11 336	88.11 330	47.12 335	71.60 315	86.19 330

.test1245		57.63 318	61.79 315	45.14 336	79.30 334	44.83 347	84.23 318	88.96 318	67.33 322	65.42 328	82.28 320	41.11 336	88.11 330	47.12 335	0.39 351	2.46 352

test2356		74.50 300	73.27 300	78.20 314	80.81 332	59.84 330	83.76 323	88.33 323	71.43 310	72.37 315	81.84 322	45.60 332	86.26 335	50.97 329	84.32 229	88.50 323

FPMVS		64.63 313	62.55 313	70.88 324	70.80 341	56.71 336	84.42 317	84.42 335	51.78 338	49.57 338	81.61 323	23.49 347	81.48 342	40.61 342	76.25 302	74.46 340

testpf		71.41 307	72.11 304	69.30 327	84.53 323	59.79 331	62.74 343	83.14 337	71.11 312	68.83 323	81.57 324	46.70 330	84.83 340	74.51 247	75.86 303	63.30 341

pmmvs3		71.81 306	68.71 309	81.11 311	75.86 338	70.42 309	86.74 302	83.66 336	58.95 335	68.64 324	80.89 325	36.93 339	89.52 326	63.10 312	63.59 334	83.39 332

LP		75.51 299	72.15 303	85.61 293	87.86 313	73.93 279	80.20 332	88.43 322	67.39 321	70.05 319	80.56 326	58.18 305	93.18 312	46.28 337	70.36 323	89.71 312

test1235678		72.22 304	70.31 305	77.93 317	78.04 337	58.04 334	85.76 310	89.80 306	70.15 317	63.43 331	80.20 327	42.24 335	87.24 332	48.68 333	74.50 305	88.50 323

new_pmnet		72.15 305	70.13 306	78.20 314	82.95 329	65.68 323	83.91 321	82.40 339	62.94 332	64.47 330	79.82 328	42.85 334	86.26 335	57.41 324	74.44 306	82.65 334

UnsupCasMVSNet_bld		76.23 298	73.27 300	85.09 298	83.79 325	72.92 285	85.65 312	93.47 225	71.52 308	68.84 322	79.08 329	49.77 323	93.21 310	66.81 296	60.52 337	89.13 318

test12356		64.99 312	63.78 311	68.61 329	72.69 340	39.14 350	78.46 334	87.61 328	64.91 328	55.77 335	77.48 330	28.10 343	85.59 337	44.69 338	64.35 332	81.12 336

DeepMVS_CX				56.31 334	74.23 339	51.81 343		56.67 353	44.85 340	48.54 340	75.16 331	27.87 344	58.74 350	40.92 341	52.22 339	58.39 345

testmv		65.49 311	62.66 312	73.96 321	68.78 343	53.14 342	84.70 316	88.56 320	65.94 327	52.35 337	74.65 332	25.02 346	85.14 338	43.54 339	60.40 338	83.60 331

JIA-IIPM		81.04 279	78.98 285	87.25 274	88.64 303	73.48 283	81.75 329	89.61 308	73.19 296	82.05 240	73.71 333	66.07 268	95.87 262	71.18 264	84.60 228	92.41 269

LCM-MVSNet		66.00 310	62.16 314	77.51 319	64.51 348	58.29 333	83.87 322	90.90 284	48.17 339	54.69 336	73.31 334	16.83 353	86.75 334	65.47 303	61.67 336	87.48 329

PMMVS2		59.60 315	56.40 317	69.21 328	68.83 342	46.58 345	73.02 341	77.48 347	55.07 337	49.21 339	72.95 335	17.43 352	80.04 343	49.32 332	44.33 341	80.99 337

gg-mvs-nofinetune		81.77 269	79.37 279	88.99 229	90.85 268	77.73 248	86.29 305	79.63 344	74.88 286	83.19 227	69.05 336	60.34 295	96.11 252	75.46 236	94.64 100	93.11 250

MVS-HIRNet		73.70 302	72.20 302	78.18 316	91.81 214	56.42 338	82.94 327	82.58 338	55.24 336	68.88 321	66.48 337	55.32 313	95.13 290	58.12 322	88.42 196	83.01 333

PMVS	47.18 22	52.22 319	48.46 320	63.48 331	45.72 353	46.20 346	73.41 339	78.31 345	41.03 343	30.06 346	65.68 338	6.05 355	83.43 341	30.04 345	65.86 329	60.80 343
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
no-one		61.56 314	56.58 316	76.49 320	67.80 346	62.76 329	78.13 335	86.11 330	63.16 331	43.24 341	64.70 339	26.12 345	88.95 328	50.84 330	29.15 343	77.77 338

ANet_high		58.88 316	54.22 319	72.86 323	56.50 352	56.67 337	80.75 331	86.00 331	73.09 298	37.39 343	64.63 340	22.17 348	79.49 345	43.51 340	23.96 347	82.43 335

tmp_tt		35.64 326	39.24 325	24.84 339	14.87 354	23.90 355	62.71 344	51.51 354	6.58 349	36.66 344	62.08 341	44.37 333	30.34 352	52.40 326	22.00 349	20.27 349

PNet_i23d		50.48 321	47.18 321	60.36 332	68.59 344	44.56 349	72.75 342	72.61 348	43.92 341	33.91 345	60.19 342	6.16 354	73.52 346	38.50 343	28.04 344	63.01 342

