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.

MCST-MVS		91.08 1	91.46 2	89.94 2	97.66 2	73.37 7	97.13 1	95.58 12	89.33 1	85.77 25	96.26 9	72.84 11	99.38 1	92.64 4	95.93 4	97.08 4

HSP-MVS		90.38 2	91.89 1	85.84 69	92.83 57	64.03 170	93.06 76	94.52 31	82.19 19	93.65 1	96.15 12	85.89 1	97.19 59	91.02 10	97.75 1	96.29 16

CNVR-MVS		90.32 3	90.89 4	88.61 11	96.76 4	70.65 18	96.47 6	94.83 23	84.83 9	89.07 9	96.80 4	70.86 16	99.06 3	92.64 4	95.71 5	96.12 18

DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 51	73.88 6	97.01 2	94.40 37	88.32 2	85.71 26	94.91 47	74.11 9	98.91 6	87.26 28	95.94 3	97.03 5
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
DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 104	93.00 54	58.16 258	96.72 3	94.41 36	86.50 5	90.25 5	97.83 1	75.46 7	98.67 14	92.78 2	95.49 7	97.32 1

CANet		89.61 6	89.99 6	88.46 13	94.39 26	69.71 32	96.53 5	93.78 46	86.89 4	89.68 6	95.78 17	65.94 44	99.10 2	92.99 1	93.91 27	96.58 11

HPM-MVS++		89.37 7	89.95 7	87.64 21	95.10 19	68.23 59	95.24 22	94.49 33	82.43 17	88.90 10	96.35 7	71.89 15	98.63 15	88.76 20	96.40 2	96.06 21

ESAPD		89.08 8	89.53 8	87.72 20	96.29 7	68.16 60	94.96 31	94.26 40	68.52 211	90.78 3	97.23 2	77.03 4	98.90 7	91.52 6	95.18 8	96.11 19

NCCC		89.07 9	89.46 9	87.91 16	96.60 5	69.05 40	96.38 7	94.64 30	84.42 10	86.74 20	96.20 10	66.56 39	98.76 13	89.03 18	94.56 20	95.92 27

MVS_0304		88.39 10	88.35 13	88.50 12	93.01 53	70.11 23	95.90 10	92.20 118	86.27 6	88.70 11	95.92 15	56.76 131	99.02 4	92.68 3	93.76 30	96.37 15

PS-MVSNAJ		88.14 11	87.61 18	89.71 4	92.06 74	76.72 1	95.75 11	93.26 76	83.86 11	89.55 7	96.06 13	53.55 179	97.89 32	91.10 8	93.31 37	94.54 70

TSAR-MVS + MP.		88.11 12	88.64 10	86.54 48	91.73 85	68.04 63	90.36 175	93.55 57	82.89 14	91.29 2	92.89 89	72.27 12	96.03 102	87.99 22	94.77 15	95.54 33

TSAR-MVS + GP.		87.96 13	88.37 12	86.70 42	93.51 44	65.32 140	95.15 25	93.84 45	78.17 55	85.93 24	94.80 50	75.80 6	98.21 23	89.38 13	88.78 79	96.59 10

DeepC-MVS_fast	79.48 2	87.95 14	88.00 14	87.79 19	95.86 14	68.32 55	95.74 12	94.11 42	83.82 12	83.49 46	96.19 11	64.53 63	98.44 19	83.42 53	94.88 14	96.61 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v2_base		87.92 15	87.38 23	89.55 7	91.41 99	76.43 2	95.74 12	93.12 85	83.53 13	89.55 7	95.95 14	53.45 184	97.68 34	91.07 9	92.62 44	94.54 70

EPNet		87.84 16	88.38 11	86.23 62	93.30 46	66.05 127	95.26 21	94.84 22	87.09 3	88.06 13	94.53 54	66.79 36	97.34 52	83.89 50	91.68 56	95.29 40
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 17	87.77 16	87.63 23	89.24 140	71.18 14	96.57 4	92.90 93	82.70 16	87.13 17	95.27 32	64.99 59	95.80 108	89.34 14	91.80 54	95.93 26

APDe-MVS		87.54 18	87.84 15	86.65 43	96.07 11	66.30 123	94.84 34	93.78 46	69.35 199	88.39 12	96.34 8	67.74 30	97.66 38	90.62 11	93.44 36	96.01 24

SD-MVS		87.49 19	87.49 20	87.50 25	93.60 41	68.82 46	93.90 55	92.63 103	76.86 71	87.90 14	95.76 18	66.17 40	97.63 40	89.06 17	91.48 60	96.05 22

test_prior3		87.38 20	87.70 17	86.42 54	94.71 23	67.35 77	95.10 27	93.10 86	75.40 89	85.25 32	95.61 23	67.94 26	96.84 81	87.47 25	94.77 15	95.05 52

alignmvs		87.28 21	86.97 26	88.24 15	91.30 100	71.14 16	95.61 16	93.56 56	79.30 38	87.07 19	95.25 34	68.43 21	96.93 79	87.87 23	84.33 114	96.65 8

Regformer-1		87.24 22	87.60 19	86.15 63	95.14 17	65.83 133	93.95 51	95.12 17	82.11 21	84.25 39	95.73 19	67.88 29	98.35 21	85.60 38	88.64 80	94.26 77

train_agg		87.21 23	87.42 22	86.60 45	94.18 29	67.28 79	94.16 38	93.51 58	71.87 157	85.52 28	95.33 28	68.19 23	97.27 56	89.09 15	94.90 12	95.25 45

MG-MVS		87.11 24	86.27 30	89.62 5	97.79 1	76.27 3	94.96 31	94.49 33	78.74 51	83.87 45	92.94 86	64.34 64	96.94 77	75.19 102	94.09 24	95.66 29

agg_prior1		87.02 25	87.26 24	86.28 61	94.16 33	66.97 88	94.08 44	93.31 74	71.85 159	84.49 37	95.39 26	68.91 19	96.75 85	88.84 19	94.32 22	95.13 49

Regformer-2		87.00 26	87.43 21	85.71 77	95.14 17	64.73 152	93.95 51	94.95 20	81.69 26	84.03 43	95.73 19	67.35 33	98.19 25	85.40 40	88.64 80	94.20 79

agg_prior3		86.93 27	87.08 25	86.48 51	94.21 27	66.95 90	94.14 41	93.40 70	71.80 162	84.86 34	95.13 38	66.16 41	97.25 58	89.09 15	94.90 12	95.25 45

CSCG		86.87 28	86.26 31	88.72 9	95.05 20	70.79 17	93.83 58	95.33 14	68.48 214	77.63 90	94.35 61	73.04 10	98.45 18	84.92 43	93.71 32	96.92 6

canonicalmvs		86.85 29	86.25 32	88.66 10	91.80 84	71.92 10	93.54 65	91.71 136	80.26 31	87.55 15	95.25 34	63.59 73	96.93 79	88.18 21	84.34 113	97.11 3

PHI-MVS		86.83 30	86.85 29	86.78 41	93.47 45	65.55 137	95.39 20	95.10 19	71.77 164	85.69 27	96.52 5	62.07 84	98.77 12	86.06 36	95.60 6	96.03 23

SteuartSystems-ACMMP		86.82 31	86.90 27	86.58 47	90.42 111	66.38 120	96.09 9	93.87 44	77.73 60	84.01 44	95.66 21	63.39 74	97.94 29	87.40 27	93.55 35	95.42 34
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 32	86.86 28	86.31 60	93.76 37	67.53 74	96.33 8	93.61 54	82.34 18	81.00 59	93.08 81	63.19 77	97.29 54	87.08 29	91.38 61	94.13 85

jason		86.40 33	86.17 33	87.11 34	86.16 190	70.54 20	95.71 15	92.19 120	82.00 24	84.58 36	94.34 62	61.86 86	95.53 124	87.76 24	90.89 66	95.27 42
jason: jason.
WTY-MVS		86.32 34	85.81 37	87.85 17	92.82 59	69.37 37	95.20 23	95.25 15	82.71 15	81.91 53	94.73 51	67.93 28	97.63 40	79.55 75	82.25 126	96.54 12

MSLP-MVS++		86.27 35	85.91 36	87.35 28	92.01 75	68.97 43	95.04 29	92.70 98	79.04 46	81.50 56	96.50 6	58.98 112	96.78 83	83.49 52	93.93 26	96.29 16

VNet		86.20 36	85.65 41	87.84 18	93.92 36	69.99 26	95.73 14	95.94 11	78.43 53	86.00 23	93.07 83	58.22 115	97.00 69	85.22 41	84.33 114	96.52 13

MVS_111021_HR		86.19 37	85.80 38	87.37 27	93.17 50	69.79 30	93.99 49	93.76 49	79.08 45	78.88 78	93.99 68	62.25 83	98.15 26	85.93 37	91.15 64	94.15 84

ACMMP_Plus		86.05 38	85.80 38	86.80 40	91.58 88	67.53 74	91.79 128	93.49 60	74.93 96	84.61 35	95.30 30	59.42 106	97.92 30	86.13 35	94.92 11	94.94 58

APD-MVS		85.93 39	85.99 34	85.76 74	95.98 13	65.21 142	93.59 63	92.58 105	66.54 229	86.17 21	95.88 16	63.83 68	97.00 69	86.39 34	92.94 40	95.06 51
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 40	85.46 42	87.18 31	88.20 162	72.42 9	92.41 99	92.77 96	82.11 21	80.34 64	93.07 83	68.27 22	95.02 132	78.39 85	93.59 34	94.09 88

Regformer-3		85.80 41	85.92 35	85.46 81	94.17 31	65.09 148	92.95 80	95.11 18	81.13 27	81.68 55	95.04 39	65.82 46	98.32 22	83.02 54	84.36 111	92.97 120

CDPH-MVS		85.71 42	85.46 42	86.46 52	94.75 22	67.19 81	93.89 56	92.83 95	70.90 178	83.09 48	95.28 31	63.62 71	97.36 50	80.63 70	94.18 23	94.84 60

DeepC-MVS	77.85 3	85.52 43	85.24 44	86.37 57	88.80 149	66.64 110	92.15 103	93.68 52	81.07 28	76.91 100	93.64 73	62.59 82	98.44 19	85.50 39	92.84 42	94.03 92
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-4		85.45 44	85.69 40	84.73 102	94.17 31	63.23 185	92.95 80	94.83 23	80.66 29	81.29 57	95.04 39	65.12 51	98.08 28	82.74 55	84.36 111	92.88 124

MP-MVS-pluss		85.24 45	85.13 45	85.56 78	91.42 97	65.59 136	91.54 140	92.51 107	74.56 99	80.62 61	95.64 22	59.15 109	97.00 69	86.94 31	93.80 28	94.07 90
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PAPR		85.15 46	84.47 48	87.18 31	96.02 12	68.29 56	91.85 126	93.00 90	76.59 76	79.03 77	95.00 41	61.59 87	97.61 42	78.16 86	89.00 78	95.63 30

