High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

CNVR-MVS		96.30 1	96.54 1	95.55 10	99.31 5	87.69 17	99.06 5	97.12 24	94.66 3	96.79 3	98.78 4	86.42 12	99.95 2	97.59 4	99.18 3	99.00 14

MCST-MVS		96.17 2	96.12 4	96.32 3	99.42 2	89.36 5	98.94 9	97.10 30	95.17 2	92.11 47	98.46 10	87.33 8	99.97 1	97.21 6	99.31 1	99.63 2

NCCC		95.63 3	95.94 5	94.69 20	99.21 6	85.15 42	99.16 3	96.96 40	94.11 6	95.59 11	98.64 6	85.07 15	99.91 3	95.61 18	99.10 5	99.00 14

HSP-MVS		95.55 4	96.51 2	92.66 84	98.31 39	80.10 145	97.42 67	96.46 85	92.20 13	97.11 2	98.29 13	93.46 1	99.10 79	96.01 13	99.30 2	98.77 22

ESAPD		95.32 5	95.52 6	94.70 19	98.90 7	85.14 43	98.15 25	96.77 51	84.95 101	96.07 7	98.83 2	89.33 6	99.80 13	97.78 2	98.95 12	99.18 10

HPM-MVS++		95.32 5	95.48 7	94.85 16	98.62 24	86.04 27	97.81 39	96.93 43	92.45 11	95.69 10	98.50 8	85.38 14	99.85 10	94.75 23	99.18 3	98.65 28

DELS-MVS		94.98 7	94.49 13	96.44 2	96.42 79	90.59 3	99.21 2	97.02 35	94.40 5	91.46 54	97.08 78	83.32 32	99.69 27	92.83 43	98.70 21	99.04 12
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
CANet		94.89 8	94.64 11	95.63 8	97.55 60	88.12 11	99.06 5	96.39 95	94.07 7	95.34 13	97.80 47	76.83 95	99.87 8	97.08 7	97.64 51	98.89 17

SD-MVS		94.84 9	95.02 9	94.29 25	97.87 53	84.61 51	97.76 45	96.19 110	89.59 32	96.66 4	98.17 21	84.33 22	99.60 36	96.09 12	98.50 26	98.66 27

TSAR-MVS + MP.		94.79 10	95.17 8	93.64 44	97.66 55	84.10 60	95.85 171	96.42 89	91.26 17	97.49 1	96.80 88	86.50 11	98.49 103	95.54 19	99.03 7	98.33 39

DeepPCF-MVS	89.82 1	94.61 11	96.17 3	89.91 168	97.09 74	70.21 288	98.99 8	96.69 61	95.57 1	95.08 17	99.23 1	86.40 13	99.87 8	97.84 1	98.66 22	99.65 1

APDe-MVS		94.56 12	94.75 10	93.96 33	98.84 11	83.40 74	98.04 30	96.41 90	85.79 80	95.00 19	98.28 14	84.32 25	99.18 72	97.35 5	98.77 17	99.28 5

DeepC-MVS_fast	89.06 2	94.48 13	94.30 19	95.02 14	98.86 10	85.68 33	98.06 29	96.64 67	93.64 8	91.74 52	98.54 7	80.17 56	99.90 4	92.28 50	98.75 18	99.49 3
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		94.35 14	94.50 12	93.89 34	97.38 68	83.04 80	98.10 28	95.29 157	91.57 15	93.81 32	97.45 62	86.64 9	99.43 51	96.28 11	94.01 96	99.20 8

train_agg		94.28 15	94.45 14	93.74 39	98.64 21	83.71 67	97.82 37	96.65 64	84.50 113	95.16 14	98.09 28	84.33 22	99.36 55	95.91 15	98.96 10	98.16 49

MSLP-MVS++		94.28 15	94.39 16	93.97 32	98.30 40	84.06 61	98.64 13	96.93 43	90.71 22	93.08 39	98.70 5	79.98 58	99.21 65	94.12 30	99.07 6	98.63 29

MG-MVS		94.25 17	93.72 24	95.85 7	99.38 3	89.35 6	97.98 32	98.09 13	89.99 29	92.34 46	96.97 81	81.30 46	98.99 85	88.54 82	98.88 14	99.20 8

PS-MVSNAJ		94.17 18	93.52 28	96.10 4	95.65 100	92.35 1	98.21 23	95.79 131	92.42 12	96.24 5	98.18 17	71.04 159	99.17 73	96.77 9	97.39 58	96.79 125

SteuartSystems-ACMMP		94.13 19	94.44 15	93.20 63	95.41 106	81.35 116	99.02 7	96.59 73	89.50 33	94.18 30	98.36 12	83.68 30	99.45 50	94.77 22	98.45 28	98.81 20
Skip Steuart: Steuart Systems R&D Blog.
agg_prior3		94.10 20	94.29 20	93.53 52	98.62 24	83.03 81	97.80 41	96.64 67	84.28 122	95.01 18	98.03 32	83.40 31	99.41 52	95.91 15	98.96 10	98.16 49

agg_prior1		94.10 20	94.31 18	93.48 55	98.59 26	83.13 77	97.77 42	96.56 75	84.38 117	94.19 28	98.13 23	84.66 19	99.16 74	95.74 17	98.74 19	98.15 51

EPNet		94.06 22	94.15 21	93.76 38	97.27 71	84.35 56	98.29 20	97.64 16	94.57 4	95.36 12	96.88 84	79.96 59	99.12 78	91.30 56	96.11 76	97.82 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_prior3		94.03 23	94.34 17	93.09 68	98.68 15	81.91 100	98.37 18	96.40 92	86.08 75	94.57 25	98.02 33	83.14 33	99.06 81	95.05 20	98.79 15	98.29 43

Regformer-1		94.00 24	94.04 22	93.87 35	98.41 34	84.29 58	97.43 65	97.04 34	89.50 33	92.75 43	98.13 23	82.60 36	99.26 60	93.55 33	96.99 64	98.06 57

xiu_mvs_v2_base		93.92 25	93.26 30	95.91 6	95.07 116	92.02 2	98.19 24	95.68 135	92.06 14	96.01 9	98.14 22	70.83 162	98.96 87	96.74 10	96.57 72	96.76 128

Regformer-2		93.92 25	94.01 23	93.67 43	98.41 34	83.75 66	97.43 65	97.00 36	89.43 35	92.69 44	98.13 23	82.48 37	99.22 63	93.51 34	96.99 64	98.04 58

lupinMVS		93.87 27	93.58 27	94.75 18	93.00 163	88.08 12	99.15 4	95.50 144	91.03 19	94.90 20	97.66 50	78.84 69	97.56 136	94.64 26	97.46 53	98.62 30

MVS_0304		93.82 28	93.11 34	95.95 5	96.79 76	89.15 7	98.56 15	95.30 156	93.61 9	94.82 22	98.02 33	66.60 194	99.88 7	96.94 8	97.39 58	98.81 20

APD-MVS		93.61 29	93.59 26	93.69 42	98.76 12	83.26 75	97.21 74	96.09 115	82.41 156	94.65 24	98.21 16	81.96 39	98.81 95	94.65 25	98.36 35	99.01 13
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PHI-MVS		93.59 30	93.63 25	93.48 55	98.05 47	81.76 106	98.64 13	97.13 23	82.60 154	94.09 31	98.49 9	80.35 51	99.85 10	94.74 24	98.62 23	98.83 19

ACMMP_Plus		93.46 31	93.23 31	94.17 28	97.16 72	84.28 59	96.82 110	96.65 64	86.24 72	94.27 27	97.99 36	77.94 81	99.83 12	93.39 35	98.57 24	98.39 37

MVS_111021_HR		93.41 32	93.39 29	93.47 58	97.34 69	82.83 85	97.56 56	98.27 11	89.16 36	89.71 73	97.14 75	79.77 60	99.56 41	93.65 32	97.94 46	98.02 60

Regformer-3		93.19 33	93.19 32	93.19 64	98.10 45	83.01 82	97.08 94	96.98 38	88.98 37	91.35 59	97.89 43	80.80 48	99.23 61	92.30 49	95.20 86	97.32 105

PVSNet_Blended		93.13 34	92.98 36	93.57 48	97.47 61	83.86 63	99.32 1	96.73 55	91.02 20	89.53 78	96.21 96	76.42 100	99.57 39	94.29 28	95.81 83	97.29 110

CDPH-MVS		93.12 35	92.91 37	93.74 39	98.65 20	83.88 62	97.67 50	96.26 104	83.00 147	93.22 38	98.24 15	81.31 45	99.21 65	89.12 78	98.74 19	98.14 52

Regformer-4		93.06 36	93.12 33	92.89 74	98.10 45	82.20 95	97.08 94	96.92 45	88.87 39	91.23 61	97.89 43	80.57 50	99.19 70	92.21 51	95.20 86	97.29 110

#test#		92.99 37	92.99 35	92.98 71	98.71 13	81.12 119	97.77 42	96.70 59	85.75 81	91.75 50	97.97 40	78.47 74	99.71 23	91.36 55	98.41 30	98.12 54

alignmvs		92.97 38	92.26 48	95.12 13	95.54 102	87.77 15	98.67 11	96.38 96	88.04 50	93.01 40	97.45 62	79.20 66	98.60 97	93.25 40	88.76 137	98.99 16

HFP-MVS		92.89 39	92.86 38	92.98 71	98.71 13	81.12 119	97.58 54	96.70 59	85.20 94	91.75 50	97.97 40	78.47 74	99.71 23	90.95 59	98.41 30	98.12 54

PAPM		92.87 40	92.40 45	94.30 24	92.25 180	87.85 14	96.40 141	96.38 96	91.07 18	88.72 87	96.90 82	82.11 38	97.37 146	90.05 69	97.70 50	97.67 85

MPTG		92.74 41	92.71 39	92.86 75	97.90 49	80.85 125	96.47 130	96.33 100	87.92 52	90.20 70	98.18 17	76.71 98	99.76 15	92.57 47	98.09 40	97.96 68

PAPR		92.74 41	92.17 50	94.45 21	98.89 9	84.87 49	97.20 76	96.20 108	87.73 57	88.40 90	98.12 26	78.71 72	99.76 15	87.99 90	96.28 74	98.74 23

jason		92.73 43	92.23 49	94.21 27	90.50 211	87.30 21	98.65 12	95.09 161	90.61 23	92.76 42	97.13 76	75.28 127	97.30 149	93.32 38	96.75 71	98.02 60
jason: jason.
region2R		92.72 44	92.70 41	92.79 78	98.68 15	80.53 134	97.53 58	96.51 80	85.22 92	91.94 48	97.98 38	77.26 88	99.67 31	90.83 62	98.37 34	98.18 48