MVE	39.65 23	43.39 322	38.59 327	57.77 333	56.52 351	48.77 344	55.38 345	58.64 352	29.33 347	28.96 347	52.65 343	4.68 356	64.62 349	28.11 346	33.07 342	59.93 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		57.99 317	54.91 318	67.24 330	88.51 304	65.59 324	52.21 346	90.33 294	43.58 342	42.84 342	51.18 344	20.29 350	85.07 339	34.77 344	70.45 322	51.05 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		43.23 323	42.29 323	46.03 335	65.58 347	37.41 351	73.51 338	64.62 349	33.99 345	28.47 348	47.87 345	19.90 351	67.91 347	22.23 347	24.45 346	32.77 347

wuykxyi23d		50.55 320	44.13 322	69.81 326	56.77 350	54.58 341	73.22 340	80.78 341	39.79 344	22.08 350	46.69 346	4.03 357	79.71 344	47.65 334	26.13 345	75.14 339

EMVS		42.07 324	41.12 324	44.92 337	63.45 349	35.56 353	73.65 337	63.48 350	33.05 346	26.88 349	45.45 347	21.27 349	67.14 348	19.80 348	23.02 348	32.06 348

X-MVStestdata		88.31 136	86.13 182	94.85 16	98.54 7	86.60 26	96.93 12	97.19 22	90.66 22	92.85 36	23.41 348	85.02 43	99.49 15	91.99 37	98.56 35	98.47 14

test_post											10.29 349	70.57 210	95.91 261

test_post1							88.00 294				9.81 350	69.31 225	95.53 272	76.65 227

testmvs		8.92 329	11.52 330	1.12 342	1.06 355	0.46 357	86.02 306	0.65 357	0.62 350	2.74 352	9.52 351	0.31 360	0.45 354	2.38 350	0.39 351	2.46 352

test123		8.76 330	11.22 331	1.39 341	0.85 356	0.97 356	85.76 310	0.35 358	0.54 351	2.45 353	8.14 352	0.60 359	0.48 353	2.16 351	0.17 353	2.71 351

wuyk23d		21.27 328	20.48 329	23.63 340	68.59 344	36.41 352	49.57 347	6.85 356	9.37 348	7.89 351	4.46 353	4.03 357	31.37 351	17.47 349	16.07 350	3.12 350

pcd_1.5k_mvsjas		6.64 332	8.86 333	0.00 343	0.00 357	0.00 358	0.00 348	0.00 359	0.00 352	0.00 354	0.00 354	79.70 90	0.00 355	0.00 352	0.00 354	0.00 354

pcd1.5k->3k		37.02 325	38.84 326	31.53 338	92.33 203	0.00 358	0.00 348	96.13 92	0.00 352	0.00 354	0.00 354	72.70 181	0.00 355	0.00 352	88.43 195	94.60 171

sosnet-low-res		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 348	0.00 359	0.00 352	0.00 354	0.00 354	0.00 361	0.00 355	0.00 352	0.00 354	0.00 354

sosnet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 348	0.00 359	0.00 352	0.00 354	0.00 354	0.00 361	0.00 355	0.00 352	0.00 354	0.00 354

uncertanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 348	0.00 359	0.00 352	0.00 354	0.00 354	0.00 361	0.00 355	0.00 352	0.00 354	0.00 354

Regformer		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 348	0.00 359	0.00 352	0.00 354	0.00 354	0.00 361	0.00 355	0.00 352	0.00 354	0.00 354

uanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 348	0.00 359	0.00 352	0.00 354	0.00 354	0.00 361	0.00 355	0.00 352	0.00 354	0.00 354

GSMVS																96.12 110

test_part2					98.55 5	87.22 10				96.40 2

test_part1								97.45 6				91.93 1			99.02 2	98.67 4

sam_mvs1												71.70 191				96.12 110

sam_mvs												70.60 206

MTGPA								96.97 34

MTMP								60.64 351

test9_res														91.91 41	98.71 19	98.07 45

agg_prior2														90.54 60	98.68 24	98.27 31

agg_prior					97.38 43	85.92 44		96.72 56		92.16 56			98.97 60

test_prior4						85.96 43	94.11 150

test_prior				93.82 51	97.29 48	84.49 64		96.88 43					98.87 66			98.11 43

旧先验2							93.36 194		71.25 311	94.37 13			97.13 198	86.74 99

新几何2							93.11 208

无先验							93.28 201	96.26 82	73.95 291				99.05 45	80.56 176		96.59 98

原ACMM2							92.94 216

testdata2													98.75 77	78.30 211

segment_acmp												87.16 21

testdata1							92.15 239		87.94 71

test12				94.34 40	97.13 53	86.15 40		96.29 81		91.04 75		85.08 41	99.01 54		98.13 46	97.86 59

plane_prior7					94.70 135	82.74 111

plane_prior6					94.52 141	82.75 109						74.23 158

plane_prior5								96.22 86					98.12 107	88.15 78	89.99 164	94.63 168

plane_prior3						82.75 109			90.26 25	86.91 128

plane_prior2							95.85 41		90.81 18

plane_prior1					94.59 139

plane_prior						82.73 112	95.21 72		89.66 35						89.88 167

n2								0.00 359

nn								0.00 359

door-mid								85.49 332

test11								96.57 70

door								85.33 333

HQP5-MVS						81.56 129

HQP-NCC					94.17 152		94.39 127		88.81 50	85.43 171

ACMP_Plane					94.17 152		94.39 127		88.81 50	85.43 171

BP-MVS														87.11 96

HQP4-MVS										85.43 171			97.96 128			94.51 178

HQP3-MVS								96.04 99							89.77 169

HQP2-MVS												73.83 167

MDTV_nov1_ep13_2view						55.91 340	87.62 299		73.32 295	84.59 195		70.33 213		74.65 244		95.50 133

ACMMP++_ref															87.47 205

ACMMP++															88.01 202

Test By Simon												80.02 85

High-resolution multi-view benchmark