MP-MVS		85.02 47	84.97 46	85.17 93	92.60 63	64.27 167	93.24 71	92.27 112	73.13 130	79.63 71	94.43 55	61.90 85	97.17 60	85.00 42	92.56 45	94.06 91

#test#		84.98 48	84.74 47	85.72 75	93.75 39	65.01 149	94.09 43	93.19 81	73.55 124	79.22 74	94.93 44	59.04 110	97.67 35	82.66 56	92.21 48	94.49 74

CHOSEN 1792x2688		84.98 48	83.45 57	89.57 6	89.94 120	75.14 4	92.07 109	92.32 110	81.87 25	75.68 105	88.27 150	60.18 100	98.60 16	80.46 72	90.27 73	94.96 57

MPTG		84.73 50	84.47 48	85.50 79	91.89 80	65.16 143	91.55 139	92.23 113	75.32 91	80.53 62	95.21 36	56.06 143	97.16 61	84.86 44	92.55 46	94.18 80

HFP-MVS		84.73 50	84.40 50	85.72 75	93.75 39	65.01 149	93.50 66	93.19 81	72.19 149	79.22 74	94.93 44	59.04 110	97.67 35	81.55 64	92.21 48	94.49 74

MVS		84.66 52	82.86 67	90.06 1	90.93 105	74.56 5	87.91 222	95.54 13	68.55 210	72.35 141	94.71 52	59.78 103	98.90 7	81.29 69	94.69 19	96.74 7

ACMMPR		84.37 53	84.06 51	85.28 89	93.56 42	64.37 162	93.50 66	93.15 84	72.19 149	78.85 80	94.86 48	56.69 135	97.45 46	81.55 64	92.20 50	94.02 93

region2R		84.36 54	84.03 52	85.36 87	93.54 43	64.31 164	93.43 69	92.95 91	72.16 152	78.86 79	94.84 49	56.97 128	97.53 44	81.38 67	92.11 52	94.24 78

LFMVS		84.34 55	82.73 70	89.18 8	94.76 21	73.25 8	94.99 30	91.89 129	71.90 155	82.16 52	93.49 76	47.98 228	97.05 64	82.55 57	84.82 107	97.25 2

HY-MVS	76.49 5	84.28 56	83.36 62	87.02 37	92.22 71	67.74 68	84.65 258	94.50 32	79.15 42	82.23 51	87.93 156	66.88 35	96.94 77	80.53 71	82.20 127	96.39 14

MAR-MVS		84.18 57	83.43 58	86.44 53	96.25 9	65.93 130	94.28 37	94.27 39	74.41 100	79.16 76	95.61 23	53.99 174	98.88 11	69.62 146	93.26 38	94.50 73
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
MVS_Test		84.16 58	83.20 63	87.05 36	91.56 89	69.82 29	89.99 184	92.05 123	77.77 59	82.84 49	86.57 174	63.93 67	96.09 99	74.91 108	89.18 77	95.25 45

CANet_DTU		84.09 59	83.52 54	85.81 70	90.30 114	66.82 95	91.87 124	89.01 230	85.27 7	86.09 22	93.74 72	47.71 231	96.98 73	77.90 89	89.78 75	93.65 101

PVSNet_Blended_VisFu		83.97 60	83.50 55	85.39 86	90.02 118	66.59 113	93.77 59	91.73 134	77.43 66	77.08 99	89.81 136	63.77 70	96.97 74	79.67 74	88.21 83	92.60 128

DWT-MVSNet_test		83.95 61	82.80 68	87.41 26	92.90 56	70.07 25	89.12 202	94.42 35	82.15 20	77.64 89	91.77 105	70.81 17	96.22 94	65.03 185	81.36 130	95.94 25

MTAPA		83.91 62	83.38 61	85.50 79	91.89 80	65.16 143	81.75 279	92.23 113	75.32 91	80.53 62	95.21 36	56.06 143	97.16 61	84.86 44	92.55 46	94.18 80

XVS		83.87 63	83.47 56	85.05 95	93.22 47	63.78 173	92.92 82	92.66 101	73.99 111	78.18 84	94.31 64	55.25 148	97.41 47	79.16 78	91.58 58	93.95 95

Effi-MVS+		83.82 64	82.76 69	86.99 38	89.56 133	69.40 36	91.35 147	86.12 277	72.59 138	83.22 47	92.81 91	59.60 105	96.01 104	81.76 62	87.80 86	95.56 32

EI-MVSNet-Vis-set		83.77 65	83.67 53	84.06 116	92.79 61	63.56 182	91.76 131	94.81 25	79.65 36	77.87 86	94.09 66	63.35 75	97.90 31	79.35 76	79.36 140	90.74 154

MVSFormer		83.75 66	82.88 66	86.37 57	89.24 140	71.18 14	89.07 203	90.69 168	65.80 235	87.13 17	94.34 62	64.99 59	92.67 218	72.83 115	91.80 54	95.27 42

CP-MVS		83.71 67	83.40 60	84.65 105	93.14 51	63.84 171	94.59 35	92.28 111	71.03 176	77.41 93	94.92 46	55.21 151	96.19 95	81.32 68	90.70 68	93.91 97

PVSNet_BlendedMVS		83.38 68	83.43 58	83.22 132	93.76 37	67.53 74	94.06 45	93.61 54	79.13 43	81.00 59	85.14 188	63.19 77	97.29 54	87.08 29	73.91 184	84.83 251

PGM-MVS		83.25 69	82.70 71	84.92 97	92.81 60	64.07 169	90.44 172	92.20 118	71.28 174	77.23 96	94.43 55	55.17 152	97.31 53	79.33 77	91.38 61	93.37 106

HPM-MVS		83.25 69	82.95 65	84.17 114	92.25 70	62.88 195	90.91 161	91.86 130	70.30 191	77.12 97	93.96 69	56.75 133	96.28 93	82.04 60	91.34 63	93.34 107

EI-MVSNet-UG-set		83.14 71	82.96 64	83.67 126	92.28 69	63.19 188	91.38 145	94.68 28	79.22 40	76.60 101	93.75 71	62.64 81	97.76 33	78.07 87	78.01 151	90.05 161

PatchFormer-LS_test		83.14 71	81.81 79	87.12 33	92.34 66	69.92 28	88.64 209	93.32 73	82.07 23	74.87 114	91.62 109	68.91 19	96.08 101	66.07 176	78.45 150	95.37 35

VDD-MVS		83.06 73	81.81 79	86.81 39	90.86 108	67.70 69	95.40 19	91.50 144	75.46 86	81.78 54	92.34 99	40.09 268	97.13 63	86.85 32	82.04 128	95.60 31

PAPM_NR		82.97 74	81.84 78	86.37 57	94.10 35	66.76 102	87.66 233	92.84 94	69.96 194	74.07 121	93.57 74	63.10 79	97.50 45	70.66 139	90.58 70	94.85 59

mPP-MVS		82.96 75	82.44 72	84.52 109	92.83 57	62.92 193	92.76 85	91.85 131	71.52 171	75.61 108	94.24 65	53.48 183	96.99 72	78.97 81	90.73 67	93.64 102

DP-MVS Recon		82.73 76	81.65 81	85.98 65	97.31 3	67.06 85	95.15 25	91.99 125	69.08 202	76.50 103	93.89 70	54.48 168	98.20 24	70.76 138	85.66 103	92.69 125

CLD-MVS		82.73 76	82.35 74	83.86 119	87.90 168	67.65 71	95.45 18	92.18 121	85.06 8	72.58 134	92.27 100	52.46 192	95.78 109	84.18 46	79.06 143	88.16 186
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 78	82.38 73	83.73 123	89.25 138	59.58 244	92.24 102	94.89 21	77.96 57	79.86 68	92.38 97	56.70 134	97.05 64	77.26 92	80.86 134	94.55 68

3Dnovator	73.91 6	82.69 79	80.82 89	88.31 14	89.57 132	71.26 13	92.60 93	94.39 38	78.84 48	67.89 199	92.48 95	48.42 223	98.52 17	68.80 154	94.40 21	95.15 48

MVSTER		82.47 80	82.05 75	83.74 121	92.68 62	69.01 41	91.90 123	93.21 78	79.83 32	72.14 142	85.71 184	74.72 8	94.72 143	75.72 98	72.49 194	87.50 196

TESTMET0.1,1		82.41 81	81.98 77	83.72 124	88.08 163	63.74 175	92.70 88	93.77 48	79.30 38	77.61 91	87.57 162	58.19 116	94.08 176	73.91 111	86.68 94	93.33 109

CostFormer		82.33 82	81.15 85	85.86 68	89.01 145	68.46 51	82.39 276	93.01 88	75.59 84	80.25 65	81.57 229	72.03 14	94.96 134	79.06 80	77.48 161	94.16 83

API-MVS		82.28 83	80.53 94	87.54 24	96.13 10	70.59 19	93.63 61	91.04 161	65.72 237	75.45 110	92.83 90	56.11 142	98.89 10	64.10 193	89.75 76	93.15 114

IB-MVS	77.80 4	82.18 84	80.46 95	87.35 28	89.14 142	70.28 22	95.59 17	95.17 16	78.85 47	70.19 161	85.82 182	70.66 18	97.67 35	72.19 124	66.52 236	94.09 88
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
xiu_mvs_v1_base_debu		82.16 85	81.12 86	85.26 90	86.42 184	68.72 47	92.59 95	90.44 173	73.12 131	84.20 40	94.36 57	38.04 278	95.73 112	84.12 47	86.81 91	91.33 146

xiu_mvs_v1_base		82.16 85	81.12 86	85.26 90	86.42 184	68.72 47	92.59 95	90.44 173	73.12 131	84.20 40	94.36 57	38.04 278	95.73 112	84.12 47	86.81 91	91.33 146

xiu_mvs_v1_base_debi		82.16 85	81.12 86	85.26 90	86.42 184	68.72 47	92.59 95	90.44 173	73.12 131	84.20 40	94.36 57	38.04 278	95.73 112	84.12 47	86.81 91	91.33 146

3Dnovator+	73.60 7	82.10 88	80.60 93	86.60 45	90.89 107	66.80 101	95.20 23	93.44 68	74.05 110	67.42 204	92.49 94	49.46 214	97.65 39	70.80 137	91.68 56	95.33 37

MVS_111021_LR		82.02 89	81.52 82	83.51 129	88.42 158	62.88 195	89.77 191	88.93 232	76.78 73	75.55 109	93.10 79	50.31 206	95.38 126	83.82 51	87.02 90	92.26 137