XVS		92.69 45	92.71 39	92.63 87	98.52 29	80.29 138	97.37 69	96.44 87	87.04 67	91.38 55	97.83 46	77.24 90	99.59 37	90.46 65	98.07 42	98.02 60

ACMMPR		92.69 45	92.67 42	92.75 80	98.66 18	80.57 131	97.58 54	96.69 61	85.20 94	91.57 53	97.92 42	77.01 92	99.67 31	90.95 59	98.41 30	98.00 65

WTY-MVS		92.65 47	91.68 55	95.56 9	96.00 88	88.90 8	98.23 22	97.65 15	88.57 40	89.82 72	97.22 73	79.29 62	99.06 81	89.57 74	88.73 138	98.73 25

MP-MVS		92.61 48	92.67 42	92.42 92	98.13 44	79.73 153	97.33 71	96.20 108	85.63 83	90.53 66	97.66 50	78.14 79	99.70 26	92.12 52	98.30 37	97.85 75

MP-MVS-pluss		92.58 49	92.35 46	93.29 60	97.30 70	82.53 89	96.44 135	96.04 119	84.68 108	89.12 83	98.37 11	77.48 86	99.74 20	93.31 39	98.38 33	97.59 92
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		92.54 50	92.60 44	92.34 95	98.50 31	79.90 148	98.40 17	96.40 92	84.75 106	90.48 68	98.09 28	77.40 87	99.21 65	91.15 58	98.23 39	97.92 71

MTAPA		92.45 51	92.31 47	92.86 75	97.90 49	80.85 125	92.88 245	96.33 100	87.92 52	90.20 70	98.18 17	76.71 98	99.76 15	92.57 47	98.09 40	97.96 68

canonicalmvs		92.27 52	91.22 60	95.41 11	95.80 97	88.31 9	97.09 92	94.64 187	88.49 43	92.99 41	97.31 68	72.68 144	98.57 99	93.38 37	88.58 140	99.36 4

VNet		92.11 53	91.22 60	94.79 17	96.91 75	86.98 22	97.91 33	97.96 14	86.38 71	93.65 34	95.74 101	70.16 167	98.95 89	93.39 35	88.87 136	98.43 35

CSCG		92.02 54	91.65 56	93.12 66	98.53 28	80.59 130	97.47 61	97.18 22	77.06 241	84.64 122	97.98 38	83.98 27	99.52 43	90.72 63	97.33 60	99.23 7

PGM-MVS		91.93 55	91.80 53	92.32 97	98.27 41	79.74 152	95.28 184	97.27 19	83.83 132	90.89 65	97.78 48	76.12 106	99.56 41	88.82 80	97.93 48	97.66 86

mPP-MVS		91.88 56	91.82 52	92.07 104	98.38 36	78.63 193	97.29 72	96.09 115	85.12 96	88.45 89	97.66 50	75.53 112	99.68 29	89.83 71	98.02 45	97.88 72

EI-MVSNet-Vis-set		91.84 57	91.77 54	92.04 106	97.60 57	81.17 118	96.61 124	96.87 47	88.20 48	89.19 82	97.55 60	78.69 73	99.14 76	90.29 67	90.94 126	95.80 149

DP-MVS Recon		91.72 58	90.85 64	94.34 23	99.50 1	85.00 45	98.51 16	95.96 122	80.57 186	88.08 95	97.63 55	76.84 94	99.89 6	85.67 104	94.88 90	98.13 53

CHOSEN 280x420		91.71 59	91.85 51	91.29 126	94.94 118	82.69 87	87.89 295	96.17 111	85.94 77	87.27 101	94.31 137	90.27 4	95.65 229	94.04 31	95.86 81	95.53 156

HY-MVS	84.06 6	91.63 60	90.37 68	95.39 12	96.12 84	88.25 10	90.22 277	97.58 17	88.33 46	90.50 67	91.96 168	79.26 64	99.06 81	90.29 67	89.07 134	98.88 18

HPM-MVS		91.62 61	91.53 58	91.89 110	97.88 52	79.22 170	96.99 98	95.73 133	82.07 160	89.50 80	97.19 74	75.59 111	98.93 92	90.91 61	97.94 46	97.54 93

MVS_111021_LR		91.60 62	91.64 57	91.47 123	95.74 98	78.79 190	96.15 154	96.77 51	88.49 43	88.64 88	97.07 79	72.33 147	99.19 70	93.13 41	96.48 73	96.43 136

DeepC-MVS	86.58 3	91.53 63	91.06 63	92.94 73	94.52 132	81.89 102	95.95 162	95.98 121	90.76 21	83.76 132	96.76 89	73.24 141	99.71 23	91.67 54	96.96 66	97.22 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PAPM_NR		91.46 64	90.82 65	93.37 59	98.50 31	81.81 105	95.03 197	96.13 112	84.65 109	86.10 110	97.65 54	79.24 65	99.75 18	83.20 130	96.88 69	98.56 32

MVSFormer		91.36 65	90.57 67	93.73 41	93.00 163	88.08 12	94.80 202	94.48 192	80.74 182	94.90 20	97.13 76	78.84 69	95.10 254	83.77 119	97.46 53	98.02 60

EI-MVSNet-UG-set		91.35 66	91.22 60	91.73 118	97.39 65	80.68 128	96.47 130	96.83 49	87.92 52	88.30 93	97.36 67	77.84 83	99.13 77	89.43 77	89.45 132	95.37 159

PVSNet_Blended_VisFu		91.24 67	90.77 66	92.66 84	95.09 114	82.40 91	97.77 42	95.87 128	88.26 47	86.39 106	93.94 144	76.77 96	99.27 58	88.80 81	94.00 97	96.31 142

APD-MVS_3200maxsize		91.23 68	91.35 59	90.89 139	97.89 51	76.35 239	96.30 149	95.52 143	79.82 205	91.03 64	97.88 45	74.70 133	98.54 100	92.11 53	96.89 68	97.77 80

CHOSEN 1792x2688		91.07 69	90.21 70	93.64 44	95.18 112	83.53 71	96.26 151	96.13 112	88.92 38	84.90 116	93.10 160	72.86 143	99.62 35	88.86 79	95.67 84	97.79 79

CANet_DTU		90.98 70	90.04 72	93.83 36	94.76 122	86.23 26	96.32 145	93.12 257	93.11 10	93.71 33	96.82 87	63.08 222	99.48 48	84.29 114	95.12 89	95.77 150

sss		90.87 71	89.96 74	93.60 47	94.15 140	83.84 65	97.14 84	98.13 12	85.93 78	89.68 74	96.09 97	71.67 151	99.30 57	87.69 92	89.16 133	97.66 86

Effi-MVS+		90.70 72	89.90 77	93.09 68	93.61 152	83.48 72	95.20 187	92.79 261	83.22 142	91.82 49	95.70 103	71.82 150	97.48 143	91.25 57	93.67 101	98.32 40

1121		90.66 73	89.82 79	93.16 65	97.39 65	81.71 110	93.33 232	96.66 63	74.45 273	91.38 55	97.55 60	79.27 63	99.52 43	79.95 149	98.43 29	98.26 46

MAR-MVS		90.63 74	90.22 69	91.86 115	98.47 33	78.20 209	97.18 78	96.61 71	83.87 131	88.18 94	98.18 17	68.71 172	99.75 18	83.66 124	97.15 62	97.63 89
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		90.60 75	88.64 93	96.50 1	94.25 138	90.53 4	93.33 232	97.21 21	77.59 232	78.88 188	97.31 68	71.52 154	99.69 27	89.60 73	98.03 44	99.27 6

xiu_mvs_v1_base_debu		90.54 76	89.54 83	93.55 49	92.31 173	87.58 18	96.99 98	94.87 170	87.23 62	93.27 35	97.56 57	57.43 262	98.32 107	92.72 44	93.46 104	94.74 172

xiu_mvs_v1_base		90.54 76	89.54 83	93.55 49	92.31 173	87.58 18	96.99 98	94.87 170	87.23 62	93.27 35	97.56 57	57.43 262	98.32 107	92.72 44	93.46 104	94.74 172

xiu_mvs_v1_base_debi		90.54 76	89.54 83	93.55 49	92.31 173	87.58 18	96.99 98	94.87 170	87.23 62	93.27 35	97.56 57	57.43 262	98.32 107	92.72 44	93.46 104	94.74 172

DWT-MVSNet_test		90.52 79	89.80 80	92.70 83	95.73 99	82.20 95	93.69 223	96.55 77	88.34 45	87.04 104	95.34 110	86.53 10	97.55 138	76.32 185	88.66 139	98.34 38

ACMMP		90.39 80	89.97 73	91.64 120	97.58 59	78.21 208	96.78 112	96.72 57	84.73 107	84.72 120	97.23 72	71.22 156	99.63 34	88.37 87	92.41 112	97.08 116
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
HPM-MVS_fast		90.38 81	90.17 71	91.03 134	97.61 56	77.35 228	97.15 83	95.48 145	79.51 210	88.79 86	96.90 82	71.64 153	98.81 95	87.01 99	97.44 55	96.94 119

MVS_Test		90.29 82	89.18 88	93.62 46	95.23 110	84.93 46	94.41 208	94.66 184	84.31 119	90.37 69	91.02 181	75.13 128	97.82 126	83.11 132	94.42 92	98.12 54

API-MVS		90.18 83	88.97 89	93.80 37	98.66 18	82.95 84	97.50 60	95.63 138	75.16 260	86.31 107	97.69 49	72.49 145	99.90 4	81.26 141	96.07 77	98.56 32

PatchFormer-LS_test		90.14 84	89.30 87	92.65 86	95.43 104	82.46 90	93.46 228	96.35 98	88.56 41	84.82 117	95.22 116	84.63 20	97.55 138	78.40 161	86.81 151	97.94 70

PVSNet_BlendedMVS		90.05 85	89.96 74	90.33 149	97.47 61	83.86 63	98.02 31	96.73 55	87.98 51	89.53 78	89.61 201	76.42 100	99.57 39	94.29 28	79.59 210	87.57 271

TESTMET0.1,1		89.83 86	89.34 86	91.31 124	92.54 171	80.19 143	97.11 88	96.57 74	86.15 73	86.85 105	91.83 172	79.32 61	96.95 166	81.30 140	92.35 113	96.77 127

abl_6		89.80 87	89.71 82	90.07 159	96.53 78	75.52 245	94.48 205	95.04 164	81.12 173	89.22 81	97.00 80	68.83 171	98.96 87	89.86 70	95.27 85	95.73 151

EPP-MVSNet		89.76 88	89.72 81	89.87 169	93.78 147	76.02 242	97.22 73	96.51 80	79.35 212	85.11 114	95.01 129	84.82 16	97.10 161	87.46 95	88.21 143	96.50 134

