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 26	94.66 3	96.79 4	98.78 4	86.42 11	99.95 2	97.59 3	99.18 3	99.00 13

MCST-MVS		96.17 2	96.12 4	96.32 3	99.42 2	89.36 5	98.94 9	97.10 33	95.17 2	92.11 50	98.46 11	87.33 7	99.97 1	97.21 5	99.31 1	99.63 2

NCCC		95.63 3	95.94 5	94.69 20	99.21 6	85.15 44	99.16 3	96.96 43	94.11 6	95.59 13	98.64 7	85.07 14	99.91 3	95.61 18	99.10 5	99.00 13

HSP-MVS		95.55 4	96.51 2	92.66 88	98.31 40	80.10 150	97.42 70	96.46 90	92.20 13	97.11 3	98.29 14	93.46 1	99.10 79	96.01 12	99.30 2	98.77 21

ESAPD		95.32 5	95.55 6	94.64 21	98.79 12	84.87 51	97.77 41	96.74 59	86.11 75	96.54 6	98.89 2	88.39 6	99.74 19	97.67 2	99.05 8	99.31 5

HPM-MVS++		95.32 5	95.48 7	94.85 16	98.62 25	86.04 27	97.81 38	96.93 46	92.45 11	95.69 12	98.50 9	85.38 13	99.85 10	94.75 23	99.18 3	98.65 28

DELS-MVS		94.98 7	94.49 14	96.44 2	96.42 83	90.59 3	99.21 2	97.02 38	94.40 5	91.46 58	97.08 79	83.32 31	99.69 27	92.83 43	98.70 21	99.04 11
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 12	95.63 8	97.55 62	88.12 11	99.06 5	96.39 100	94.07 7	95.34 15	97.80 48	76.83 97	99.87 8	97.08 6	97.64 51	98.89 16

SD-MVS		94.84 9	95.02 9	94.29 26	97.87 55	84.61 54	97.76 45	96.19 115	89.59 32	96.66 5	98.17 22	84.33 21	99.60 36	96.09 11	98.50 26	98.66 27

TSAR-MVS + MP.		94.79 10	95.17 8	93.64 46	97.66 57	84.10 64	95.85 176	96.42 94	91.26 17	97.49 2	96.80 89	86.50 10	98.49 106	95.54 19	99.03 9	98.33 41

SMA-MVS		94.70 11	94.68 11	94.76 18	98.02 49	85.94 29	97.47 62	96.77 54	85.32 92	97.92 1	98.70 5	83.09 34	99.84 12	95.79 16	99.08 6	98.49 36

DeepPCF-MVS	89.82 1	94.61 12	96.17 3	89.91 176	97.09 77	70.21 301	98.99 8	96.69 66	95.57 1	95.08 19	99.23 1	86.40 12	99.87 8	97.84 1	98.66 22	99.65 1

APDe-MVS		94.56 13	94.75 10	93.96 35	98.84 11	83.40 79	98.04 29	96.41 95	85.79 82	95.00 21	98.28 15	84.32 24	99.18 72	97.35 4	98.77 17	99.28 6

DeepC-MVS_fast	89.06 2	94.48 14	94.30 20	95.02 14	98.86 10	85.68 34	98.06 28	96.64 72	93.64 8	91.74 55	98.54 8	80.17 57	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 15	94.50 13	93.89 36	97.38 71	83.04 85	98.10 26	95.29 163	91.57 15	93.81 35	97.45 63	86.64 8	99.43 51	96.28 10	94.01 97	99.20 9

train_agg		94.28 16	94.45 15	93.74 41	98.64 22	83.71 71	97.82 36	96.65 69	84.50 117	95.16 16	98.09 29	84.33 21	99.36 55	95.91 14	98.96 12	98.16 53

MSLP-MVS++		94.28 16	94.39 17	93.97 34	98.30 41	84.06 65	98.64 13	96.93 46	90.71 22	93.08 42	98.70 5	79.98 59	99.21 65	94.12 30	99.07 7	98.63 29

MG-MVS		94.25 18	93.72 25	95.85 7	99.38 3	89.35 6	97.98 31	98.09 14	89.99 29	92.34 49	96.97 82	81.30 47	98.99 85	88.54 85	98.88 14	99.20 9