PMMVS		81.98 90	82.04 76	81.78 174	89.76 124	56.17 279	91.13 158	90.69 168	77.96 57	80.09 66	93.57 74	46.33 241	94.99 133	81.41 66	87.46 88	94.17 82

EPP-MVSNet		81.79 91	81.52 82	82.61 144	88.77 150	60.21 234	93.02 78	93.66 53	68.52 211	72.90 129	90.39 122	72.19 13	94.96 134	74.93 107	79.29 142	92.67 126

APD-MVS_3200maxsize		81.64 92	81.32 84	82.59 145	92.36 65	58.74 255	91.39 143	91.01 162	63.35 258	79.72 70	94.62 53	51.82 195	96.14 97	79.71 73	87.93 85	92.89 123

ACMMP		81.49 93	80.67 91	83.93 118	91.71 86	62.90 194	92.13 104	92.22 117	71.79 163	71.68 149	93.49 76	50.32 205	96.96 75	78.47 83	84.22 118	91.93 140
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
CDS-MVSNet		81.43 94	80.74 90	83.52 128	86.26 188	64.45 157	92.09 107	90.65 171	75.83 83	73.95 123	89.81 136	63.97 66	92.91 210	71.27 131	82.82 123	93.20 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 95	79.99 98	85.46 81	90.39 113	68.40 52	86.88 246	90.61 172	74.41 100	70.31 160	84.67 193	63.79 69	92.32 229	73.13 112	85.70 102	95.67 28

1121		81.25 96	80.05 96	84.87 99	92.30 68	64.31 164	87.91 222	91.39 148	59.44 284	79.94 67	92.91 87	57.09 124	97.01 67	66.63 168	92.81 43	93.29 110

Fast-Effi-MVS+		81.14 97	80.01 97	84.51 110	90.24 116	65.86 131	94.12 42	89.15 224	73.81 118	75.37 111	88.26 151	57.26 123	94.53 150	66.97 167	84.92 106	93.15 114

HQP-MVS		81.14 97	80.64 92	82.64 143	87.54 171	63.66 180	94.06 45	91.70 137	79.80 33	74.18 117	90.30 123	51.63 199	95.61 118	77.63 90	78.90 144	88.63 175

HyFIR lowres test		81.03 99	79.56 105	85.43 84	87.81 169	68.11 62	90.18 179	90.01 196	70.65 186	72.95 128	86.06 180	63.61 72	94.50 151	75.01 106	79.75 138	93.67 100

nrg030		80.93 100	79.86 100	84.13 115	83.69 220	68.83 45	93.23 72	91.20 154	75.55 85	75.06 113	88.22 154	63.04 80	94.74 142	81.88 61	66.88 233	88.82 173

Vis-MVSNet		80.92 101	79.98 99	83.74 121	88.48 155	61.80 211	93.44 68	88.26 247	73.96 114	77.73 87	91.76 106	49.94 210	94.76 140	65.84 179	90.37 72	94.65 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
1314		80.70 102	78.95 117	85.94 67	87.77 170	67.56 73	87.91 222	92.55 106	72.17 151	67.44 203	93.09 80	50.27 207	97.04 66	71.68 127	87.64 87	93.23 112

tpmrst		80.57 103	79.14 116	84.84 100	90.10 117	68.28 57	81.70 280	89.72 207	77.63 62	75.96 104	79.54 259	64.94 61	92.71 216	75.43 100	77.28 164	93.55 103

1112_ss		80.56 104	79.83 101	82.77 138	88.65 151	60.78 221	92.29 100	88.36 243	72.58 139	72.46 138	94.95 42	65.09 52	93.42 199	66.38 172	77.71 153	94.10 87

VDDNet		80.50 105	78.26 124	87.21 30	86.19 189	69.79 30	94.48 36	91.31 151	60.42 278	79.34 73	90.91 113	38.48 274	96.56 91	82.16 58	81.05 132	95.27 42

BH-w/o		80.49 106	79.30 112	84.05 117	90.83 109	64.36 163	93.60 62	89.42 214	74.35 105	69.09 179	90.15 125	55.23 150	95.61 118	64.61 188	86.43 99	92.17 138

TAMVS		80.37 107	79.45 108	83.13 134	85.14 201	63.37 183	91.23 152	90.76 167	74.81 98	72.65 132	88.49 145	60.63 96	92.95 206	69.41 148	81.95 129	93.08 117

HQP_MVS		80.34 108	79.75 102	82.12 167	86.94 179	62.42 200	93.13 74	91.31 151	78.81 49	72.53 135	89.14 141	50.66 203	95.55 122	76.74 93	78.53 148	88.39 180

HPM-MVS_fast		80.25 109	79.55 107	82.33 157	91.55 90	59.95 239	91.32 149	89.16 223	65.23 241	74.71 115	93.07 83	47.81 230	95.74 111	74.87 110	88.23 82	91.31 150

diffmvs		80.18 110	78.55 121	85.07 94	88.56 152	66.93 91	86.70 249	88.62 238	70.42 188	78.69 82	85.26 186	56.93 130	94.77 139	68.68 155	83.09 120	93.51 104

ab-mvs		80.18 110	78.31 123	85.80 71	88.44 157	65.49 139	83.00 273	92.67 100	71.82 161	77.36 94	85.01 189	54.50 166	96.59 88	76.35 97	75.63 171	95.32 39

IS-MVSNet		80.14 112	79.41 109	82.33 157	87.91 167	60.08 238	91.97 115	88.27 246	72.90 135	71.44 151	91.73 108	61.44 88	93.66 194	62.47 210	86.53 97	93.24 111

test-LLR		80.10 113	79.56 105	81.72 176	86.93 181	61.17 216	92.70 88	91.54 141	71.51 172	75.62 106	86.94 171	53.83 175	92.38 226	72.21 122	84.76 109	91.60 143

PVSNet	73.49 8	80.05 114	78.63 119	84.31 112	90.92 106	64.97 151	92.47 98	91.05 160	79.18 41	72.43 139	90.51 121	37.05 290	94.06 177	68.06 156	86.00 101	93.90 98

UA-Net		80.02 115	79.65 103	81.11 190	89.33 136	57.72 262	86.33 251	89.00 231	77.44 65	81.01 58	89.15 140	59.33 107	95.90 105	61.01 217	84.28 116	89.73 165

test-mter		79.96 116	79.38 111	81.72 176	86.93 181	61.17 216	92.70 88	91.54 141	73.85 116	75.62 106	86.94 171	49.84 212	92.38 226	72.21 122	84.76 109	91.60 143

QAPM		79.95 117	77.39 140	87.64 21	89.63 131	71.41 12	93.30 70	93.70 51	65.34 240	67.39 206	91.75 107	47.83 229	98.96 5	57.71 233	89.81 74	92.54 130

UGNet		79.87 118	78.68 118	83.45 131	89.96 119	61.51 214	92.13 104	90.79 165	76.83 72	78.85 80	86.33 177	38.16 276	96.17 96	67.93 158	87.17 89	92.67 126
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
abl_6		79.82 119	79.20 114	81.70 178	89.85 121	58.34 257	88.47 212	90.07 193	62.56 265	77.71 88	93.08 81	47.65 232	96.78 83	77.94 88	85.45 105	89.99 162

tpm2		79.80 120	77.95 129	85.34 88	88.28 160	68.26 58	81.56 284	91.42 147	70.11 192	77.59 92	80.50 245	67.40 31	94.26 167	67.34 163	77.35 162	93.51 104

DI_MVS_plusplus_test		79.78 121	77.50 137	86.62 44	80.90 243	69.46 35	90.69 167	91.97 127	77.00 68	59.07 260	82.34 214	46.82 235	95.88 106	82.14 59	86.59 96	94.53 72

test_normal		79.66 122	77.36 142	86.54 48	80.72 247	69.21 38	90.68 168	92.16 122	76.99 69	58.63 264	82.03 223	46.70 237	95.86 107	81.74 63	86.63 95	94.56 67

thres200		79.66 122	78.33 122	83.66 127	92.54 64	65.82 134	93.06 76	96.31 8	74.90 97	73.30 126	88.66 143	59.67 104	95.61 118	47.84 266	78.67 147	89.56 167

CPTT-MVS		79.59 124	79.16 115	80.89 197	91.54 91	59.80 241	92.10 106	88.54 241	60.42 278	72.96 127	93.28 78	48.27 224	92.80 213	78.89 82	86.50 98	90.06 160

Test_1112_low_res		79.56 125	78.60 120	82.43 150	88.24 161	60.39 230	92.09 107	87.99 250	72.10 153	71.84 145	87.42 164	64.62 62	93.04 203	65.80 180	77.30 163	93.85 99

FIs		79.47 126	79.41 109	79.67 216	85.95 193	59.40 246	91.68 135	93.94 43	78.06 56	68.96 182	88.28 149	66.61 38	91.77 240	66.20 175	74.99 177	87.82 193

BH-RMVSNet		79.46 127	77.65 133	84.89 98	91.68 87	65.66 135	93.55 64	88.09 248	72.93 134	73.37 125	91.12 112	46.20 243	96.12 98	56.28 237	85.61 104	92.91 122

PCF-MVS	73.15 9	79.29 128	77.63 134	84.29 113	86.06 191	65.96 129	87.03 241	91.10 158	69.86 195	69.79 168	90.64 116	57.54 122	96.59 88	64.37 192	82.29 125	90.32 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 129	79.57 104	78.24 250	88.46 156	52.29 296	90.41 174	89.12 225	74.24 106	69.13 178	91.91 103	65.77 47	90.09 272	59.00 228	88.09 84	92.33 133

114514_t		79.17 130	77.67 132	83.68 125	95.32 16	65.53 138	92.85 84	91.60 140	63.49 257	67.92 198	90.63 118	46.65 238	95.72 116	67.01 166	83.54 119	89.79 163

VPA-MVSNet		79.03 131	78.00 128	82.11 170	85.95 193	64.48 156	93.22 73	94.66 29	75.05 95	74.04 122	84.95 190	52.17 194	93.52 196	74.90 109	67.04 232	88.32 182

OPM-MVS		79.00 132	78.09 126	81.73 175	83.52 223	63.83 172	91.64 138	90.30 183	76.36 79	71.97 144	89.93 135	46.30 242	95.17 131	75.10 103	77.70 154	86.19 224

EI-MVSNet		78.97 133	78.22 125	81.25 183	85.33 198	62.73 198	89.53 195	93.21 78	72.39 143	72.14 142	90.13 126	60.99 89	94.72 143	67.73 160	72.49 194	86.29 222