CPTT-MVS		89.72 89	89.87 78	89.29 177	98.33 38	73.30 259	97.70 48	95.35 154	75.68 252	87.40 98	97.44 65	70.43 164	98.25 110	89.56 75	96.90 67	96.33 141

3Dnovator+	82.88 8	89.63 90	87.85 102	94.99 15	94.49 135	86.76 23	97.84 36	95.74 132	86.10 74	75.47 230	96.02 98	65.00 211	99.51 46	82.91 134	97.07 63	98.72 26

CDS-MVSNet		89.50 91	88.96 90	91.14 132	91.94 193	80.93 123	97.09 92	95.81 130	84.26 123	84.72 120	94.20 139	80.31 52	95.64 230	83.37 129	88.96 135	96.85 124
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PMMVS		89.46 92	89.92 76	88.06 200	94.64 123	69.57 294	96.22 152	94.95 167	87.27 61	91.37 58	96.54 93	65.88 199	97.39 145	88.54 82	93.89 98	97.23 113

HyFIR lowres test		89.36 93	88.60 94	91.63 121	94.91 120	80.76 127	95.60 178	95.53 141	82.56 155	84.03 126	91.24 178	78.03 80	96.81 174	87.07 98	88.41 141	97.32 105

3Dnovator	82.32 10	89.33 94	87.64 107	94.42 22	93.73 151	85.70 32	97.73 47	96.75 54	86.73 70	76.21 221	95.93 99	62.17 226	99.68 29	81.67 139	97.81 49	97.88 72

LFMVS		89.27 95	87.64 107	94.16 30	97.16 72	85.52 36	97.18 78	94.66 184	79.17 217	89.63 76	96.57 92	55.35 279	98.22 111	89.52 76	89.54 131	98.74 23

MVSTER		89.25 96	88.92 91	90.24 151	95.98 89	84.66 50	96.79 111	95.36 152	87.19 65	80.33 172	90.61 188	90.02 5	95.97 204	85.38 107	78.64 219	90.09 218

CostFormer		89.08 97	88.39 97	91.15 131	93.13 161	79.15 173	88.61 290	96.11 114	83.14 143	89.58 77	86.93 233	83.83 29	96.87 171	88.22 88	85.92 161	97.42 101

PVSNet	82.34 9	89.02 98	87.79 104	92.71 82	95.49 103	81.50 113	97.70 48	97.29 18	87.76 56	85.47 112	95.12 125	56.90 267	98.90 93	80.33 144	94.02 95	97.71 83

test-mter		88.95 99	88.60 94	89.98 164	92.26 178	77.23 230	97.11 88	95.96 122	85.32 90	86.30 108	91.38 175	76.37 102	96.78 176	80.82 142	91.92 119	95.94 146

1314		88.94 100	87.20 118	94.17 28	93.21 158	85.73 31	93.33 232	96.64 67	82.89 148	75.98 223	96.36 94	66.83 190	99.39 53	83.52 128	96.02 79	97.39 103

UA-Net		88.92 101	88.48 96	90.24 151	94.06 143	77.18 232	93.04 242	94.66 184	87.39 60	91.09 63	93.89 145	74.92 131	98.18 114	75.83 189	91.43 123	95.35 160

thres200		88.92 101	87.65 106	92.73 81	96.30 80	85.62 34	97.85 35	98.86 1	84.38 117	84.82 117	93.99 143	75.12 129	98.01 115	70.86 226	86.67 152	94.56 175

Vis-MVSNet (Re-imp)		88.88 103	88.87 92	88.91 183	93.89 146	74.43 253	96.93 106	94.19 204	84.39 116	83.22 138	95.67 105	78.24 77	94.70 263	78.88 158	94.40 93	97.61 91

AdaColmap		88.81 104	87.61 110	92.39 94	99.33 4	79.95 146	96.70 119	95.58 139	77.51 233	83.05 140	96.69 91	61.90 233	99.72 22	84.29 114	93.47 103	97.50 97

OMC-MVS		88.80 105	88.16 98	90.72 141	95.30 109	77.92 217	94.81 201	94.51 191	86.80 69	84.97 115	96.85 85	67.53 176	98.60 97	85.08 108	87.62 146	95.63 154

114514_t		88.79 106	87.57 111	92.45 91	98.21 42	81.74 107	96.99 98	95.45 149	75.16 260	82.48 143	95.69 104	68.59 173	98.50 102	80.33 144	95.18 88	97.10 115

mvs_anonymous		88.68 107	87.62 109	91.86 115	94.80 121	81.69 111	93.53 227	94.92 168	82.03 161	78.87 189	90.43 191	75.77 110	95.34 245	85.04 109	93.16 107	98.55 34

Vis-MVSNet		88.67 108	87.82 103	91.24 129	92.68 166	78.82 187	96.95 104	93.85 225	87.55 58	87.07 103	95.13 124	63.43 220	97.21 154	77.58 171	96.15 75	97.70 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		88.67 108	88.16 98	90.20 153	93.61 152	76.86 234	96.77 114	93.07 258	84.02 127	83.62 133	95.60 107	74.69 134	96.24 192	78.43 160	93.66 102	97.49 98

IB-MVS	85.34 4	88.67 108	87.14 122	93.26 61	93.12 162	84.32 57	98.76 10	97.27 19	87.19 65	79.36 185	90.45 190	83.92 28	98.53 101	84.41 113	69.79 266	96.93 120
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
1112_ss		88.60 111	87.47 114	92.00 107	93.21 158	80.97 122	96.47 130	92.46 264	83.64 137	80.86 165	97.30 70	80.24 54	97.62 134	77.60 170	85.49 166	97.40 102

tfpn200view9		88.48 112	87.15 120	92.47 90	96.21 81	85.30 39	97.44 62	98.85 2	83.37 140	83.99 127	93.82 146	75.36 124	97.93 117	69.04 236	86.24 157	94.17 176

test-LLR		88.48 112	87.98 100	89.98 164	92.26 178	77.23 230	97.11 88	95.96 122	83.76 134	86.30 108	91.38 175	72.30 148	96.78 176	80.82 142	91.92 119	95.94 146

TAMVS		88.48 112	87.79 104	90.56 144	91.09 203	79.18 171	96.45 133	95.88 127	83.64 137	83.12 139	93.33 156	75.94 108	95.74 223	82.40 135	88.27 142	96.75 129

thres400		88.42 115	87.15 120	92.23 99	96.21 81	85.30 39	97.44 62	98.85 2	83.37 140	83.99 127	93.82 146	75.36 124	97.93 117	69.04 236	86.24 157	93.45 190

tpmrst		88.36 116	87.38 116	91.31 124	94.36 136	79.92 147	87.32 299	95.26 159	85.32 90	88.34 91	86.13 253	80.60 49	96.70 178	83.78 118	85.34 173	97.30 108

thres100view900		88.30 117	86.95 125	92.33 96	96.10 85	84.90 47	97.14 84	98.85 2	82.69 152	83.41 134	93.66 149	75.43 120	97.93 117	69.04 236	86.24 157	94.17 176

VDD-MVS		88.28 118	87.02 124	92.06 105	95.09 114	80.18 144	97.55 57	94.45 195	83.09 144	89.10 84	95.92 100	47.97 302	98.49 103	93.08 42	86.91 150	97.52 96

conf200view11		88.27 119	86.95 125	92.24 98	96.10 85	84.90 47	97.14 84	98.85 2	82.69 152	83.41 134	93.66 149	75.43 120	97.93 117	69.04 236	86.24 157	93.89 183

BH-w/o		88.24 120	87.47 114	90.54 145	95.03 117	78.54 196	97.41 68	93.82 226	84.08 125	78.23 193	94.51 136	69.34 170	97.21 154	80.21 147	94.58 91	95.87 148

thres600view7		88.06 121	86.70 129	92.15 102	96.10 85	85.17 41	97.14 84	98.85 2	82.70 151	83.41 134	93.66 149	75.43 120	97.82 126	67.13 251	85.88 162	93.45 190

Test_1112_low_res		88.03 122	86.73 128	91.94 109	93.15 160	80.88 124	96.44 135	92.41 265	83.59 139	80.74 167	91.16 179	80.18 55	97.59 135	77.48 172	85.40 167	97.36 104

PLC	83.97 7	88.00 123	87.38 116	89.83 171	98.02 48	76.46 237	97.16 82	94.43 196	79.26 216	81.98 157	96.28 95	69.36 169	99.27 58	77.71 169	92.25 116	93.77 185
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CLD-MVS		87.97 124	87.48 113	89.44 175	92.16 183	80.54 133	98.14 27	94.92 168	91.41 16	79.43 184	95.40 109	62.34 225	97.27 152	90.60 64	82.90 197	90.50 208
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
diffmvs		87.96 125	86.47 131	92.42 92	94.26 137	82.70 86	92.79 249	94.03 217	77.94 227	88.99 85	89.98 198	70.72 163	97.56 136	77.75 163	91.80 121	96.98 117

Fast-Effi-MVS+		87.93 126	86.94 127	90.92 138	94.04 144	79.16 172	98.26 21	93.72 234	81.29 171	83.94 130	92.90 161	69.83 168	96.68 179	76.70 181	91.74 122	96.93 120

HQP-MVS		87.91 127	87.55 112	88.98 182	92.08 184	78.48 198	97.63 51	94.80 175	90.52 24	82.30 146	94.56 134	65.40 207	97.32 147	87.67 93	83.01 189	91.13 201

UGNet		87.73 128	86.55 130	91.27 127	95.16 113	79.11 174	96.35 143	96.23 106	88.14 49	87.83 97	90.48 189	50.65 291	99.09 80	80.13 148	94.03 94	95.60 155
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
EPNet_dtu		87.65 129	87.89 101	86.93 227	94.57 125	71.37 279	96.72 115	96.50 82	88.56 41	87.12 102	95.02 128	75.91 109	94.01 276	66.62 254	90.00 130	95.42 158
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP_MVS		87.50 130	87.09 123	88.74 187	91.86 194	77.96 214	97.18 78	94.69 180	89.89 30	81.33 161	94.15 140	64.77 212	97.30 149	87.08 96	82.82 198	90.96 203