PS-MVSNAJ		94.17 19	93.52 29	96.10 4	95.65 106	92.35 1	98.21 23	95.79 136	92.42 12	96.24 7	98.18 18	71.04 166	99.17 73	96.77 8	97.39 58	96.79 130

SteuartSystems-ACMMP		94.13 20	94.44 16	93.20 66	95.41 113	81.35 120	99.02 7	96.59 78	89.50 33	94.18 33	98.36 13	83.68 29	99.45 50	94.77 22	98.45 28	98.81 19
Skip Steuart: Steuart Systems R&D Blog.
agg_prior3		94.10 21	94.29 21	93.53 54	98.62 25	83.03 86	97.80 40	96.64 72	84.28 127	95.01 20	98.03 33	83.40 30	99.41 52	95.91 14	98.96 12	98.16 53

agg_prior1		94.10 21	94.31 19	93.48 57	98.59 27	83.13 82	97.77 41	96.56 80	84.38 122	94.19 31	98.13 24	84.66 18	99.16 74	95.74 17	98.74 19	98.15 55

EPNet		94.06 23	94.15 22	93.76 40	97.27 74	84.35 59	98.29 20	97.64 18	94.57 4	95.36 14	96.88 85	79.96 60	99.12 78	91.30 57	96.11 76	97.82 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_prior3		94.03 24	94.34 18	93.09 72	98.68 16	81.91 104	98.37 18	96.40 97	86.08 77	94.57 27	98.02 34	83.14 32	99.06 81	95.05 20	98.79 15	98.29 45

Regformer-1		94.00 25	94.04 23	93.87 37	98.41 35	84.29 61	97.43 68	97.04 37	89.50 33	92.75 46	98.13 24	82.60 36	99.26 60	93.55 33	96.99 64	98.06 61

xiu_mvs_v2_base		93.92 26	93.26 31	95.91 6	95.07 123	92.02 2	98.19 24	95.68 140	92.06 14	96.01 11	98.14 23	70.83 169	98.96 87	96.74 9	96.57 72	96.76 133

Regformer-2		93.92 26	94.01 24	93.67 45	98.41 35	83.75 70	97.43 68	97.00 39	89.43 35	92.69 47	98.13 24	82.48 37	99.22 63	93.51 34	96.99 64	98.04 62

lupinMVS		93.87 28	93.58 28	94.75 19	93.00 174	88.08 12	99.15 4	95.50 149	91.03 19	94.90 22	97.66 51	78.84 71	97.56 142	94.64 26	97.46 53	98.62 30

MVS_0304		93.82 29	93.11 35	95.95 5	96.79 79	89.15 7	98.56 15	95.30 162	93.61 9	94.82 24	98.02 34	66.60 200	99.88 7	96.94 7	97.39 58	98.81 19

APD-MVS		93.61 30	93.59 27	93.69 44	98.76 13	83.26 80	97.21 77	96.09 120	82.41 162	94.65 26	98.21 17	81.96 39	98.81 97	94.65 25	98.36 35	99.01 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PHI-MVS		93.59 31	93.63 26	93.48 57	98.05 48	81.76 110	98.64 13	97.13 25	82.60 160	94.09 34	98.49 10	80.35 52	99.85 10	94.74 24	98.62 23	98.83 18

ACMMP_Plus		93.46 32	93.23 32	94.17 30	97.16 75	84.28 62	96.82 114	96.65 69	86.24 73	94.27 29	97.99 37	77.94 83	99.83 13	93.39 35	98.57 24	98.39 39

MVS_111021_HR		93.41 33	93.39 30	93.47 60	97.34 72	82.83 90	97.56 56	98.27 12	89.16 36	89.71 79	97.14 76	79.77 61	99.56 41	93.65 32	97.94 46	98.02 64

Regformer-3		93.19 34	93.19 33	93.19 67	98.10 46	83.01 87	97.08 98	96.98 41	88.98 37	91.35 63	97.89 44	80.80 49	99.23 61	92.30 49	95.20 86	97.32 111

PVSNet_Blended		93.13 35	92.98 37	93.57 50	97.47 63	83.86 67	99.32 1	96.73 60	91.02 20	89.53 84	96.21 97	76.42 103	99.57 39	94.29 28	95.81 83	97.29 116