AdaColmap		78.94 134	77.00 147	84.76 101	96.34 6	65.86 131	92.66 92	87.97 251	62.18 267	70.56 153	92.37 98	43.53 255	97.35 51	64.50 190	82.86 122	91.05 152

tpmp4_e23		78.85 135	76.55 151	85.77 73	89.25 138	68.39 53	81.63 283	91.38 149	70.40 189	75.21 112	79.22 261	67.37 32	94.79 138	58.98 229	75.51 172	94.13 85

VPNet		78.82 136	77.53 136	82.70 140	84.52 208	66.44 119	93.93 53	92.23 113	80.46 30	72.60 133	88.38 148	49.18 217	93.13 202	72.47 120	63.97 259	88.55 177

EPNet_dtu		78.80 137	79.26 113	77.43 260	88.06 164	49.71 309	91.96 116	91.95 128	77.67 61	76.56 102	91.28 111	58.51 114	90.20 267	56.37 236	80.95 133	92.39 132
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 138	77.43 138	82.88 136	92.21 72	64.49 154	92.05 110	96.28 9	73.48 125	71.75 147	88.26 151	60.07 101	95.32 127	45.16 275	77.58 156	88.83 171

TR-MVS		78.77 139	77.37 141	82.95 135	90.49 110	60.88 219	93.67 60	90.07 193	70.08 193	74.51 116	91.37 110	45.69 244	95.70 117	60.12 222	80.32 135	92.29 135

mvs-test1		78.74 140	77.95 129	81.14 189	83.22 225	57.13 269	93.96 50	87.78 252	75.42 87	72.68 131	90.80 115	45.08 248	94.54 149	75.08 104	77.49 160	91.74 142

thres400		78.68 141	77.43 138	82.43 150	92.21 72	64.49 154	92.05 110	96.28 9	73.48 125	71.75 147	88.26 151	60.07 101	95.32 127	45.16 275	77.58 156	87.48 197

BH-untuned		78.68 141	77.08 143	83.48 130	89.84 122	63.74 175	92.70 88	88.59 239	71.57 169	66.83 212	88.65 144	51.75 197	95.39 125	59.03 227	84.77 108	91.32 149

OMC-MVS		78.67 143	77.91 131	80.95 196	85.76 197	57.40 267	88.49 211	88.67 236	73.85 116	72.43 139	92.10 101	49.29 216	94.55 148	72.73 117	77.89 152	90.91 153

tpm		78.58 144	77.03 144	83.22 132	85.94 195	64.56 153	83.21 271	91.14 157	78.31 54	73.67 124	79.68 256	64.01 65	92.09 235	66.07 176	71.26 203	93.03 118

OpenMVS	70.45 11	78.54 145	75.92 159	86.41 56	85.93 196	71.68 11	92.74 86	92.51 107	66.49 230	64.56 228	91.96 102	43.88 254	98.10 27	54.61 241	90.65 69	89.44 168

EPMVS		78.49 146	75.98 158	86.02 64	91.21 101	69.68 33	80.23 293	91.20 154	75.25 93	72.48 137	78.11 266	54.65 165	93.69 193	57.66 234	83.04 121	94.69 63

thres100view900		78.37 147	77.01 145	82.46 146	91.89 80	63.21 186	91.19 156	96.33 5	72.28 145	70.45 156	87.89 157	60.31 97	95.32 127	45.16 275	77.58 156	88.83 171

GA-MVS		78.33 148	76.23 155	84.65 105	83.65 221	66.30 123	91.44 141	90.14 191	76.01 81	70.32 159	84.02 198	42.50 258	94.72 143	70.98 135	77.00 165	92.94 121

conf200view11		78.32 149	77.01 145	82.27 160	91.89 80	63.21 186	91.19 156	96.33 5	72.28 145	70.45 156	87.89 157	60.31 97	95.32 127	45.16 275	77.58 156	88.27 183

cascas		78.18 150	75.77 161	85.41 85	87.14 177	69.11 39	92.96 79	91.15 156	66.71 228	70.47 154	86.07 179	37.49 284	96.48 92	70.15 142	79.80 137	90.65 155

UniMVSNet_NR-MVSNet		78.15 151	77.55 135	79.98 209	84.46 210	60.26 232	92.25 101	93.20 80	77.50 64	68.88 183	86.61 173	66.10 42	92.13 233	66.38 172	62.55 262	87.54 195

thres600view7		78.00 152	76.66 150	82.03 172	91.93 77	63.69 178	91.30 150	96.33 5	72.43 141	70.46 155	87.89 157	60.31 97	94.92 137	42.64 287	76.64 166	87.48 197

FC-MVSNet-test		77.99 153	78.08 127	77.70 255	84.89 204	55.51 283	90.27 177	93.75 50	76.87 70	66.80 213	87.59 161	65.71 48	90.23 266	62.89 205	73.94 183	87.37 204

XXY-MVS		77.94 154	76.44 153	82.43 150	82.60 231	64.44 158	92.01 112	91.83 132	73.59 123	70.00 164	85.82 182	54.43 169	94.76 140	69.63 145	68.02 227	88.10 187

MS-PatchMatch		77.90 155	76.50 152	82.12 167	85.99 192	69.95 27	91.75 133	92.70 98	73.97 113	62.58 246	84.44 196	41.11 265	95.78 109	63.76 194	92.17 51	80.62 300

FMVSNet3		77.73 156	76.04 157	82.80 137	91.20 102	68.99 42	91.87 124	91.99 125	73.35 128	67.04 209	83.19 206	56.62 136	92.14 232	59.80 224	69.34 216	87.28 208

UniMVSNet (Re)		77.58 157	76.78 149	79.98 209	84.11 216	60.80 220	91.76 131	93.17 83	76.56 77	69.93 167	84.78 192	63.32 76	92.36 228	64.89 186	62.51 264	86.78 216

PatchmatchNet		77.46 158	74.63 179	85.96 66	89.55 134	70.35 21	79.97 297	89.55 210	72.23 147	70.94 152	76.91 278	57.03 126	92.79 214	54.27 243	81.17 131	94.74 62
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 159	75.65 166	82.73 139	80.38 261	67.13 84	91.85 126	90.23 185	75.09 94	69.37 175	83.39 204	53.79 177	94.44 152	71.77 125	65.00 250	86.63 220

v1neww		77.39 160	75.71 163	82.44 147	80.69 249	66.83 93	91.94 120	90.18 188	74.19 107	69.60 169	82.51 210	54.99 159	94.44 152	71.68 127	65.60 239	86.05 229

v7new		77.39 160	75.71 163	82.44 147	80.69 249	66.83 93	91.94 120	90.18 188	74.19 107	69.60 169	82.51 210	54.99 159	94.44 152	71.68 127	65.60 239	86.05 229

v6		77.39 160	75.71 163	82.44 147	80.67 251	66.82 95	91.94 120	90.18 188	74.19 107	69.60 169	82.50 213	55.00 158	94.44 152	71.68 127	65.60 239	86.05 229

CHOSEN 280x420		77.35 163	76.95 148	78.55 244	87.07 178	62.68 199	69.71 322	82.95 303	68.80 205	71.48 150	87.27 170	66.03 43	84.00 311	76.47 96	82.81 124	88.95 170

v1		77.29 164	75.57 167	82.42 153	80.61 259	66.73 103	91.96 116	90.42 176	74.41 100	69.46 172	82.12 220	55.14 153	94.40 157	71.00 132	65.04 247	86.13 225

v1141		77.28 165	75.57 167	82.42 153	80.63 255	66.73 103	91.96 116	90.42 176	74.41 100	69.46 172	82.12 220	55.09 155	94.40 157	70.99 134	65.05 246	86.12 226

divwei89l23v2f112		77.28 165	75.57 167	82.42 153	80.62 256	66.72 105	91.96 116	90.42 176	74.41 100	69.46 172	82.12 220	55.11 154	94.40 157	71.00 132	65.04 247	86.12 226

PS-MVSNAJss		77.26 167	76.31 154	80.13 206	80.64 254	59.16 250	90.63 171	91.06 159	72.80 136	68.58 188	84.57 195	53.55 179	93.96 184	72.97 113	71.96 197	87.27 209

gg-mvs-nofinetune		77.18 168	74.31 185	85.80 71	91.42 97	68.36 54	71.78 316	94.72 27	49.61 314	77.12 97	45.92 337	77.41 3	93.98 183	67.62 161	93.16 39	95.05 52

MVP-Stereo		77.12 169	76.23 155	79.79 215	81.72 237	66.34 122	89.29 197	90.88 163	70.56 187	62.01 249	82.88 207	49.34 215	94.13 173	65.55 182	93.80 28	78.88 313
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
view600		76.93 170	75.50 170	81.23 184	91.44 93	62.00 206	89.94 185	96.56 1	70.68 182	68.54 189	87.31 165	60.79 91	94.19 168	38.90 300	75.31 173	87.48 197

view800		76.93 170	75.50 170	81.23 184	91.44 93	62.00 206	89.94 185	96.56 1	70.68 182	68.54 189	87.31 165	60.79 91	94.19 168	38.90 300	75.31 173	87.48 197

conf0.05thres1000		76.93 170	75.50 170	81.23 184	91.44 93	62.00 206	89.94 185	96.56 1	70.68 182	68.54 189	87.31 165	60.79 91	94.19 168	38.90 300	75.31 173	87.48 197

tfpn		76.93 170	75.50 170	81.23 184	91.44 93	62.00 206	89.94 185	96.56 1	70.68 182	68.54 189	87.31 165	60.79 91	94.19 168	38.90 300	75.31 173	87.48 197

X-MVStestdata		76.86 174	74.13 188	85.05 95	93.22 47	63.78 173	92.92 82	92.66 101	73.99 111	78.18 84	10.19 352	55.25 148	97.41 47	79.16 78	91.58 58	93.95 95

DU-MVS		76.86 174	75.84 160	79.91 211	82.96 229	60.26 232	91.26 151	91.54 141	76.46 78	68.88 183	86.35 175	56.16 140	92.13 233	66.38 172	62.55 262	87.35 206

v7		76.83 176	75.01 177	82.29 159	80.35 262	66.70 107	91.68 135	89.97 197	73.47 127	69.22 177	82.22 217	52.52 190	94.43 156	69.73 144	65.96 238	85.74 240

WR-MVS		76.76 177	75.74 162	79.82 214	84.60 206	62.27 204	92.60 93	92.51 107	76.06 80	67.87 200	85.34 185	56.76 131	90.24 265	62.20 211	63.69 261	86.94 214

v1144		76.73 178	74.88 178	82.27 160	80.23 269	66.60 111	91.68 135	90.21 187	73.69 120	69.06 180	81.89 225	52.73 189	94.40 157	69.21 150	65.23 243	85.80 236