EPMVS		87.47 131	85.90 140	92.18 101	95.41 106	82.26 94	87.00 303	96.28 103	85.88 79	84.23 125	85.57 260	75.07 130	96.26 190	71.14 224	92.50 110	98.03 59

view600		87.45 132	85.98 135	91.88 111	95.90 91	84.52 52	96.68 120	98.85 2	81.85 163	82.30 146	93.39 152	75.44 116	97.66 129	64.02 269	85.36 168	93.45 190

view800		87.45 132	85.98 135	91.88 111	95.90 91	84.52 52	96.68 120	98.85 2	81.85 163	82.30 146	93.39 152	75.44 116	97.66 129	64.02 269	85.36 168	93.45 190

conf0.05thres1000		87.45 132	85.98 135	91.88 111	95.90 91	84.52 52	96.68 120	98.85 2	81.85 163	82.30 146	93.39 152	75.44 116	97.66 129	64.02 269	85.36 168	93.45 190

tfpn		87.45 132	85.98 135	91.88 111	95.90 91	84.52 52	96.68 120	98.85 2	81.85 163	82.30 146	93.39 152	75.44 116	97.66 129	64.02 269	85.36 168	93.45 190

tpm2		87.35 136	86.26 132	90.62 143	92.93 165	78.67 191	88.06 294	95.99 120	79.33 213	87.40 98	86.43 249	80.28 53	96.40 184	80.23 146	85.73 165	96.79 125

tfpn_ndepth		87.25 137	86.00 134	91.01 136	95.86 95	81.46 114	96.53 127	97.09 31	77.35 236	81.36 160	95.07 127	84.74 18	95.86 213	60.88 284	85.14 174	95.72 152

ab-mvs		87.08 138	84.94 153	93.48 55	93.34 157	83.67 69	88.82 287	95.70 134	81.18 172	84.55 123	90.14 196	62.72 223	98.94 91	85.49 106	82.54 201	97.85 75

CNLPA		86.96 139	85.37 144	91.72 119	97.59 58	79.34 165	97.21 74	91.05 281	74.22 274	78.90 187	96.75 90	67.21 180	98.95 89	74.68 199	90.77 127	96.88 123

BH-untuned		86.95 140	85.94 139	89.99 163	94.52 132	77.46 225	96.78 112	93.37 250	81.80 167	76.62 214	93.81 148	66.64 193	97.02 164	76.06 187	93.88 99	95.48 157

QAPM		86.88 141	84.51 157	93.98 31	94.04 144	85.89 29	97.19 77	96.05 118	73.62 278	75.12 233	95.62 106	62.02 229	99.74 20	70.88 225	96.06 78	96.30 143

BH-RMVSNet		86.84 142	85.28 145	91.49 122	95.35 108	80.26 141	96.95 104	92.21 266	82.86 149	81.77 159	95.46 108	59.34 245	97.64 133	69.79 233	93.81 100	96.57 133

mvs-test1		86.83 143	87.17 119	85.81 238	91.96 190	65.24 306	97.90 34	93.34 251	85.57 84	84.51 124	95.14 123	61.99 230	97.19 156	83.55 125	90.55 128	95.00 167

PatchmatchNet		86.83 143	85.12 148	91.95 108	94.12 141	82.27 93	86.55 307	95.64 137	84.59 111	82.98 141	84.99 270	77.26 88	95.96 208	68.61 244	91.34 124	97.64 88
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
nrg030		86.79 145	85.43 142	90.87 140	88.76 235	85.34 38	97.06 96	94.33 198	84.31 119	80.45 170	91.98 167	72.36 146	96.36 186	88.48 85	71.13 249	90.93 205

PCF-MVS	84.09 5	86.77 146	85.00 151	92.08 103	92.06 187	83.07 79	92.14 261	94.47 194	79.63 209	76.90 211	94.78 131	71.15 157	99.20 69	72.87 207	91.05 125	93.98 181
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FIs		86.73 147	86.10 133	88.61 189	90.05 219	80.21 142	96.14 155	96.95 41	85.56 87	78.37 192	92.30 164	76.73 97	95.28 248	79.51 152	79.27 214	90.35 210

cascas		86.50 148	84.48 159	92.55 89	92.64 170	85.95 28	97.04 97	95.07 163	75.32 257	80.50 168	91.02 181	54.33 286	97.98 116	86.79 100	87.62 146	93.71 186

tpmp4_e23		86.46 149	84.95 152	90.98 137	93.74 150	78.60 195	88.13 293	95.90 126	79.65 208	85.42 113	85.67 255	80.08 57	97.06 162	71.71 216	84.26 180	97.28 112

VDDNet		86.44 150	84.51 157	92.22 100	91.56 196	81.83 104	97.10 91	94.64 187	69.50 299	87.84 96	95.19 119	48.01 301	97.92 123	89.82 72	86.92 149	96.89 122

tfpn1000		86.43 151	85.10 149	90.41 147	95.56 101	80.51 135	95.90 167	97.09 31	75.91 249	80.02 176	94.82 130	84.78 17	95.47 240	57.36 293	84.46 177	95.26 162

TR-MVS		86.30 152	84.93 154	90.42 146	94.63 124	77.58 223	96.57 126	93.82 226	80.30 193	82.42 145	95.16 121	58.74 249	97.55 138	74.88 197	87.82 145	96.13 144

X-MVStestdata		86.26 153	84.14 169	92.63 87	98.52 29	80.29 138	97.37 69	96.44 87	87.04 67	91.38 55	20.73 351	77.24 90	99.59 37	90.46 65	98.07 42	98.02 60

OpenMVS	79.58 14	86.09 154	83.62 175	93.50 53	90.95 205	86.71 24	97.44 62	95.83 129	75.35 256	72.64 249	95.72 102	57.42 265	99.64 33	71.41 219	95.85 82	94.13 179

FC-MVSNet-test		85.96 155	85.39 143	87.66 213	89.38 231	78.02 212	95.65 177	96.87 47	85.12 96	77.34 204	91.94 170	76.28 104	94.74 262	77.09 177	78.82 217	90.21 213

DI_MVS_plusplus_test		85.92 156	83.61 176	92.86 75	86.43 265	83.20 76	95.57 179	95.46 146	85.10 99	65.99 280	86.84 237	56.70 269	97.89 124	88.10 89	92.33 114	97.48 99

OPM-MVS		85.84 157	85.10 149	88.06 200	88.34 241	77.83 220	95.72 174	94.20 202	87.89 55	80.45 170	94.05 142	58.57 250	97.26 153	83.88 117	82.76 200	89.09 233

test_normal		85.83 158	83.51 178	92.78 79	86.33 270	83.01 82	95.56 181	95.46 146	85.11 98	65.73 282	86.63 242	56.62 271	97.86 125	87.87 91	92.29 115	97.47 100

thresconf0.02		85.80 159	84.35 162	90.17 154	94.53 127	79.70 154	95.17 188	97.11 25	75.97 244	79.44 179	95.31 111	81.90 40	95.73 224	56.78 298	82.91 192	95.09 163

tfpn_n400		85.80 159	84.35 162	90.17 154	94.53 127	79.70 154	95.17 188	97.11 25	75.97 244	79.44 179	95.31 111	81.90 40	95.73 224	56.78 298	82.91 192	95.09 163

tfpnconf		85.80 159	84.35 162	90.17 154	94.53 127	79.70 154	95.17 188	97.11 25	75.97 244	79.44 179	95.31 111	81.90 40	95.73 224	56.78 298	82.91 192	95.09 163

tfpnview11		85.80 159	84.35 162	90.17 154	94.53 127	79.70 154	95.17 188	97.11 25	75.97 244	79.44 179	95.31 111	81.90 40	95.73 224	56.78 298	82.91 192	95.09 163

EI-MVSNet		85.80 159	85.20 146	87.59 215	91.55 197	77.41 226	95.13 193	95.36 152	80.43 190	80.33 172	94.71 132	73.72 139	95.97 204	76.96 180	78.64 219	89.39 227

GA-MVS		85.79 164	84.04 170	91.02 135	89.47 229	80.27 140	96.90 107	94.84 173	85.57 84	80.88 164	89.08 204	56.56 272	96.47 183	77.72 168	85.35 172	96.34 139

XVG-OURS-SEG-HR		85.74 165	85.16 147	87.49 219	90.22 215	71.45 278	91.29 271	94.09 215	81.37 170	83.90 131	95.22 116	60.30 237	97.53 142	85.58 105	84.42 179	93.50 188

conf0.002		85.70 166	84.35 162	89.77 173	94.53 127	79.70 154	95.17 188	97.11 25	75.97 244	79.44 179	95.31 111	81.90 40	95.73 224	56.78 298	82.91 192	93.89 183

tpm		85.55 167	84.47 160	88.80 186	90.19 216	75.39 247	88.79 288	94.69 180	84.83 105	83.96 129	85.21 264	78.22 78	94.68 264	76.32 185	78.02 224	96.34 139

UniMVSNet_NR-MVSNet		85.49 168	84.59 156	88.21 199	89.44 230	79.36 163	96.71 117	96.41 90	85.22 92	78.11 194	90.98 183	76.97 93	95.14 252	79.14 155	68.30 278	90.12 216

gg-mvs-nofinetune		85.48 169	82.90 185	93.24 62	94.51 134	85.82 30	79.22 324	96.97 39	61.19 321	87.33 100	53.01 338	90.58 3	96.07 197	86.07 102	97.23 61	97.81 78

VPA-MVSNet		85.32 170	83.83 171	89.77 173	90.25 214	82.63 88	96.36 142	97.07 33	83.03 146	81.21 163	89.02 206	61.58 234	96.31 188	85.02 110	70.95 251	90.36 209

UniMVSNet (Re)		85.31 171	84.23 168	88.55 190	89.75 222	80.55 132	96.72 115	96.89 46	85.42 88	78.40 191	88.93 207	75.38 123	95.52 237	78.58 159	68.02 281	89.57 225

XVG-OURS		85.18 172	84.38 161	87.59 215	90.42 213	71.73 275	91.06 274	94.07 216	82.00 162	83.29 137	95.08 126	56.42 273	97.55 138	83.70 123	83.42 185	93.49 189

TAPA-MVS	81.61 12	85.02 173	83.67 173	89.06 179	96.79 76	73.27 261	95.92 164	94.79 177	74.81 267	80.47 169	96.83 86	71.07 158	98.19 113	49.82 321	92.57 109	95.71 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		85.00 174	83.66 174	89.02 181	95.86 95	74.55 252	92.49 253	93.60 239	79.30 215	79.29 186	91.47 173	58.53 251	98.45 105	70.22 229	92.17 117	94.07 180

PS-MVSNAJss		84.91 175	84.30 167	86.74 228	85.89 284	74.40 254	94.95 198	94.16 208	83.93 129	76.45 216	90.11 197	71.04 159	95.77 218	83.16 131	79.02 216	90.06 220

Patchmatch-test1		84.89 176	82.76 188	91.27 127	92.30 176	81.86 103	92.88 245	95.56 140	84.85 104	82.52 142	85.19 265	58.04 256	94.21 272	65.93 260	87.58 148	97.74 81

CVMVSNet		84.83 177	85.57 141	82.63 287	91.55 197	60.38 319	95.13 193	95.03 165	80.60 185	82.10 156	94.71 132	66.40 196	90.19 317	74.30 201	90.32 129	97.31 107