CDPH-MVS		93.12 36	92.91 38	93.74 41	98.65 21	83.88 66	97.67 50	96.26 109	83.00 152	93.22 41	98.24 16	81.31 46	99.21 65	89.12 81	98.74 19	98.14 56

Regformer-4		93.06 37	93.12 34	92.89 78	98.10 46	82.20 99	97.08 98	96.92 48	88.87 39	91.23 65	97.89 44	80.57 51	99.19 70	92.21 51	95.20 86	97.29 116

#test#		92.99 38	92.99 36	92.98 75	98.71 14	81.12 123	97.77 41	96.70 64	85.75 83	91.75 53	97.97 41	78.47 76	99.71 23	91.36 56	98.41 30	98.12 58

alignmvs		92.97 39	92.26 49	95.12 13	95.54 109	87.77 15	98.67 11	96.38 101	88.04 50	93.01 43	97.45 63	79.20 67	98.60 100	93.25 40	88.76 140	98.99 15

HFP-MVS		92.89 40	92.86 39	92.98 75	98.71 14	81.12 123	97.58 54	96.70 64	85.20 97	91.75 53	97.97 41	78.47 76	99.71 23	90.95 60	98.41 30	98.12 58

PAPM		92.87 41	92.40 46	94.30 25	92.25 191	87.85 14	96.40 146	96.38 101	91.07 18	88.72 92	96.90 83	82.11 38	97.37 153	90.05 71	97.70 50	97.67 90

zzz-MVS		92.74 42	92.71 40	92.86 79	97.90 51	80.85 129	96.47 135	96.33 105	87.92 52	90.20 74	98.18 18	76.71 101	99.76 14	92.57 47	98.09 40	97.96 72

PAPR		92.74 42	92.17 51	94.45 22	98.89 9	84.87 51	97.20 79	96.20 113	87.73 57	88.40 95	98.12 27	78.71 74	99.76 14	87.99 93	96.28 74	98.74 22

jason		92.73 44	92.23 50	94.21 29	90.50 222	87.30 21	98.65 12	95.09 167	90.61 23	92.76 45	97.13 77	75.28 132	97.30 156	93.32 38	96.75 71	98.02 64
jason: jason.
region2R		92.72 45	92.70 42	92.79 82	98.68 16	80.53 138	97.53 58	96.51 85	85.22 95	91.94 51	97.98 39	77.26 90	99.67 31	90.83 63	98.37 34	98.18 50

XVS		92.69 46	92.71 40	92.63 91	98.52 30	80.29 143	97.37 72	96.44 92	87.04 67	91.38 59	97.83 47	77.24 92	99.59 37	90.46 66	98.07 42	98.02 64

ACMMPR		92.69 46	92.67 43	92.75 84	98.66 19	80.57 135	97.58 54	96.69 66	85.20 97	91.57 56	97.92 43	77.01 94	99.67 31	90.95 60	98.41 30	98.00 69

WTY-MVS		92.65 48	91.68 56	95.56 9	96.00 93	88.90 8	98.23 22	97.65 17	88.57 40	89.82 78	97.22 74	79.29 63	99.06 81	89.57 77	88.73 141	98.73 25

MP-MVS		92.61 49	92.67 43	92.42 96	98.13 45	79.73 158	97.33 74	96.20 113	85.63 85	90.53 70	97.66 51	78.14 81	99.70 26	92.12 52	98.30 37	97.85 79
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		92.58 50	92.35 47	93.29 62	97.30 73	82.53 93	96.44 140	96.04 124	84.68 112	89.12 89	98.37 12	77.48 88	99.74 19	93.31 39	98.38 33	97.59 97
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		92.54 51	92.60 45	92.34 98	98.50 32	79.90 153	98.40 17	96.40 97	84.75 109	90.48 72	98.09 29	77.40 89	99.21 65	91.15 59	98.23 39	97.92 75

MTAPA		92.45 52	92.31 48	92.86 79	97.90 51	80.85 129	92.88 259	96.33 105	87.92 52	90.20 74	98.18 18	76.71 101	99.76 14	92.57 47	98.09 40	97.96 72

canonicalmvs		92.27 53	91.22 62	95.41 11	95.80 102	88.31 9	97.09 96	94.64 195	88.49 43	92.99 44	97.31 69	72.68 151	98.57 102	93.38 37	88.58 143	99.36 4