IterMVS-LS		76.49 179	75.18 176	80.43 200	84.49 209	62.74 197	90.64 169	88.80 234	72.40 142	65.16 224	81.72 228	60.98 90	92.27 231	67.74 159	64.65 254	86.29 222
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 180	74.55 182	82.19 165	79.14 283	67.82 66	90.26 178	89.42 214	73.75 119	68.63 187	81.89 225	51.31 201	94.09 175	71.69 126	64.84 251	84.66 252

tfpn_ndepth		76.45 181	75.22 175	80.14 204	90.97 104	58.92 252	90.11 180	93.24 77	65.96 234	67.37 207	90.52 120	66.67 37	92.29 230	37.71 306	74.44 179	89.21 169

Test4		76.45 181	73.45 204	85.45 83	76.07 305	67.61 72	88.38 214	90.83 164	76.71 74	53.06 293	79.65 258	31.61 307	94.35 161	78.47 83	86.22 100	94.40 76

v148		76.19 183	74.47 184	81.36 181	80.05 273	64.44 158	91.75 133	90.23 185	73.68 121	67.13 208	80.84 241	55.92 146	93.86 190	68.95 152	61.73 271	85.76 239

Effi-MVS+-dtu		76.14 184	75.28 174	78.72 243	83.22 225	55.17 285	89.87 189	87.78 252	75.42 87	67.98 197	81.43 230	45.08 248	92.52 222	75.08 104	71.63 198	88.48 178

FMVSNet2		76.07 185	74.01 190	82.26 163	88.85 146	67.66 70	91.33 148	91.61 139	70.84 179	65.98 214	82.25 216	48.03 225	92.00 237	58.46 230	68.73 222	87.10 210

v144192		76.05 186	74.03 189	82.12 167	79.50 278	66.55 115	91.39 143	89.71 208	72.30 144	68.17 195	81.33 233	51.75 197	94.03 181	67.94 157	64.19 256	85.77 237

NR-MVSNet		76.05 186	74.59 180	80.44 199	82.96 229	62.18 205	90.83 163	91.73 134	77.12 67	60.96 250	86.35 175	59.28 108	91.80 239	60.74 218	61.34 275	87.35 206

v1192		75.98 188	73.92 192	82.15 166	79.73 274	66.24 125	91.22 153	89.75 202	72.67 137	68.49 193	81.42 231	49.86 211	94.27 165	67.08 165	65.02 249	85.95 233

TranMVSNet+NR-MVSNet		75.86 189	74.52 183	79.89 212	82.44 232	60.64 227	91.37 146	91.37 150	76.63 75	67.65 202	86.21 178	52.37 193	91.55 250	61.84 213	60.81 278	87.48 197

LPG-MVS_test		75.82 190	74.58 181	79.56 220	84.31 213	59.37 247	90.44 172	89.73 205	69.49 197	64.86 225	88.42 146	38.65 272	94.30 163	72.56 118	72.76 191	85.01 249

GBi-Net		75.65 191	73.83 193	81.10 191	88.85 146	65.11 145	90.01 181	90.32 179	70.84 179	67.04 209	80.25 250	48.03 225	91.54 251	59.80 224	69.34 216	86.64 217

test1		75.65 191	73.83 193	81.10 191	88.85 146	65.11 145	90.01 181	90.32 179	70.84 179	67.04 209	80.25 250	48.03 225	91.54 251	59.80 224	69.34 216	86.64 217

v1921920		75.63 193	73.49 203	82.06 171	79.38 279	66.35 121	91.07 160	89.48 211	71.98 154	67.99 196	81.22 236	49.16 219	93.90 187	66.56 170	64.56 255	85.92 235

ACMP	71.68 10	75.58 194	74.23 187	79.62 218	84.97 203	59.64 242	90.80 164	89.07 228	70.39 190	62.95 242	87.30 169	38.28 275	93.87 188	72.89 114	71.45 201	85.36 246
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 195	73.26 206	81.61 179	80.67 251	66.82 95	89.54 194	89.27 218	71.65 166	63.30 240	80.30 249	54.99 159	94.06 177	67.33 164	62.33 265	83.94 257

tpm cat1		75.30 196	72.21 217	84.58 107	88.52 153	67.77 67	78.16 308	88.02 249	61.88 271	68.45 194	76.37 279	60.65 95	94.03 181	53.77 246	74.11 181	91.93 140

tfpn1000		75.25 197	74.00 191	79.03 235	90.30 114	57.56 266	88.55 210	93.36 72	64.14 254	65.17 223	89.76 138	67.06 34	91.46 256	34.54 321	73.09 189	88.06 188

PLC	68.80 14	75.23 198	73.68 195	79.86 213	92.93 55	58.68 256	90.64 169	88.30 244	60.90 275	64.43 231	90.53 119	42.38 259	94.57 146	56.52 235	76.54 167	86.33 221
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 199	72.98 208	81.88 173	79.20 281	66.00 128	90.75 166	89.11 226	71.63 167	67.41 205	81.22 236	47.36 233	93.87 188	65.46 183	64.72 253	85.77 237

Fast-Effi-MVS+-dtu		75.04 200	73.37 205	80.07 207	80.86 244	59.52 245	91.20 155	85.38 284	71.90 155	65.20 222	84.84 191	41.46 264	92.97 205	66.50 171	72.96 190	87.73 194

dp		75.01 201	72.09 218	83.76 120	89.28 137	66.22 126	79.96 298	89.75 202	71.16 175	67.80 201	77.19 274	51.81 196	92.54 221	50.39 256	71.44 202	92.51 131

Patchmatch-test1		75.00 202	71.80 221	84.58 107	86.63 183	70.08 24	81.06 286	89.19 221	71.60 168	70.01 163	77.16 276	45.53 245	88.63 285	51.79 252	73.27 186	95.02 56

conf0.01		74.95 203	73.61 196	78.96 236	89.65 125	56.94 272	87.72 226	93.45 61	65.14 242	65.68 215	89.99 129	65.09 52	91.67 242	35.16 313	70.61 205	88.27 183

conf0.002		74.95 203	73.61 196	78.96 236	89.65 125	56.94 272	87.72 226	93.45 61	65.14 242	65.68 215	89.99 129	65.09 52	91.67 242	35.16 313	70.61 205	88.27 183

TAPA-MVS	70.22 12	74.94 205	73.53 202	79.17 232	90.40 112	52.07 297	89.19 200	89.61 209	62.69 264	70.07 162	92.67 92	48.89 222	94.32 162	38.26 305	79.97 136	91.12 151
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thresconf0.02		74.92 206	73.61 196	78.85 239	89.65 125	56.94 272	87.72 226	93.45 61	65.14 242	65.68 215	89.99 129	65.09 52	91.67 242	35.16 313	70.61 205	87.94 189

tfpn_n400		74.92 206	73.61 196	78.85 239	89.65 125	56.94 272	87.72 226	93.45 61	65.14 242	65.68 215	89.99 129	65.09 52	91.67 242	35.16 313	70.61 205	87.94 189

tfpnconf		74.92 206	73.61 196	78.85 239	89.65 125	56.94 272	87.72 226	93.45 61	65.14 242	65.68 215	89.99 129	65.09 52	91.67 242	35.16 313	70.61 205	87.94 189

tfpnview11		74.92 206	73.61 196	78.85 239	89.65 125	56.94 272	87.72 226	93.45 61	65.14 242	65.68 215	89.99 129	65.09 52	91.67 242	35.16 313	70.61 205	87.94 189

v10		74.77 210	72.54 214	81.46 180	80.33 266	66.71 106	89.15 201	89.08 227	70.94 177	63.08 241	79.86 254	52.52 190	94.04 180	65.70 181	62.17 266	83.64 259

XVG-OURS-SEG-HR		74.70 211	73.08 207	79.57 219	78.25 291	57.33 268	80.49 289	87.32 258	63.22 260	68.76 185	90.12 128	44.89 251	91.59 249	70.55 140	74.09 182	89.79 163

ACMM	69.62 13	74.34 212	72.73 210	79.17 232	84.25 215	57.87 260	90.36 175	89.93 198	63.17 261	65.64 221	86.04 181	37.79 282	94.10 174	65.89 178	71.52 200	85.55 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 213	72.30 216	80.32 201	91.49 92	61.66 212	90.85 162	80.72 310	56.67 298	63.85 235	90.64 116	46.75 236	90.84 259	53.79 245	75.99 170	88.47 179

XVG-OURS		74.25 214	72.46 215	79.63 217	78.45 290	57.59 265	80.33 291	87.39 255	63.86 256	68.76 185	89.62 139	40.50 267	91.72 241	69.00 151	74.25 180	89.58 166

CVMVSNet		74.04 215	74.27 186	73.33 286	85.33 198	43.94 323	89.53 195	88.39 242	54.33 304	70.37 158	90.13 126	49.17 218	84.05 308	61.83 214	79.36 140	91.99 139

Baseline_NR-MVSNet		73.99 216	72.83 209	77.48 259	80.78 245	59.29 249	91.79 128	84.55 288	68.85 204	68.99 181	80.70 242	56.16 140	92.04 236	62.67 208	60.98 277	81.11 294

pmmvs4		73.92 217	71.81 220	80.25 203	79.17 282	65.24 141	87.43 236	87.26 260	67.64 223	63.46 238	83.91 199	48.96 221	91.53 254	62.94 204	65.49 242	83.96 256

CR-MVSNet		73.79 218	70.82 227	82.70 140	83.15 227	67.96 64	70.25 319	84.00 293	73.67 122	69.97 165	72.41 298	57.82 119	89.48 281	52.99 250	73.13 187	90.64 156

test_djsdf		73.76 219	72.56 213	77.39 261	77.00 299	53.93 290	89.07 203	90.69 168	65.80 235	63.92 233	82.03 223	43.14 257	92.67 218	72.83 115	68.53 223	85.57 242

pmmvs5		73.35 220	71.52 222	78.86 238	78.64 289	60.61 228	91.08 159	86.90 261	67.69 220	63.32 239	83.64 200	44.33 253	90.53 260	62.04 212	66.02 237	85.46 244

jajsoiax		73.05 221	71.51 223	77.67 256	77.46 296	54.83 286	88.81 206	90.04 195	69.13 201	62.85 244	83.51 202	31.16 310	92.75 215	70.83 136	69.80 212	85.43 245