FMVSNet3		84.71 178	82.71 189	90.70 142	94.55 126	87.71 16	95.92 164	94.67 183	81.73 168	75.82 226	88.08 219	66.99 188	94.47 267	71.23 221	75.38 232	89.91 222

VPNet		84.69 179	82.92 184	90.01 162	89.01 233	83.45 73	96.71 117	95.46 146	85.71 82	79.65 178	92.18 166	56.66 270	96.01 203	83.05 133	67.84 283	90.56 207

Effi-MVS+-dtu		84.61 180	84.90 155	83.72 277	91.96 190	63.14 313	94.95 198	93.34 251	85.57 84	79.79 177	87.12 231	61.99 230	95.61 233	83.55 125	85.83 163	92.41 197

DU-MVS		84.57 181	83.33 181	88.28 197	88.76 235	79.36 163	96.43 139	95.41 151	85.42 88	78.11 194	90.82 184	67.61 174	95.14 252	79.14 155	68.30 278	90.33 211

F-COLMAP		84.50 182	83.44 180	87.67 212	95.22 111	72.22 266	95.95 162	93.78 231	75.74 250	76.30 219	95.18 120	59.50 242	98.45 105	72.67 209	86.59 154	92.35 198

WR-MVS		84.32 183	82.96 183	88.41 192	89.38 231	80.32 137	96.59 125	96.25 105	83.97 128	76.63 213	90.36 192	67.53 176	94.86 260	75.82 190	70.09 261	90.06 220

dp		84.30 184	82.31 193	90.28 150	94.24 139	77.97 213	86.57 306	95.53 141	79.94 203	80.75 166	85.16 267	71.49 155	96.39 185	63.73 274	83.36 186	96.48 135

LPG-MVS_test		84.20 185	83.49 179	86.33 231	90.88 206	73.06 262	95.28 184	94.13 209	82.20 158	76.31 217	93.20 157	54.83 284	96.95 166	83.72 121	80.83 204	88.98 237

ACMP	81.66 11	84.00 186	83.22 182	86.33 231	91.53 199	72.95 264	95.91 166	93.79 230	83.70 136	73.79 237	92.22 165	54.31 287	96.89 170	83.98 116	79.74 209	89.16 232
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 187	82.80 187	87.31 222	91.46 200	77.39 227	95.66 176	93.43 244	80.44 188	75.51 229	87.26 227	73.72 139	95.16 251	76.99 178	70.72 253	89.39 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		83.84 188	82.00 194	89.35 176	87.13 251	81.38 115	95.72 174	94.26 200	80.15 198	75.92 225	90.63 187	61.96 232	96.52 181	78.98 157	73.28 243	90.14 214

LCM-MVSNet-Re		83.75 189	83.54 177	84.39 267	93.54 154	64.14 309	92.51 252	84.03 331	83.90 130	66.14 279	86.59 243	67.36 178	92.68 286	84.89 111	92.87 108	96.35 138

ACMM	80.70 13	83.72 190	82.85 186	86.31 234	91.19 202	72.12 269	95.88 168	94.29 199	80.44 188	77.02 209	91.96 168	55.24 280	97.14 160	79.30 154	80.38 206	89.67 224
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm cat1		83.63 191	81.38 208	90.39 148	93.53 155	78.19 210	85.56 313	95.09 161	70.78 295	78.51 190	83.28 286	74.80 132	97.03 163	66.77 253	84.05 181	95.95 145

CR-MVSNet		83.53 192	81.36 209	90.06 160	90.16 217	79.75 150	79.02 326	91.12 278	84.24 124	82.27 154	80.35 298	75.45 114	93.67 282	63.37 277	86.25 155	96.75 129

v2v482		83.46 193	81.86 198	88.25 198	86.19 276	79.65 159	96.34 144	94.02 218	81.56 169	77.32 205	88.23 216	65.62 202	96.03 199	77.77 162	69.72 268	89.09 233

v1neww		83.45 194	81.95 195	87.95 205	86.66 255	79.04 178	96.32 145	94.17 205	80.76 179	77.56 197	87.25 228	67.02 186	96.08 195	77.73 165	70.07 262	88.74 247

v7new		83.45 194	81.95 195	87.95 205	86.66 255	79.04 178	96.32 145	94.17 205	80.76 179	77.56 197	87.25 228	67.02 186	96.08 195	77.73 165	70.07 262	88.74 247

v6		83.45 194	81.94 197	87.95 205	86.62 259	79.03 181	96.32 145	94.17 205	80.76 179	77.57 196	87.23 230	67.03 185	96.09 194	77.73 165	70.06 264	88.75 245

v1		83.37 197	81.82 199	88.01 202	86.58 263	79.24 167	96.45 133	94.13 209	80.88 175	77.48 201	86.88 234	67.15 181	96.04 198	77.15 174	69.67 270	88.76 243

v1141		83.36 198	81.81 201	88.01 202	86.61 261	79.26 166	96.44 135	94.12 212	80.88 175	77.48 201	86.87 235	67.08 183	96.03 199	77.14 175	69.69 269	88.75 245

divwei89l23v2f112		83.36 198	81.81 201	88.01 202	86.60 262	79.23 169	96.44 135	94.12 212	80.88 175	77.49 199	86.87 235	67.08 183	96.03 199	77.14 175	69.67 270	88.76 243

NR-MVSNet		83.35 200	81.52 206	88.84 184	88.76 235	81.31 117	94.45 207	95.16 160	84.65 109	67.81 272	90.82 184	70.36 165	94.87 259	74.75 198	66.89 290	90.33 211

Fast-Effi-MVS+-dtu		83.33 201	82.60 190	85.50 242	89.55 227	69.38 295	96.09 159	91.38 275	82.30 157	75.96 224	91.41 174	56.71 268	95.58 235	75.13 196	84.90 176	91.54 199

TranMVSNet+NR-MVSNet		83.24 202	81.71 203	87.83 208	87.71 247	78.81 189	96.13 157	94.82 174	84.52 112	76.18 222	90.78 186	64.07 215	94.60 265	74.60 200	66.59 294	90.09 218

MS-PatchMatch		83.05 203	81.82 199	86.72 230	89.64 225	79.10 175	94.88 200	94.59 190	79.70 207	70.67 259	89.65 200	50.43 293	96.82 173	70.82 228	95.99 80	84.25 302

V42		83.04 204	81.53 205	87.57 217	86.27 274	79.09 176	95.87 169	94.11 214	80.35 192	77.22 207	86.79 240	65.32 209	96.02 202	77.74 164	70.14 257	87.61 270

tpmvs		83.04 204	80.77 214	89.84 170	95.43 104	77.96 214	85.59 312	95.32 155	75.31 258	76.27 220	83.70 282	73.89 137	97.41 144	59.53 286	81.93 202	94.14 178

test_djsdf		83.00 206	82.45 192	84.64 258	84.07 301	69.78 291	94.80 202	94.48 192	80.74 182	75.41 231	87.70 222	61.32 235	95.10 254	83.77 119	79.76 207	89.04 235

v7		82.99 207	81.41 207	87.73 211	86.41 266	78.86 186	96.10 158	93.98 219	79.88 204	77.49 199	87.11 232	65.44 205	95.97 204	75.69 192	70.59 255	88.36 255

v1144		82.90 208	81.27 210	87.78 210	86.29 272	79.07 177	96.14 155	93.93 221	80.05 200	77.38 203	86.80 239	65.50 203	95.93 210	75.21 195	70.13 258	88.33 257

test0.0.03 1		82.79 209	82.48 191	83.74 276	86.81 253	72.22 266	96.52 128	95.03 165	83.76 134	73.00 245	93.20 157	72.30 148	88.88 320	64.15 268	77.52 226	90.12 216

FMVSNet2		82.79 209	80.44 218	89.83 171	92.66 167	85.43 37	95.42 183	94.35 197	79.06 219	74.46 234	87.28 225	56.38 274	94.31 270	69.72 234	74.68 236	89.76 223

MVP-Stereo		82.65 211	81.67 204	85.59 241	86.10 280	78.29 204	93.33 232	92.82 260	77.75 230	69.17 270	87.98 220	59.28 246	95.76 219	71.77 215	96.88 69	82.73 320
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs4		82.54 212	80.79 213	87.79 209	86.11 279	80.49 136	93.55 226	93.18 254	77.29 237	73.35 241	89.40 203	65.26 210	95.05 257	75.32 194	73.61 239	87.83 265

v144192		82.43 213	80.73 215	87.54 218	85.81 285	78.22 206	95.98 160	93.78 231	79.09 218	77.11 208	86.49 245	64.66 214	95.91 211	74.20 202	69.42 272	88.49 250

GBi-Net		82.42 214	80.43 219	88.39 193	92.66 167	81.95 97	94.30 212	93.38 247	79.06 219	75.82 226	85.66 256	56.38 274	93.84 278	71.23 221	75.38 232	89.38 229

test1		82.42 214	80.43 219	88.39 193	92.66 167	81.95 97	94.30 212	93.38 247	79.06 219	75.82 226	85.66 256	56.38 274	93.84 278	71.23 221	75.38 232	89.38 229

v148		82.41 216	80.89 212	86.99 226	86.18 277	76.81 235	96.27 150	93.82 226	80.49 187	75.28 232	86.11 254	67.32 179	95.75 220	75.48 193	67.03 289	88.42 254

v1192		82.31 217	80.55 217	87.60 214	85.94 282	78.47 201	95.85 171	93.80 229	79.33 213	76.97 210	86.51 244	63.33 221	95.87 212	73.11 206	70.13 258	88.46 252

Test4		82.30 218	79.15 233	91.78 117	81.84 307	81.74 107	94.04 218	94.20 202	84.86 103	59.75 313	83.88 277	37.14 325	96.28 189	84.60 112	92.00 118	97.30 108

LS3D		82.22 219	79.94 227	89.06 179	97.43 64	74.06 257	93.20 240	92.05 268	61.90 317	73.33 242	95.21 118	59.35 244	99.21 65	54.54 308	92.48 111	93.90 182

jajsoiax		82.12 220	81.15 211	85.03 246	84.19 299	70.70 284	94.22 216	93.95 220	83.07 145	73.48 239	89.75 199	49.66 296	95.37 244	82.24 137	79.76 207	89.02 236

v1921920		82.02 221	80.23 221	87.41 220	85.62 286	77.92 217	95.79 173	93.69 235	78.86 222	76.67 212	86.44 247	62.50 224	95.83 215	72.69 208	69.77 267	88.47 251

v8		81.88 222	80.06 225	87.32 221	86.63 258	79.04 178	94.41 208	93.65 237	78.77 223	73.19 244	85.57 260	66.87 189	95.81 216	73.84 205	67.61 285	87.11 278

mvs_tets		81.74 223	80.71 216	84.84 250	84.22 298	70.29 287	93.91 219	93.78 231	82.77 150	73.37 240	89.46 202	47.36 305	95.31 247	81.99 138	79.55 213	88.92 241

v1240		81.70 224	79.83 228	87.30 223	85.50 287	77.70 222	95.48 182	93.44 242	78.46 226	76.53 215	86.44 247	60.85 236	95.84 214	71.59 218	70.17 256	88.35 256