VNet		92.11 54	91.22 62	94.79 17	96.91 78	86.98 22	97.91 32	97.96 15	86.38 72	93.65 37	95.74 103	70.16 173	98.95 90	93.39 35	88.87 139	98.43 37

CSCG		92.02 55	91.65 57	93.12 70	98.53 29	80.59 134	97.47 62	97.18 24	77.06 247	84.64 128	97.98 39	83.98 26	99.52 43	90.72 64	97.33 60	99.23 8

casdiffmvs1		91.94 56	91.49 60	93.28 63	95.02 125	83.53 75	95.37 189	95.49 150	86.52 71	94.24 30	91.65 179	79.04 69	97.74 133	91.67 54	94.45 92	98.57 31

PGM-MVS		91.93 57	91.80 54	92.32 100	98.27 42	79.74 157	95.28 190	97.27 21	83.83 137	90.89 69	97.78 49	76.12 109	99.56 41	88.82 83	97.93 48	97.66 91

mPP-MVS		91.88 58	91.82 53	92.07 109	98.38 37	78.63 200	97.29 75	96.09 120	85.12 99	88.45 94	97.66 51	75.53 116	99.68 29	89.83 73	98.02 45	97.88 76

EI-MVSNet-Vis-set		91.84 59	91.77 55	92.04 111	97.60 59	81.17 122	96.61 129	96.87 50	88.20 48	89.19 88	97.55 61	78.69 75	99.14 76	90.29 69	90.94 129	95.80 154

DP-MVS Recon		91.72 60	90.85 66	94.34 24	99.50 1	85.00 46	98.51 16	95.96 127	80.57 193	88.08 101	97.63 56	76.84 96	99.89 6	85.67 108	94.88 90	98.13 57

CHOSEN 280x420		91.71 61	91.85 52	91.29 133	94.94 126	82.69 91	87.89 308	96.17 116	85.94 79	87.27 107	94.31 140	90.27 4	95.65 238	94.04 31	95.86 81	95.53 161

HY-MVS	84.06 6	91.63 62	90.37 71	95.39 12	96.12 88	88.25 10	90.22 290	97.58 19	88.33 46	90.50 71	91.96 174	79.26 65	99.06 81	90.29 69	89.07 137	98.88 17

HPM-MVS		91.62 63	91.53 59	91.89 115	97.88 54	79.22 177	96.99 102	95.73 138	82.07 166	89.50 86	97.19 75	75.59 115	98.93 93	90.91 62	97.94 46	97.54 98
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_LR		91.60 64	91.64 58	91.47 130	95.74 103	78.79 197	96.15 159	96.77 54	88.49 43	88.64 93	97.07 80	72.33 154	99.19 70	93.13 41	96.48 73	96.43 141

DeepC-MVS	86.58 3	91.53 65	91.06 65	92.94 77	94.52 142	81.89 106	95.95 167	95.98 126	90.76 21	83.76 139	96.76 90	73.24 147	99.71 23	91.67 54	96.96 66	97.22 120
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
0601test		91.46 66	90.53 70	94.24 28	97.41 67	85.18 42	98.08 27	97.72 16	80.94 181	89.85 77	96.14 98	75.61 114	98.81 97	90.42 68	88.56 144	98.74 22

PAPM_NR		91.46 66	90.82 67	93.37 61	98.50 32	81.81 109	95.03 205	96.13 117	84.65 113	86.10 116	97.65 55	79.24 66	99.75 17	83.20 135	96.88 69	98.56 33

MVSFormer		91.36 68	90.57 69	93.73 43	93.00 174	88.08 12	94.80 211	94.48 200	80.74 189	94.90 22	97.13 77	78.84 71	95.10 263	83.77 124	97.46 53	98.02 64

EI-MVSNet-UG-set		91.35 69	91.22 62	91.73 124	97.39 68	80.68 132	96.47 135	96.83 52	87.92 52	88.30 99	97.36 68	77.84 85	99.13 77	89.43 80	89.45 135	95.37 164