LCM-MVSNet-Re		72.93 222	71.84 219	76.18 271	88.49 154	48.02 313	80.07 296	70.17 335	73.96 114	52.25 296	80.09 253	49.98 209	88.24 291	67.35 162	84.23 117	92.28 136

pm-mvs1		72.89 223	71.09 225	78.26 249	79.10 285	57.62 264	90.80 164	89.30 217	67.66 221	62.91 243	81.78 227	49.11 220	92.95 206	60.29 221	58.89 287	84.22 255

tpmvs		72.88 224	69.76 232	82.22 164	90.98 103	67.05 86	78.22 307	88.30 244	63.10 262	64.35 232	74.98 286	55.09 155	94.27 165	43.25 281	69.57 215	85.34 247

test0.0.03 1		72.76 225	72.71 211	72.88 290	80.25 268	47.99 314	91.22 153	89.45 212	71.51 172	62.51 247	87.66 160	53.83 175	85.06 304	50.16 257	67.84 230	85.58 241

mvs_tets		72.71 226	71.11 224	77.52 257	77.41 297	54.52 288	88.45 213	89.76 201	68.76 206	62.70 245	83.26 205	29.49 314	92.71 216	70.51 141	69.62 214	85.34 247

FMVSNet1		72.71 226	69.91 230	81.10 191	83.60 222	65.11 145	90.01 181	90.32 179	63.92 255	63.56 237	80.25 250	36.35 292	91.54 251	54.46 242	66.75 234	86.64 217

IterMVS		72.65 228	70.83 226	78.09 253	82.17 233	62.96 190	87.64 234	86.28 273	71.56 170	60.44 252	78.85 263	45.42 247	86.66 299	63.30 198	61.83 268	84.65 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchMatch-RL		72.06 229	69.98 228	78.28 247	89.51 135	55.70 282	83.49 265	83.39 299	61.24 274	63.72 236	82.76 208	34.77 297	93.03 204	53.37 249	77.59 155	86.12 226

v18		71.94 230	69.43 233	79.50 222	80.74 246	66.82 95	88.16 216	86.66 263	68.95 203	55.55 276	72.66 293	55.03 157	90.15 268	64.78 187	52.30 304	81.54 282

v16		71.81 231	69.26 235	79.47 223	80.66 253	66.81 99	87.93 220	86.63 265	68.70 208	55.35 277	72.51 294	54.75 163	90.12 270	64.51 189	52.28 305	81.47 283

PVSNet_0	68.08 15	71.81 231	68.32 245	82.27 160	84.68 205	62.31 203	88.68 208	90.31 182	75.84 82	57.93 267	80.65 244	37.85 281	94.19 168	69.94 143	29.05 340	90.31 159

v17		71.77 233	69.20 236	79.46 224	80.62 256	66.81 99	87.93 220	86.63 265	68.71 207	55.25 278	72.49 295	54.72 164	90.11 271	64.50 190	51.97 306	81.47 283

MIMVSNet		71.64 234	68.44 243	81.23 184	81.97 236	64.44 158	73.05 315	88.80 234	69.67 196	64.59 227	74.79 287	32.79 301	87.82 294	53.99 244	76.35 168	91.42 145

v15		71.40 235	68.75 238	79.35 225	80.39 260	66.70 107	87.57 235	86.64 264	68.66 209	54.68 280	72.00 302	54.50 166	89.98 273	63.69 195	50.66 311	81.38 287

v7n		71.31 236	68.65 239	79.28 227	76.40 301	60.77 222	86.71 248	89.45 212	64.17 253	58.77 263	78.24 265	44.59 252	93.54 195	57.76 232	61.75 270	83.52 262

V14		71.29 237	68.61 240	79.31 226	80.34 264	66.65 109	87.39 237	86.61 267	68.41 215	54.49 282	71.91 303	54.25 171	89.96 274	63.50 196	50.62 312	81.33 289

V9		71.16 238	68.46 242	79.27 228	80.26 267	66.60 111	87.21 240	86.56 268	68.17 216	54.26 285	71.81 305	54.00 173	89.93 275	63.28 199	50.57 313	81.27 290

anonymousdsp		71.14 239	69.37 234	76.45 268	72.95 311	54.71 287	84.19 260	88.88 233	61.92 270	62.15 248	79.77 255	38.14 277	91.44 257	68.90 153	67.45 231	83.21 268

testing_2		71.09 240	67.32 253	82.40 156	69.82 322	66.52 117	83.64 263	90.77 166	72.21 148	45.12 319	71.07 312	27.60 319	93.74 191	75.71 99	69.96 211	86.95 213

v11		71.05 241	68.32 245	79.23 229	80.34 264	66.57 114	87.01 243	86.55 269	68.11 217	54.40 283	71.66 307	52.94 187	89.91 276	62.71 207	51.12 309	81.21 291

v12		71.02 242	68.29 247	79.22 230	80.18 270	66.53 116	87.01 243	86.54 270	67.90 218	54.00 288	71.70 306	53.66 178	89.91 276	63.09 201	50.51 314	81.21 291

v13		70.90 243	68.15 248	79.15 234	80.08 271	66.45 118	86.83 247	86.50 271	67.62 224	53.78 290	71.61 308	53.51 182	89.87 278	62.89 205	50.50 315	81.14 293

F-COLMAP		70.66 244	68.44 243	77.32 262	86.37 187	55.91 281	88.00 218	86.32 272	56.94 296	57.28 272	88.07 155	33.58 299	92.49 223	51.02 254	68.37 224	83.55 260

WR-MVS_H		70.59 245	69.94 229	72.53 292	81.03 242	51.43 300	87.35 238	92.03 124	67.38 225	60.23 253	80.70 242	55.84 147	83.45 315	46.33 271	58.58 288	82.72 275

v748		70.55 246	67.97 249	78.27 248	75.75 306	58.78 254	86.29 252	89.25 219	65.12 248	56.66 274	77.17 275	45.05 250	92.95 206	58.13 231	58.33 289	83.10 271

CP-MVSNet		70.50 247	69.91 230	72.26 295	80.71 248	51.00 303	87.23 239	90.30 183	67.84 219	59.64 255	82.69 209	50.23 208	82.30 322	51.28 253	59.28 282	83.46 264

tfpnnormal		70.10 248	67.36 251	78.32 246	83.45 224	60.97 218	88.85 205	92.77 96	64.85 249	60.83 251	78.53 264	43.52 256	93.48 197	31.73 329	61.70 272	80.52 301

TransMVSNet (Re)		70.07 249	67.66 250	77.31 263	80.62 256	59.13 251	91.78 130	84.94 286	65.97 233	60.08 254	80.44 246	50.78 202	91.87 238	48.84 262	45.46 324	80.94 296

DP-MVS		69.90 250	66.48 256	80.14 204	95.36 15	62.93 191	89.56 192	76.11 318	50.27 313	57.69 270	85.23 187	39.68 269	95.73 112	33.35 323	71.05 204	81.78 281

PS-CasMVS		69.86 251	69.13 237	72.07 298	80.35 262	50.57 305	87.02 242	89.75 202	67.27 226	59.19 258	82.28 215	46.58 239	82.24 323	50.69 255	59.02 285	83.39 266

v52		69.80 252	67.01 255	78.15 251	71.84 315	60.10 236	82.02 277	87.39 255	64.48 250	57.80 268	75.97 282	41.47 263	92.90 211	63.00 202	59.13 284	81.45 285

V4		69.80 252	67.02 254	78.15 251	71.86 314	60.10 236	82.02 277	87.39 255	64.48 250	57.78 269	75.98 281	41.49 262	92.90 211	63.00 202	59.16 283	81.44 286

RPMNet		69.58 254	65.21 264	82.70 140	83.15 227	67.96 64	70.25 319	86.15 276	46.83 322	69.97 165	65.10 323	56.48 139	89.48 281	35.79 312	73.13 187	90.64 156

MSDG		69.54 255	65.73 259	80.96 195	85.11 202	63.71 177	84.19 260	83.28 300	56.95 295	54.50 281	84.03 197	31.50 308	96.03 102	42.87 285	69.13 219	83.14 270

PEN-MVS		69.46 256	68.56 241	72.17 297	79.27 280	49.71 309	86.90 245	89.24 220	67.24 227	59.08 259	82.51 210	47.23 234	83.54 314	48.42 264	57.12 290	83.25 267

LS3D		69.17 257	66.40 257	77.50 258	91.92 78	56.12 280	85.12 255	80.37 311	46.96 320	56.50 275	87.51 163	37.25 285	93.71 192	32.52 328	79.40 139	82.68 276

PatchT		69.11 258	65.37 263	80.32 201	82.07 235	63.68 179	67.96 328	87.62 254	50.86 312	69.37 175	65.18 322	57.09 124	88.53 289	41.59 290	66.60 235	88.74 174

ACMH	63.93 17	68.62 259	64.81 265	80.03 208	85.22 200	63.25 184	87.72 226	84.66 287	60.83 276	51.57 299	79.43 260	27.29 320	94.96 134	41.76 288	64.84 251	81.88 280
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 260	65.41 262	77.96 254	78.69 288	62.93 191	89.86 190	89.17 222	60.55 277	50.27 304	77.73 269	22.60 328	94.06 177	47.18 269	72.65 193	76.88 321

ADS-MVSNet		68.54 261	64.38 271	81.03 194	88.06 164	66.90 92	68.01 326	84.02 292	57.57 290	64.48 229	69.87 313	38.68 270	89.21 284	40.87 292	67.89 228	86.97 211

DTE-MVSNet		68.46 262	67.33 252	71.87 301	77.94 294	49.00 312	86.16 253	88.58 240	66.36 231	58.19 265	82.21 218	46.36 240	83.87 312	44.97 279	55.17 297	82.73 274

Patchmatch-RL test		68.17 263	64.49 269	79.19 231	71.22 317	53.93 290	70.07 321	71.54 334	69.22 200	56.79 273	62.89 326	56.58 137	88.61 286	69.53 147	52.61 303	95.03 55

XVG-ACMP-BASELINE		68.04 264	65.53 261	75.56 273	74.06 310	52.37 295	78.43 304	85.88 281	62.03 268	58.91 262	81.21 238	20.38 331	91.15 258	60.69 219	68.18 225	83.16 269

FMVSNet5		68.04 264	65.66 260	75.18 275	84.43 211	57.89 259	83.54 264	86.26 274	61.83 272	53.64 291	73.30 290	37.15 288	85.08 303	48.99 261	61.77 269	82.56 277

ACMH+	65.35 16	67.65 266	64.55 267	76.96 265	84.59 207	57.10 270	88.08 217	80.79 309	58.59 289	53.00 294	81.09 240	26.63 322	92.95 206	46.51 270	61.69 273	80.82 297

pmmvs6		67.57 267	64.76 266	76.00 272	72.82 313	53.37 292	88.71 207	86.78 262	53.19 305	57.58 271	78.03 267	35.33 295	92.41 225	55.56 239	54.88 299	82.21 278

Anonymous20231206		67.53 268	65.78 258	72.79 291	74.95 307	47.59 316	88.23 215	87.32 258	61.75 273	58.07 266	77.29 272	37.79 282	87.29 297	42.91 283	63.71 260	83.48 263