PVSNet_0	77.72 15	81.70 224	78.95 234	89.94 167	90.77 208	76.72 236	95.96 161	96.95 41	85.01 100	70.24 264	88.53 213	52.32 288	98.20 112	86.68 101	44.08 338	94.89 168

DP-MVS		81.47 226	78.28 236	91.04 133	98.14 43	78.48 198	95.09 196	86.97 317	61.14 322	71.12 256	92.78 163	59.59 240	99.38 54	53.11 312	86.61 153	95.27 161

v10		81.43 227	79.53 230	87.11 224	86.38 267	78.87 185	94.31 211	93.43 244	77.88 229	73.24 243	85.26 263	65.44 205	95.75 220	72.14 212	67.71 284	86.72 282

pmmvs5		81.34 228	79.54 229	86.73 229	85.02 292	76.91 233	96.22 152	91.65 273	77.65 231	73.55 238	88.61 210	55.70 277	94.43 268	74.12 203	73.35 242	88.86 242

ADS-MVSNet		81.26 229	78.36 235	89.96 166	93.78 147	79.78 149	79.48 322	93.60 239	73.09 282	80.14 174	79.99 300	62.15 227	95.24 250	59.49 287	83.52 183	94.85 169

Baseline_NR-MVSNet		81.22 230	80.07 224	84.68 256	85.32 290	75.12 249	96.48 129	88.80 306	76.24 243	77.28 206	86.40 250	67.61 174	94.39 269	75.73 191	66.73 293	84.54 300

WR-MVS_H		81.02 231	80.09 222	83.79 274	88.08 244	71.26 282	94.46 206	96.54 78	80.08 199	72.81 248	86.82 238	70.36 165	92.65 287	64.18 267	67.50 286	87.46 275

CP-MVSNet		81.01 232	80.08 223	83.79 274	87.91 246	70.51 285	94.29 215	95.65 136	80.83 178	72.54 250	88.84 208	63.71 216	92.32 290	68.58 245	68.36 277	88.55 249

anonymousdsp		80.98 233	79.97 226	84.01 269	81.73 308	70.44 286	92.49 253	93.58 241	77.10 240	72.98 246	86.31 251	57.58 261	94.90 258	79.32 153	78.63 221	86.69 283

IterMVS		80.67 234	79.16 232	85.20 244	89.79 221	76.08 241	92.97 244	91.86 270	80.28 194	71.20 255	85.14 268	57.93 260	91.34 308	72.52 210	70.74 252	88.18 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		80.62 235	77.77 240	89.14 178	93.43 156	77.24 229	91.89 265	90.18 295	69.86 298	68.02 271	91.94 170	52.21 289	98.84 94	59.32 289	83.12 187	91.35 200

PS-CasMVS		80.27 236	79.18 231	83.52 280	87.56 249	69.88 290	94.08 217	95.29 157	80.27 195	72.08 251	88.51 214	59.22 247	92.23 292	67.49 249	68.15 280	88.45 253

pm-mvs1		80.05 237	78.02 238	86.15 236	85.42 288	75.81 243	95.11 195	92.69 263	77.13 238	70.36 261	87.43 224	58.44 252	95.27 249	71.36 220	64.25 298	87.36 276

PatchT		79.75 238	76.85 248	88.42 191	89.55 227	75.49 246	77.37 330	94.61 189	63.07 311	82.46 144	73.32 325	75.52 113	93.41 285	51.36 315	84.43 178	96.36 137

ADS-MVSNet2		79.57 239	77.53 241	85.71 239	93.78 147	72.13 268	79.48 322	86.11 322	73.09 282	80.14 174	79.99 300	62.15 227	90.14 318	59.49 287	83.52 183	94.85 169

FMVSNet1		79.50 240	76.54 251	88.39 193	88.47 240	81.95 97	94.30 212	93.38 247	73.14 281	72.04 252	85.66 256	43.86 309	93.84 278	65.48 262	72.53 246	89.38 229

PEN-MVS		79.47 241	78.26 237	83.08 283	86.36 269	68.58 297	93.85 220	94.77 178	79.76 206	71.37 253	88.55 211	59.79 238	92.46 288	64.50 266	65.40 295	88.19 259

XVG-ACMP-BASELINE		79.38 242	77.90 239	83.81 273	84.98 293	67.14 303	89.03 286	93.18 254	80.26 196	72.87 247	88.15 218	38.55 322	96.26 190	76.05 188	78.05 223	88.02 262

v7n		79.32 243	77.34 242	85.28 243	84.05 302	72.89 265	93.38 230	93.87 224	75.02 263	70.68 258	84.37 272	59.58 241	95.62 232	67.60 248	67.50 286	87.32 277

RPMNet		79.32 243	75.75 255	90.06 160	90.16 217	79.75 150	79.02 326	93.92 222	58.43 328	82.27 154	72.55 326	73.03 142	93.67 282	46.10 327	86.25 155	96.75 129

MIMVSNet		79.18 245	75.99 254	88.72 188	87.37 250	80.66 129	79.96 321	91.82 271	77.38 235	74.33 235	81.87 291	41.78 317	90.74 313	66.36 259	83.10 188	94.76 171

JIA-IIPM		79.00 246	77.20 243	84.40 266	89.74 224	64.06 310	75.30 332	95.44 150	62.15 316	81.90 158	59.08 336	78.92 68	95.59 234	66.51 257	85.78 164	93.54 187

v52		78.70 247	76.95 245	83.95 270	81.71 309	71.34 280	91.93 264	93.43 244	74.69 270	70.36 261	83.71 281	58.04 256	95.50 238	71.84 213	66.82 292	85.00 297

V4		78.70 247	76.95 245	83.95 270	81.66 310	71.34 280	91.94 263	93.44 242	74.69 270	70.35 263	83.73 280	58.07 255	95.50 238	71.84 213	66.86 291	85.02 296

v748		78.69 249	76.79 249	84.39 267	83.40 305	70.78 283	93.25 238	93.62 238	74.96 264	69.40 267	83.74 279	59.40 243	95.39 242	68.74 242	64.59 297	86.99 281

USDC		78.65 250	76.25 252	85.85 237	87.58 248	74.60 251	89.58 281	90.58 294	84.05 126	63.13 293	88.23 216	40.69 321	96.86 172	66.57 256	75.81 230	86.09 290

DTE-MVSNet		78.37 251	77.06 244	82.32 291	85.22 291	67.17 302	93.40 229	93.66 236	78.71 224	70.53 260	88.29 215	59.06 248	92.23 292	61.38 282	63.28 302	87.56 272

Patchmatch-test		78.25 252	74.72 267	88.83 185	91.20 201	74.10 256	73.91 336	88.70 309	59.89 326	66.82 275	85.12 269	78.38 76	94.54 266	48.84 323	79.58 211	97.86 74

tfpnnormal		78.14 253	75.42 259	86.31 234	88.33 242	79.24 167	94.41 208	96.22 107	73.51 279	69.81 265	85.52 262	55.43 278	95.75 220	47.65 325	67.86 282	83.95 306

ACMH	75.40 17	77.99 254	74.96 263	87.10 225	90.67 209	76.41 238	93.19 241	91.64 274	72.47 288	63.44 291	87.61 223	43.34 312	97.16 157	58.34 291	73.94 238	87.72 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	73.68 18	77.99 254	75.74 256	84.74 253	90.45 212	72.02 270	86.41 308	91.12 278	72.57 287	66.63 276	87.27 226	54.95 283	96.98 165	56.29 303	75.98 228	85.21 295
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
v18		77.96 256	75.61 257	84.98 247	86.66 255	79.01 182	93.02 243	90.94 283	75.69 251	63.19 292	77.62 307	67.11 182	92.07 295	70.05 230	56.24 314	83.87 307

v16		77.84 257	75.47 258	84.93 249	86.62 259	78.93 184	92.84 247	90.89 284	75.50 254	63.03 296	77.54 308	66.82 191	92.04 296	69.82 231	56.22 315	83.82 309

v17		77.79 258	75.41 260	84.94 248	86.53 264	78.94 183	92.83 248	90.88 285	75.51 253	62.97 297	77.50 309	66.69 192	92.03 297	69.80 232	56.01 316	83.83 308

RPSCF		77.73 259	76.63 250	81.06 296	88.66 239	55.76 328	87.77 296	87.88 312	64.82 310	74.14 236	92.79 162	49.22 298	96.81 174	67.47 250	76.88 227	90.62 206

v15		77.52 260	75.09 261	84.82 251	86.37 268	78.82 187	92.58 251	90.78 287	75.47 255	62.53 299	77.17 310	66.58 195	91.92 298	69.18 235	55.16 318	83.73 310

ACMH+	76.62 16	77.47 261	74.94 264	85.05 245	91.07 204	71.58 277	93.26 237	90.01 296	71.80 291	64.76 286	88.55 211	41.62 318	96.48 182	62.35 280	71.00 250	87.09 279

V14		77.43 262	74.99 262	84.75 252	86.32 271	78.67 191	92.44 255	90.77 288	75.28 259	62.42 300	77.13 311	66.40 196	91.88 299	69.01 240	55.14 319	83.70 311

Patchmtry		77.36 263	74.59 270	85.67 240	89.75 222	75.75 244	77.85 329	91.12 278	60.28 324	71.23 254	80.35 298	75.45 114	93.56 284	57.94 292	67.34 288	87.68 268

V9		77.32 264	74.87 265	84.69 255	86.26 275	78.52 197	92.33 258	90.72 289	75.11 262	62.21 302	77.08 313	66.19 198	91.87 300	68.84 241	55.06 321	83.69 312

v11		77.21 265	74.72 267	84.68 256	86.29 272	78.62 194	92.30 259	90.63 293	74.86 266	62.32 301	76.73 316	65.49 204	91.83 301	68.17 247	55.72 317	83.59 314

v12		77.20 266	74.73 266	84.63 259	86.15 278	78.41 202	92.17 260	90.71 290	74.92 265	62.05 304	77.00 314	65.83 200	91.83 301	68.69 243	55.01 322	83.64 313