Patchmtry		67.53 268	63.93 272	78.34 245	82.12 234	64.38 161	68.72 323	84.00 293	48.23 319	59.24 257	72.41 298	57.82 119	89.27 283	46.10 272	56.68 294	81.36 288

USDC		67.43 270	64.51 268	76.19 270	77.94 294	55.29 284	78.38 305	85.00 285	73.17 129	48.36 309	80.37 247	21.23 330	92.48 224	52.15 251	64.02 258	80.81 298

ADS-MVSNet2		66.90 271	63.44 274	77.26 264	88.06 164	60.70 225	68.01 326	75.56 323	57.57 290	64.48 229	69.87 313	38.68 270	84.10 307	40.87 292	67.89 228	86.97 211

CMPMVS	48.56 21	66.77 272	64.41 270	73.84 283	70.65 320	50.31 306	77.79 309	85.73 283	45.54 325	44.76 320	82.14 219	35.40 294	90.14 269	63.18 200	74.54 178	81.07 295
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 273	62.92 278	76.80 267	76.51 300	57.77 261	89.22 198	83.41 298	55.48 302	53.86 289	77.84 268	26.28 323	93.95 185	34.90 320	68.76 221	78.68 315

LTVRE_ROB	59.60 19	66.27 274	63.54 273	74.45 279	84.00 218	51.55 299	67.08 329	83.53 296	58.78 287	54.94 279	80.31 248	34.54 298	93.23 201	40.64 294	68.03 226	78.58 316
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
JIA-IIPM		66.06 275	62.45 281	76.88 266	81.42 241	54.45 289	57.49 340	88.67 236	49.36 315	63.86 234	46.86 336	56.06 143	90.25 263	49.53 260	68.83 220	85.95 233

Patchmatch-test		65.86 276	60.94 287	80.62 198	83.75 219	58.83 253	58.91 339	75.26 325	44.50 329	50.95 303	77.09 277	58.81 113	87.90 293	35.13 319	64.03 257	95.12 50

UnsupCasMVSNet_eth		65.79 277	63.10 276	73.88 282	70.71 319	50.29 307	81.09 285	89.88 199	72.58 139	49.25 307	74.77 288	32.57 303	87.43 296	55.96 238	41.04 330	83.90 258

pmmvs-eth3d		65.53 278	62.32 282	75.19 274	69.39 324	59.59 243	82.80 274	83.43 297	62.52 266	51.30 301	72.49 295	32.86 300	87.16 298	55.32 240	50.73 310	78.83 314

SixPastTwentyTwo		64.92 279	61.78 285	74.34 281	78.74 287	49.76 308	83.42 268	79.51 314	62.86 263	50.27 304	77.35 270	30.92 312	90.49 261	45.89 273	47.06 321	82.78 272

OurMVSNet-221017-0		64.68 280	62.17 283	72.21 296	76.08 304	47.35 317	80.67 288	81.02 308	56.19 299	51.60 298	79.66 257	27.05 321	88.56 288	53.60 247	53.63 302	80.71 299

test_0402		64.54 281	61.09 286	74.92 276	84.10 217	60.75 223	87.95 219	79.71 313	52.03 308	52.41 295	77.20 273	32.21 305	91.64 248	23.14 339	61.03 276	72.36 328

testgi		64.48 282	62.87 279	69.31 305	71.24 316	40.62 330	85.49 254	79.92 312	65.36 239	54.18 286	83.49 203	23.74 326	84.55 305	41.60 289	60.79 279	82.77 273

RPSCF		64.24 283	61.98 284	71.01 302	76.10 303	45.00 320	75.83 312	75.94 320	46.94 321	58.96 261	84.59 194	31.40 309	82.00 324	47.76 267	60.33 281	86.04 232

test2356		64.16 284	63.28 275	66.81 312	69.37 325	39.86 333	87.76 225	86.02 278	59.83 282	53.54 292	73.23 291	34.94 296	80.67 327	39.66 296	65.20 244	79.89 306

EU-MVSNet		64.01 285	63.01 277	67.02 311	74.40 309	38.86 335	83.27 269	86.19 275	45.11 326	54.27 284	81.15 239	36.91 291	80.01 328	48.79 263	57.02 291	82.19 279

test20.03		63.83 286	62.65 280	67.38 310	70.58 321	39.94 331	86.57 250	84.17 290	63.29 259	51.86 297	77.30 271	37.09 289	82.47 320	38.87 304	54.13 301	79.73 308

MDA-MVSNet_test_wron		63.78 287	60.16 288	74.64 277	78.15 292	60.41 229	83.49 265	84.03 291	56.17 301	39.17 332	71.59 310	37.22 286	83.24 318	42.87 285	48.73 318	80.26 304

YYNet1		63.76 288	60.14 289	74.62 278	78.06 293	60.19 235	83.46 267	83.99 295	56.18 300	39.25 331	71.56 311	37.18 287	83.34 316	42.90 284	48.70 319	80.32 303

K. test v3		63.09 289	59.61 291	73.53 285	76.26 302	49.38 311	83.27 269	77.15 317	64.35 252	47.77 310	72.32 300	28.73 315	87.79 295	49.93 259	36.69 335	83.41 265

COLMAP_ROB	57.96 20	62.98 290	59.65 290	72.98 289	81.44 240	53.00 294	83.75 262	75.53 324	48.34 318	48.81 308	81.40 232	24.14 324	90.30 262	32.95 325	60.52 280	75.65 324
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		61.66 291	58.06 292	72.46 293	79.57 275	51.42 301	80.17 294	68.61 337	51.25 310	45.88 314	81.23 234	19.86 332	86.58 300	38.98 298	57.01 292	79.39 310

UnsupCasMVSNet_bld		61.60 292	57.71 293	73.29 287	68.73 326	51.64 298	78.61 303	89.05 229	57.20 294	46.11 313	61.96 328	28.70 316	88.60 287	50.08 258	38.90 333	79.63 309

MDA-MVSNet-bldmvs		61.54 293	57.70 294	73.05 288	79.53 277	57.00 271	83.08 272	81.23 306	57.57 290	34.91 335	72.45 297	32.79 301	86.26 302	35.81 311	41.95 328	75.89 323

TinyColmap		60.32 294	56.42 300	72.00 299	78.78 286	53.18 293	78.36 306	75.64 321	52.30 307	41.59 330	75.82 284	14.76 339	88.35 290	35.84 310	54.71 300	74.46 325

MVS-HIRNet		60.25 295	55.55 301	74.35 280	84.37 212	56.57 278	71.64 317	74.11 327	34.44 338	45.54 318	42.24 340	31.11 311	89.81 279	40.36 295	76.10 169	76.67 322

MIMVSNet1		60.16 296	57.33 296	68.67 306	69.71 323	44.13 322	78.92 302	84.21 289	55.05 303	44.63 321	71.85 304	23.91 325	81.54 326	32.63 327	55.03 298	80.35 302

PM-MVS		59.40 297	56.59 298	67.84 307	63.63 330	41.86 327	76.76 310	63.22 343	59.01 286	51.07 302	72.27 301	11.72 341	83.25 317	61.34 215	50.28 316	78.39 317

testus		59.36 298	57.51 295	64.90 315	66.72 327	37.56 336	84.98 256	81.09 307	57.46 293	47.72 311	72.76 292	11.43 343	78.78 334	36.56 307	58.91 286	78.36 318

new-patchmatchnet		59.30 299	56.48 299	67.79 308	65.86 328	44.19 321	82.47 275	81.77 304	59.94 281	43.65 325	66.20 319	27.67 318	81.68 325	39.34 297	41.40 329	77.50 320

testpf		57.17 300	56.93 297	57.88 322	79.13 284	42.40 324	34.23 346	85.97 280	52.64 306	47.66 312	66.50 317	36.33 293	79.65 330	53.60 247	56.31 295	51.60 341

DSMNet-mixed		56.78 301	54.44 303	63.79 317	63.21 331	29.44 344	64.43 332	64.10 342	42.12 334	51.32 300	71.60 309	31.76 306	75.04 337	36.23 309	65.20 244	86.87 215

LP		56.71 302	51.64 306	71.91 300	80.08 271	60.33 231	61.72 334	75.61 322	43.87 331	43.76 324	60.30 330	30.46 313	84.05 308	22.94 340	46.06 323	71.34 330

1111		56.66 303	54.98 302	61.69 318	61.99 334	31.38 340	79.81 299	83.17 301	45.66 323	41.94 327	65.44 320	41.50 260	79.56 331	27.64 333	47.68 320	74.14 326

test1235678		55.73 304	52.74 304	64.68 316	60.16 337	35.56 338	81.65 281	81.46 305	51.27 309	38.93 333	62.82 327	17.44 334	78.58 335	30.87 331	50.09 317	79.89 306

pmmvs3		55.51 305	51.50 308	67.53 309	57.90 339	50.93 304	80.37 290	73.66 328	40.63 335	44.15 323	64.75 324	16.30 335	78.97 333	44.77 280	40.98 331	72.69 327

TDRefinement		55.28 306	51.58 307	66.39 313	59.53 338	46.15 319	76.23 311	72.80 329	44.60 328	42.49 326	76.28 280	15.29 337	82.39 321	33.20 324	43.75 326	70.62 332

LF4IMVS		54.01 307	52.12 305	59.69 320	62.41 333	39.91 332	68.59 324	68.28 339	42.96 332	44.55 322	75.18 285	14.09 340	68.39 341	41.36 291	51.68 307	70.78 331

Anonymous20231211		53.57 308	49.43 310	66.00 314	65.01 329	42.08 325	80.95 287	72.60 330	38.46 336	41.65 329	64.48 325	15.72 336	84.23 306	25.78 336	40.24 332	71.68 329

N_pmnet		50.55 309	49.11 311	54.88 326	77.17 298	4.02 356	84.36 259	2.00 356	48.59 316	45.86 316	68.82 315	32.22 304	82.80 319	31.58 330	51.38 308	77.81 319

new_pmnet		49.31 310	46.44 312	57.93 321	62.84 332	40.74 329	68.47 325	62.96 344	36.48 337	35.09 334	57.81 332	14.97 338	72.18 338	32.86 326	46.44 322	60.88 339

test12356		47.51 311	44.82 313	55.56 324	52.53 340	21.09 351	71.45 318	76.03 319	44.14 330	30.69 336	58.18 331	9.01 347	76.14 336	26.95 335	34.43 338	69.46 334

testmv		46.98 312	43.53 314	57.35 323	47.75 345	30.41 343	74.99 314	77.69 315	42.84 333	28.03 337	53.36 333	8.18 348	71.18 339	24.36 338	34.55 336	70.46 333

.test1245		46.52 313	49.68 309	37.02 335	61.99 334	31.38 340	79.81 299	83.17 301	45.66 323	41.94 327	65.44 320	41.50 260	79.56 331	27.64 333	0.01 352	0.13 353

FPMVS		45.64 314	43.10 315	53.23 328	51.42 342	36.46 337	64.97 331	71.91 332	29.13 340	27.53 338	61.55 329	9.83 345	65.01 345	16.00 345	55.58 296	58.22 340

no-one		44.13 315	38.39 316	61.34 319	45.91 347	41.94 326	61.67 335	75.07 326	45.05 327	20.07 341	40.68 343	11.58 342	79.82 329	30.18 332	15.30 343	62.26 338

LCM-MVSNet		40.54 316	35.79 317	54.76 327	36.92 350	30.81 342	51.41 341	69.02 336	22.07 342	24.63 339	45.37 338	4.56 353	65.81 343	33.67 322	34.50 337	67.67 335

ANet_high		40.27 317	35.20 318	55.47 325	34.74 351	34.47 339	63.84 333	71.56 333	48.42 317	18.80 343	41.08 341	9.52 346	64.45 346	20.18 342	8.66 350	67.49 336

PMMVS2		37.93 318	33.61 319	50.92 329	46.31 346	24.76 349	60.55 338	50.05 347	28.94 341	20.93 340	47.59 335	4.41 354	65.13 344	25.14 337	18.55 342	62.87 337

Gipuma		34.91 319	31.44 321	45.30 331	70.99 318	39.64 334	19.85 349	72.56 331	20.10 345	16.16 345	21.47 348	5.08 352	71.16 340	13.07 346	43.70 327	25.08 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		32.77 320	29.98 322	41.11 333	48.05 343	29.17 345	65.82 330	50.02 348	21.42 343	14.74 346	37.19 344	1.11 357	55.11 348	19.75 343	11.77 345	39.06 343

PMVS	26.43 22	31.84 321	28.16 323	42.89 332	25.87 354	27.58 347	50.92 342	49.78 349	21.37 344	14.17 347	40.81 342	2.01 355	66.62 342	9.61 348	38.88 334	34.49 346
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pcd1.5k->3k		31.17 322	31.85 320	29.12 337	81.48 238	0.00 359	0.00 350	91.79 133	0.00 354	0.00 355	0.00 356	41.05 266	0.00 357	0.00 354	72.34 196	87.36 205

wuykxyi23d		29.03 323	23.09 328	46.84 330	31.67 353	28.82 346	43.46 344	57.72 346	14.39 348	7.52 351	20.84 349	0.64 358	60.29 347	21.57 341	10.04 347	51.40 342

E-PMN		24.61 324	24.00 325	26.45 338	43.74 348	18.44 353	60.86 336	39.66 350	15.11 346	9.53 349	22.10 347	6.52 350	46.94 350	8.31 349	10.14 346	13.98 349

MVE	24.84 23	24.35 325	19.77 329	38.09 334	34.56 352	26.92 348	26.57 347	38.87 352	11.73 349	11.37 348	27.44 345	1.37 356	50.42 349	11.41 347	14.60 344	36.93 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 326	23.20 327	25.46 339	41.52 349	16.90 354	60.56 337	38.79 353	14.62 347	8.99 350	20.24 351	7.35 349	45.82 351	7.25 350	9.46 348	13.64 350

tmp_tt		22.26 327	23.75 326	17.80 340	5.23 355	12.06 355	35.26 345	39.48 351	2.82 351	18.94 342	44.20 339	22.23 329	24.64 353	36.30 308	9.31 349	16.69 348

cdsmvs_eth3d_5k		19.86 328	26.47 324	0.00 344	0.00 358	0.00 359	0.00 350	93.45 61	0.00 354	0.00 355	95.27 32	49.56 213	0.00 357	0.00 354	0.00 354	0.00 355

wuyk23d		11.30 329	10.95 330	12.33 341	48.05 343	19.89 352	25.89 348	1.92 357	3.58 350	3.12 352	1.37 353	0.64 358	15.77 354	6.23 351	7.77 351	1.35 351

ab-mvs-re		7.91 330	10.55 331	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	94.95 42	0.00 362	0.00 357	0.00 354	0.00 354	0.00 355

testmvs		7.23 331	9.62 332	0.06 343	0.04 356	0.02 358	84.98 256	0.02 358	0.03 352	0.18 353	1.21 354	0.01 361	0.02 355	0.14 352	0.01 352	0.13 353

test123		6.92 332	9.21 333	0.08 342	0.03 357	0.05 357	81.65 281	0.01 359	0.02 353	0.14 354	0.85 355	0.03 360	0.02 355	0.12 353	0.00 354	0.16 352

pcd_1.5k_mvsjas		4.46 333	5.95 334	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	53.55 179	0.00 357	0.00 354	0.00 354	0.00 355

sosnet-low-res		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 354	0.00 355

sosnet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 354	0.00 355

uncertanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 354	0.00 355

Regformer		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 354	0.00 355

uanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 354	0.00 355

GSMVS																94.68 64

test_part3							94.96 31		68.52 211		97.23 2		98.90 7	91.52 6

test_part2					96.29 7	68.16 60				90.78 3

test_part1								94.26 40				77.03 4			95.18 8	96.11 19

sam_mvs1												57.85 118				94.68 64

sam_mvs												54.91 162

semantic-postprocess				76.32 269	81.48 238	60.67 226		85.99 279	66.17 232	59.50 256	78.88 262	45.51 246	83.65 313	62.58 209	61.93 267	84.63 254

ambc				69.61 304	61.38 336	41.35 328	49.07 343	85.86 282		50.18 306	66.40 318	10.16 344	88.14 292	45.73 274	44.20 325	79.32 312

MTGPA								92.23 113

test_post1							78.95 301				20.70 350	53.05 185	91.50 255	60.43 220

test_post											23.01 346	56.49 138	92.67 218

patchmatchnet-post											67.62 316	57.62 121	90.25 263

GG-mvs-BLEND				86.53 50	91.91 79	69.67 34	75.02 313	94.75 26		78.67 83	90.85 114	77.91 2	94.56 147	72.25 121	93.74 31	95.36 36

MTMP								32.52 354

gm-plane-assit					88.42 158	67.04 87			78.62 52		91.83 104		97.37 49	76.57 95

test9_res														89.41 12	94.96 10	95.29 40

TEST9					94.18 29	67.28 79	94.16 38	93.51 58	71.75 165	85.52 28	95.33 28	68.01 25	97.27 56

test_8					94.19 28	67.19 81	94.15 40	93.42 69	71.87 157	85.38 30	95.35 27	68.19 23	96.95 76

agg_prior2														86.41 33	94.75 18	95.33 37

agg_prior					94.16 33	66.97 88		93.31 74		84.49 37			96.75 85

TestCases				72.46 293	79.57 275	51.42 301		68.61 337	51.25 310	45.88 314	81.23 234	19.86 332	86.58 300	38.98 298	57.01 292	79.39 310

test_prior4						67.18 83	93.92 54

test_prior2							95.10 27		75.40 89	85.25 32	95.61 23	67.94 26		87.47 25	94.77 15

test_prior				86.42 54	94.71 23	67.35 77		93.10 86					96.84 81			95.05 52

旧先验2							92.00 114		59.37 285	87.54 16			93.47 198	75.39 101

新几何2							91.41 142

新几何1				84.73 102	92.32 67	64.28 166		91.46 146	59.56 283	79.77 69	92.90 88	56.95 129	96.57 90	63.40 197	92.91 41	93.34 107

旧先验1					91.94 76	60.74 224		91.50 144			94.36 57	65.23 50			91.84 53	94.55 68

无先验							92.71 87	92.61 104	62.03 268				97.01 67	66.63 168		93.97 94

原ACMM2							92.01 112

原ACMM1				84.42 111	93.21 49	64.27 167		93.40 70	65.39 238	79.51 72	92.50 93	58.11 117	96.69 87	65.27 184	93.96 25	92.32 134

test222					89.77 123	61.60 213	89.55 193	89.42 214	56.83 297	77.28 95	92.43 96	52.76 188			91.14 65	93.09 116

testdata2													96.09 99	61.26 216

segment_acmp												65.94 44

testdata				81.34 182	89.02 144	57.72 262		89.84 200	58.65 288	85.32 31	94.09 66	57.03 126	93.28 200	69.34 149	90.56 71	93.03 118

testdata1							89.21 199		77.55 63

test12				87.09 35	94.60 25	68.86 44		92.91 92		82.67 50		65.44 49	97.55 43		93.69 33	94.84 60

plane_prior7					86.94 179	61.51 214

plane_prior6					87.23 175	62.32 202						50.66 203

plane_prior5								91.31 151					95.55 122	76.74 93	78.53 148	88.39 180

plane_prior4											89.14 141

plane_prior3						61.95 210			79.09 44	72.53 135

plane_prior2							93.13 74		78.81 49

plane_prior1					87.15 176

plane_prior						62.42 200	93.85 57		79.38 37						78.80 146

n2								0.00 360

nn								0.00 360

door-mid								66.01 341

lessismore_v0				73.72 284	72.93 312	47.83 315		61.72 345		45.86 316	73.76 289	28.63 317	89.81 279	47.75 268	31.37 339	83.53 261

LGP-MVS_train				79.56 220	84.31 213	59.37 247		89.73 205	69.49 197	64.86 225	88.42 146	38.65 272	94.30 163	72.56 118	72.76 191	85.01 249

test11								93.01 88

door								66.57 340

HQP5-MVS						63.66 180

HQP-NCC					87.54 171		94.06 45		79.80 33	74.18 117

ACMP_Plane					87.54 171		94.06 45		79.80 33	74.18 117

BP-MVS														77.63 90

HQP4-MVS										74.18 117			95.61 118			88.63 175

HQP3-MVS								91.70 137							78.90 144

HQP2-MVS												51.63 199

NP-MVS					87.41 174	63.04 189					90.30 123

MDTV_nov1_ep13_2view						59.90 240	80.13 295		67.65 222	72.79 130		54.33 170		59.83 223		92.58 129

MDTV_nov1_ep13			72.61 212		89.06 143	68.48 50	80.33 291	90.11 192	71.84 160	71.81 146	75.92 283	53.01 186	93.92 186	48.04 265	73.38 185

ACMMP++_ref															71.63 198

ACMMP++															69.72 213

Test By Simon												54.21 172

ITE_SJBPF				70.43 303	74.44 308	47.06 318		77.32 316	60.16 280	54.04 287	83.53 201	23.30 327	84.01 310	43.07 282	61.58 274	80.21 305

DeepMVS_CX				34.71 336	51.45 341	24.73 350		28.48 355	31.46 339	17.49 344	52.75 334	5.80 351	42.60 352	18.18 344	19.42 341	36.81 345

High-resolution multi-view benchmark