OurMVSNet-221017-0		77.18 267	76.06 253	80.55 298	83.78 303	60.00 320	90.35 276	91.05 281	77.01 242	66.62 277	87.92 221	47.73 303	94.03 275	71.63 217	68.44 276	87.62 269

v13		77.11 268	74.63 269	84.55 261	86.08 281	78.27 205	92.06 262	90.68 292	74.73 268	61.86 307	76.93 315	65.73 201	91.81 304	68.55 246	55.07 320	83.59 314

testing_2		76.96 269	73.18 279	88.30 196	75.87 328	79.64 160	89.92 279	94.21 201	80.16 197	51.23 327	75.94 318	33.94 330	95.81 216	82.28 136	75.12 235	89.46 226

TransMVSNet (Re)		76.94 270	74.38 273	84.62 260	85.92 283	75.25 248	95.28 184	89.18 303	73.88 277	67.22 273	86.46 246	59.64 239	94.10 274	59.24 290	52.57 328	84.50 301

EU-MVSNet		76.92 271	76.95 245	76.83 308	84.10 300	54.73 329	91.77 267	92.71 262	72.74 285	69.57 266	88.69 209	58.03 258	87.43 325	64.91 265	70.00 265	88.33 257

Patchmatch-RL test		76.65 272	74.01 276	84.55 261	77.37 323	64.23 308	78.49 328	82.84 336	78.48 225	64.63 287	73.40 324	76.05 107	91.70 306	76.99 178	57.84 310	97.72 82

FMVSNet5		76.46 273	74.16 275	83.35 282	90.05 219	76.17 240	89.58 281	89.85 297	71.39 294	65.29 285	80.42 297	50.61 292	87.70 324	61.05 283	69.24 273	86.18 288

SixPastTwentyTwo		76.04 274	74.32 274	81.22 295	84.54 295	61.43 318	91.16 272	89.30 302	77.89 228	64.04 288	86.31 251	48.23 299	94.29 271	63.54 276	63.84 300	87.93 264

AllTest		75.92 275	73.06 280	84.47 263	92.18 181	67.29 300	91.07 273	84.43 328	67.63 302	63.48 289	90.18 194	38.20 323	97.16 157	57.04 294	73.37 240	88.97 239

COLMAP_ROB	73.24 19	75.74 276	73.00 281	83.94 272	92.38 172	69.08 296	91.85 266	86.93 318	61.48 320	65.32 284	90.27 193	42.27 316	96.93 169	50.91 318	75.63 231	85.80 292
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CMPMVS	54.94 21	75.71 277	74.56 271	79.17 304	79.69 316	55.98 326	89.59 280	93.30 253	60.28 324	53.85 325	89.07 205	47.68 304	96.33 187	76.55 182	81.02 203	85.22 294
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		75.29 278	73.64 277	80.22 299	80.75 311	63.38 312	93.36 231	90.71 290	73.09 282	67.12 274	83.70 282	50.33 294	90.85 312	53.63 311	70.10 260	86.44 284

EG-PatchMatch MVS		74.92 279	72.02 283	83.62 278	83.76 304	73.28 260	93.62 224	92.04 269	68.57 301	58.88 315	83.80 278	31.87 334	95.57 236	56.97 296	78.67 218	82.00 326

testgi		74.88 280	73.40 278	79.32 303	80.13 315	61.75 316	93.21 239	86.64 320	79.49 211	66.56 278	91.06 180	35.51 328	88.67 321	56.79 297	71.25 248	87.56 272

pmmvs6		74.65 281	71.67 284	83.60 279	79.13 318	69.94 289	93.31 236	90.88 285	61.05 323	65.83 281	84.15 275	43.43 311	94.83 261	66.62 254	60.63 306	86.02 291

test2356		74.41 282	74.53 272	74.07 315	76.13 327	54.45 330	94.74 204	92.08 267	71.96 290	65.51 283	83.05 288	56.96 266	83.71 335	52.74 313	77.58 225	84.06 304

K. test v3		73.62 283	71.59 285	79.69 301	82.98 306	59.85 321	90.85 275	88.83 305	77.13 238	58.90 314	82.11 289	43.62 310	91.72 305	65.83 261	54.10 325	87.50 274

pmmvs-eth3d		73.59 284	70.66 288	82.38 289	76.40 325	73.38 258	89.39 285	89.43 300	72.69 286	60.34 312	77.79 306	46.43 307	91.26 310	66.42 258	57.06 311	82.51 321

MDA-MVSNet_test_wron		73.54 285	70.43 291	82.86 284	84.55 294	71.85 271	91.74 268	91.32 277	67.63 302	46.73 333	81.09 295	55.11 281	90.42 316	55.91 305	59.76 308	86.31 286

YYNet1		73.53 286	70.43 291	82.85 285	84.52 296	71.73 275	91.69 269	91.37 276	67.63 302	46.79 332	81.21 294	55.04 282	90.43 315	55.93 304	59.70 309	86.38 285

UnsupCasMVSNet_eth		73.25 287	70.57 289	81.30 294	77.53 321	66.33 304	87.24 300	93.89 223	80.38 191	57.90 320	81.59 292	42.91 315	90.56 314	65.18 264	48.51 332	87.01 280

DSMNet-mixed		73.13 288	72.45 282	75.19 313	77.51 322	46.82 337	85.09 314	82.01 337	67.61 306	69.27 269	81.33 293	50.89 290	86.28 328	54.54 308	83.80 182	92.46 196

OpenMVS_ROB	68.52 20	73.02 289	69.57 293	83.37 281	80.54 314	71.82 272	93.60 225	88.22 310	62.37 315	61.98 305	83.15 287	35.31 329	95.47 240	45.08 328	75.88 229	82.82 318

test_0402		72.68 290	69.54 294	82.09 292	88.67 238	71.81 273	92.72 250	86.77 319	61.52 319	62.21 302	83.91 276	43.22 313	93.76 281	34.60 338	72.23 247	80.72 328

TinyColmap		72.41 291	68.99 296	82.68 286	88.11 243	69.59 293	88.41 291	85.20 325	65.55 308	57.91 319	84.82 271	30.80 336	95.94 209	51.38 314	68.70 274	82.49 323

test20.03		72.36 292	71.15 286	75.98 312	77.79 320	59.16 323	92.40 256	89.35 301	74.09 275	61.50 308	84.32 273	48.09 300	85.54 333	50.63 319	62.15 304	83.24 316

LF4IMVS		72.36 292	70.82 287	76.95 307	79.18 317	56.33 325	86.12 309	86.11 322	69.30 300	63.06 295	86.66 241	33.03 332	92.25 291	65.33 263	68.64 275	82.28 324

MDA-MVSNet-bldmvs		71.45 294	67.94 297	81.98 293	85.33 289	68.50 298	92.35 257	88.76 307	70.40 296	42.99 334	81.96 290	46.57 306	91.31 309	48.75 324	54.39 324	86.11 289

MVS-HIRNet		71.36 295	67.00 298	84.46 265	90.58 210	69.74 292	79.15 325	87.74 313	46.09 337	61.96 306	50.50 339	45.14 308	95.64 230	53.74 310	88.11 144	88.00 263

testpf		70.88 296	70.47 290	72.08 318	88.92 234	59.57 322	48.62 346	93.15 256	63.05 312	63.07 294	79.51 303	58.33 253	86.63 327	66.93 252	72.69 245	70.05 338

testus		70.06 297	69.09 295	72.98 317	74.54 330	51.28 335	93.78 221	87.34 314	71.49 293	62.69 298	83.46 284	24.44 339	84.77 334	51.22 317	72.85 244	82.90 317

MIMVSNet1		69.44 298	66.65 300	77.84 305	76.48 324	62.84 314	87.42 298	88.97 304	66.96 307	57.75 321	79.72 302	32.77 333	85.83 330	46.32 326	63.42 301	84.85 299

PM-MVS		69.32 299	66.93 299	76.49 309	73.60 331	55.84 327	85.91 310	79.32 343	74.72 269	61.09 309	78.18 305	21.76 340	91.10 311	70.86 226	56.90 312	82.51 321

TDRefinement		69.20 300	65.78 302	79.48 302	66.04 339	62.21 315	88.21 292	86.12 321	62.92 313	61.03 310	85.61 259	33.23 331	94.16 273	55.82 306	53.02 326	82.08 325

new-patchmatchnet		68.85 301	65.93 301	77.61 306	73.57 332	63.94 311	90.11 278	88.73 308	71.62 292	55.08 323	73.60 321	40.84 320	87.22 326	51.35 316	48.49 333	81.67 327

UnsupCasMVSNet_bld		68.60 302	64.50 303	80.92 297	74.63 329	67.80 299	83.97 315	92.94 259	65.12 309	54.63 324	68.23 333	35.97 326	92.17 294	60.13 285	44.83 336	82.78 319

LP		68.54 303	63.92 305	82.39 288	87.93 245	71.79 274	72.37 339	86.01 324	55.89 331	58.33 318	71.46 330	49.58 297	90.10 319	32.25 340	61.48 305	85.27 293

new_pmnet		66.18 304	63.18 306	75.18 314	76.27 326	61.74 317	83.79 316	84.66 327	56.64 330	51.57 326	71.85 328	31.29 335	87.93 323	49.98 320	62.55 303	75.86 333

pmmvs3		65.75 305	62.18 308	76.45 310	67.12 337	64.54 307	88.68 289	85.05 326	54.77 335	57.54 322	73.79 320	29.40 338	86.21 329	55.49 307	47.77 334	78.62 330

1111		65.60 306	64.33 304	69.41 320	68.26 334	45.11 340	87.06 301	87.32 315	54.99 332	51.20 328	73.45 322	63.57 217	85.70 331	36.53 335	56.59 313	77.42 332

test1235678		64.50 307	62.19 307	71.42 319	66.82 338	48.00 336	89.44 283	87.90 311	62.82 314	49.12 331	71.31 331	30.14 337	82.19 337	41.88 331	60.32 307	84.06 304

N_pmnet		61.30 308	60.20 309	64.60 325	84.32 297	17.00 355	91.67 270	10.98 355	61.77 318	58.45 317	78.55 304	49.89 295	91.83 301	42.27 330	63.94 299	84.97 298

Anonymous20231211		61.03 309	56.76 311	73.82 316	71.24 333	53.47 331	87.60 297	81.65 338	44.19 338	51.08 330	71.82 329	20.79 341	88.46 322	35.45 337	47.07 335	79.52 329

test12356		58.24 310	56.06 312	64.77 323	60.65 340	39.42 346	82.78 319	84.34 330	57.47 329	42.65 335	69.10 332	19.21 342	81.18 338	38.97 334	49.40 329	73.69 334

FPMVS		55.09 311	52.93 313	61.57 328	55.98 341	40.51 345	83.11 318	83.41 335	37.61 340	34.95 339	71.95 327	14.40 347	76.95 341	29.81 342	65.16 296	67.25 340

.test1245		54.61 312	58.07 310	44.24 335	68.26 334	45.11 340	87.06 301	87.32 315	54.99 332	51.20 328	73.45 322	63.57 217	85.70 331	36.53 335	0.21 351	1.91 351

testmv		54.58 313	51.53 314	63.74 327	53.58 345	40.82 344	83.26 317	83.92 332	54.07 336	36.35 338	61.26 334	14.80 346	77.07 340	33.00 339	43.53 339	73.33 335

LCM-MVSNet		52.52 314	48.24 315	65.35 322	47.63 349	41.45 343	72.55 338	83.62 334	31.75 341	37.66 337	57.92 337	9.19 353	76.76 342	49.26 322	44.60 337	77.84 331

no-one		51.12 315	45.81 317	67.03 321	53.16 347	52.22 332	75.21 333	80.40 340	54.89 334	28.26 342	48.50 341	15.54 345	82.81 336	39.29 333	17.06 344	66.07 341

PMMVS2		50.90 316	46.31 316	64.67 324	55.53 342	46.67 338	77.30 331	71.02 345	40.89 339	34.16 340	59.32 335	9.83 352	76.14 344	40.09 332	28.63 341	71.21 336

ANet_high		46.22 317	41.28 320	61.04 329	39.91 352	46.25 339	70.59 340	76.18 344	58.87 327	23.09 344	48.00 342	12.58 349	66.54 347	28.65 343	13.62 347	70.35 337

Gipuma		45.11 318	42.05 318	54.30 331	80.69 312	51.30 334	35.80 347	83.81 333	28.13 343	27.94 343	34.53 345	11.41 351	76.70 343	21.45 345	54.65 323	34.90 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		41.54 319	41.93 319	40.38 336	20.10 354	26.84 351	61.93 342	59.09 351	14.81 349	28.51 341	80.58 296	35.53 327	48.33 352	63.70 275	13.11 348	45.96 346

PNet_i23d		41.20 320	38.13 321	50.41 332	55.23 343	36.24 349	73.80 337	65.45 350	29.75 342	21.36 345	47.05 343	3.43 354	63.23 348	28.17 344	18.92 343	51.76 343

PMVS	34.80 23	39.19 321	35.53 322	50.18 333	29.72 353	30.30 350	59.60 344	66.20 349	26.06 344	17.91 347	49.53 340	3.12 355	74.09 345	18.19 347	49.40 329	46.14 344
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		37.75 322	31.85 325	55.46 330	40.00 351	38.01 347	59.81 343	69.47 346	25.46 345	12.42 350	30.55 349	2.06 357	67.08 346	31.81 341	15.03 345	61.29 342

pcd1.5k->3k		34.11 323	35.46 323	30.05 339	86.70 254	0.00 358	0.00 349	94.74 179	0.00 353	0.00 354	0.00 355	58.13 254	0.00 356	0.00 353	79.56 212	90.14 214

MVE	35.65 22	33.85 324	29.49 327	46.92 334	41.86 350	36.28 348	50.45 345	56.52 352	18.75 348	18.28 346	37.84 344	2.41 356	58.41 349	18.71 346	20.62 342	46.06 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		32.70 325	32.39 324	33.65 337	53.35 346	25.70 352	74.07 335	53.33 353	21.08 346	17.17 348	33.63 347	11.85 350	54.84 350	12.98 348	14.04 346	20.42 348

EMVS		31.70 326	31.45 326	32.48 338	50.72 348	23.95 353	74.78 334	52.30 354	20.36 347	16.08 349	31.48 348	12.80 348	53.60 351	11.39 349	13.10 349	19.88 349

cdsmvs_eth3d_5k		21.43 327	28.57 328	0.00 343	0.00 357	0.00 358	0.00 349	95.93 125	0.00 353	0.00 354	97.66 50	63.57 217	0.00 356	0.00 353	0.00 354	0.00 354

wuyk23d		14.10 328	13.89 329	14.72 340	55.23 343	22.91 354	33.83 348	3.56 356	4.94 350	4.11 351	2.28 354	2.06 357	19.66 353	10.23 350	8.74 350	1.59 353

testmvs		9.92 329	12.94 330	0.84 342	0.65 355	0.29 357	93.78 221	0.39 357	0.42 351	2.85 352	15.84 352	0.17 360	0.30 355	2.18 351	0.21 351	1.91 351

test123		9.07 330	11.73 331	1.11 341	0.50 356	0.77 356	89.44 283	0.20 358	0.34 352	2.15 353	10.72 353	0.34 359	0.32 354	1.79 352	0.08 353	2.23 350

ab-mvs-re		8.11 331	10.81 332	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	97.30 70	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

pcd_1.5k_mvsjas		5.92 332	7.89 333	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	71.04 159	0.00 356	0.00 353	0.00 354	0.00 354

sosnet-low-res		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

sosnet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

uncertanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

Regformer		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

uanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

GSMVS																97.54 93

test_part3							98.15 25		84.95 101		98.83 2		99.80 13	97.78 2

test_part2					98.90 7	85.14 43				96.07 7

test_part1								96.77 51				89.33 6			98.95 12	99.18 10

sam_mvs1												77.59 85				97.54 93

sam_mvs												75.35 126

semantic-postprocess				84.73 254	89.63 226	74.66 250		91.81 272	80.05 200	71.06 257	85.18 266	57.98 259	91.40 307	72.48 211	70.70 254	88.12 261

ambc				76.02 311	68.11 336	51.43 333	64.97 341	89.59 298		60.49 311	74.49 319	17.17 344	92.46 288	61.50 281	52.85 327	84.17 303

MTGPA								96.33 100

test_post1							85.88 311				30.24 350	73.77 138	95.07 256	73.89 204

test_post											33.80 346	76.17 105	95.97 204

patchmatchnet-post											77.09 312	77.78 84	95.39 242

GG-mvs-BLEND				93.49 54	94.94 118	86.26 25	81.62 320	97.00 36		88.32 92	94.30 138	91.23 2	96.21 193	88.49 84	97.43 56	98.00 65

MTMP								68.16 347

gm-plane-assit					92.27 177	79.64 160			84.47 115		95.15 122		97.93 117	85.81 103

test9_res														96.00 14	99.03 7	98.31 41

TEST9					98.64 21	83.71 67	97.82 37	96.65 64	84.29 121	95.16 14	98.09 28	84.39 21	99.36 55

test_8					98.63 23	83.64 70	97.81 39	96.63 70	84.50 113	95.10 16	98.11 27	84.33 22	99.23 61

agg_prior2														94.30 27	99.00 9	98.57 31

agg_prior					98.59 26	83.13 77		96.56 75		94.19 28			99.16 74

TestCases				84.47 263	92.18 181	67.29 300		84.43 328	67.63 302	63.48 289	90.18 194	38.20 323	97.16 157	57.04 294	73.37 240	88.97 239

test_prior4						82.34 92	97.75 46

test_prior2							98.37 18		86.08 75	94.57 25	98.02 33	83.14 33		95.05 20	98.79 15

test_prior				93.09 68	98.68 15	81.91 100		96.40 92					99.06 81			98.29 43

旧先验2							96.97 103		74.06 276	96.10 6			97.76 128	88.38 86

新几何2							96.42 140

新几何1				93.12 66	97.44 63	81.60 112		96.71 58	74.54 272	91.22 62	97.57 56	79.13 67	99.51 46	77.40 173	98.46 27	98.26 46

旧先验1					97.39 65	79.58 162		96.54 78			98.08 31	84.00 26			97.42 57	97.62 90

无先验							96.87 108	96.78 50	77.39 234				99.52 43	79.95 149		98.43 35

原ACMM2							96.84 109

原ACMM1				91.22 130	97.77 54	78.10 211		96.61 71	81.05 174	91.28 60	97.42 66	77.92 82	98.98 86	79.85 151	98.51 25	96.59 132

test222					96.15 83	78.41 202	95.87 169	96.46 85	71.97 289	89.66 75	97.45 62	76.33 103			98.24 38	98.30 42

testdata2													99.48 48	76.45 184

segment_acmp												82.69 35

testdata				90.13 158	95.92 90	74.17 255		96.49 84	73.49 280	94.82 22	97.99 36	78.80 71	97.93 117	83.53 127	97.52 52	98.29 43

testdata1							95.57 179		87.44 59

test12				94.25 26	98.34 37	85.55 35		96.35 98		92.36 45		80.84 47	99.22 63		98.31 36	97.98 67

plane_prior7					91.86 194	77.55 224

plane_prior6					91.98 189	77.92 217						64.77 212

plane_prior5								94.69 180					97.30 149	87.08 96	82.82 198	90.96 203

plane_prior4											94.15 140

plane_prior3						77.75 221			90.17 28	81.33 161

plane_prior2							97.18 78		89.89 30

plane_prior1					91.95 192

plane_prior						77.96 214	97.52 59		90.36 27						82.96 191

n2								0.00 359

nn								0.00 359

door-mid								79.75 342

lessismore_v0				79.98 300	80.59 313	58.34 324		80.87 339		58.49 316	83.46 284	43.10 314	93.89 277	63.11 278	48.68 331	87.72 266

LGP-MVS_train				86.33 231	90.88 206	73.06 262		94.13 209	82.20 158	76.31 217	93.20 157	54.83 284	96.95 166	83.72 121	80.83 204	88.98 237

test11								96.50 82

door								80.13 341

HQP5-MVS						78.48 198

HQP-NCC					92.08 184		97.63 51		90.52 24	82.30 146

ACMP_Plane					92.08 184		97.63 51		90.52 24	82.30 146

BP-MVS														87.67 93

HQP4-MVS										82.30 146			97.32 147			91.13 201

HQP3-MVS								94.80 175							83.01 189

HQP2-MVS												65.40 207

NP-MVS					92.04 188	78.22 206					94.56 134

MDTV_nov1_ep13_2view						81.74 107	86.80 304		80.65 184	85.65 111		74.26 136		76.52 183		96.98 117

MDTV_nov1_ep13			83.69 172		94.09 142	81.01 121	86.78 305	96.09 115	83.81 133	84.75 119	84.32 273	74.44 135	96.54 180	63.88 273	85.07 175

ACMMP++_ref															78.45 222

ACMMP++															79.05 215

Test By Simon												71.65 152

ITE_SJBPF				82.38 289	87.00 252	65.59 305		89.55 299	79.99 202	69.37 268	91.30 177	41.60 319	95.33 246	62.86 279	74.63 237	86.24 287

DeepMVS_CX				64.06 326	78.53 319	43.26 342		68.11 348	69.94 297	38.55 336	76.14 317	18.53 343	79.34 339	43.72 329	41.62 340	69.57 339

High-resolution multi-view benchmark