PVSNet_Blended_VisFu		91.24 70	90.77 68	92.66 88	95.09 121	82.40 95	97.77 41	95.87 133	88.26 47	86.39 112	93.94 148	76.77 98	99.27 58	88.80 84	94.00 98	96.31 147

APD-MVS_3200maxsize		91.23 71	91.35 61	90.89 146	97.89 53	76.35 251	96.30 154	95.52 148	79.82 212	91.03 68	97.88 46	74.70 139	98.54 103	92.11 53	96.89 68	97.77 85

CHOSEN 1792x2688		91.07 72	90.21 74	93.64 46	95.18 119	83.53 75	96.26 156	96.13 117	88.92 38	84.90 122	93.10 165	72.86 150	99.62 35	88.86 82	95.67 84	97.79 84

CANet_DTU		90.98 73	90.04 76	93.83 38	94.76 130	86.23 26	96.32 150	93.12 268	93.11 10	93.71 36	96.82 88	63.08 228	99.48 48	84.29 119	95.12 89	95.77 155

casdiffmvs		90.98 73	90.24 72	93.19 67	94.60 133	84.15 63	95.01 206	94.98 173	84.98 104	91.53 57	91.14 187	76.72 100	97.62 139	89.78 75	93.42 109	97.81 82

sss		90.87 75	89.96 78	93.60 49	94.15 149	83.84 69	97.14 87	98.13 13	85.93 80	89.68 80	96.09 99	71.67 158	99.30 57	87.69 95	89.16 136	97.66 91

Effi-MVS+		90.70 76	89.90 81	93.09 72	93.61 163	83.48 77	95.20 193	92.79 272	83.22 147	91.82 52	95.70 105	71.82 157	97.48 149	91.25 58	93.67 102	98.32 42

1121		90.66 77	89.82 83	93.16 69	97.39 68	81.71 114	93.33 245	96.66 68	74.45 281	91.38 59	97.55 61	79.27 64	99.52 43	79.95 154	98.43 29	98.26 48

MAR-MVS		90.63 78	90.22 73	91.86 120	98.47 34	78.20 219	97.18 81	96.61 76	83.87 136	88.18 100	98.18 18	68.71 178	99.75 17	83.66 129	97.15 62	97.63 94
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 79	88.64 98	96.50 1	94.25 147	90.53 4	93.33 245	97.21 23	77.59 238	78.88 198	97.31 69	71.52 161	99.69 27	89.60 76	98.03 44	99.27 7

xiu_mvs_v1_base_debu		90.54 80	89.54 87	93.55 51	92.31 184	87.58 18	96.99 102	94.87 178	87.23 62	93.27 38	97.56 58	57.43 270	98.32 110	92.72 44	93.46 106	94.74 178

xiu_mvs_v1_base		90.54 80	89.54 87	93.55 51	92.31 184	87.58 18	96.99 102	94.87 178	87.23 62	93.27 38	97.56 58	57.43 270	98.32 110	92.72 44	93.46 106	94.74 178

xiu_mvs_v1_base_debi		90.54 80	89.54 87	93.55 51	92.31 184	87.58 18	96.99 102	94.87 178	87.23 62	93.27 38	97.56 58	57.43 270	98.32 110	92.72 44	93.46 106	94.74 178

DWT-MVSNet_test		90.52 83	89.80 84	92.70 87	95.73 105	82.20 99	93.69 234	96.55 82	88.34 45	87.04 110	95.34 112	86.53 9	97.55 143	76.32 191	88.66 142	98.34 40

ACMMP		90.39 84	89.97 77	91.64 126	97.58 61	78.21 218	96.78 117	96.72 62	84.73 110	84.72 126	97.23 73	71.22 163	99.63 34	88.37 90	92.41 115	97.08 122
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 85	90.17 75	91.03 141	97.61 58	77.35 238	97.15 86	95.48 151	79.51 217	88.79 91	96.90 83	71.64 160	98.81 97	87.01 102	97.44 55	96.94 124

MVS_Test		90.29 86	89.18 93	93.62 48	95.23 117	84.93 47	94.41 217	94.66 192	84.31 124	90.37 73	91.02 189	75.13 133	97.82 129	83.11 137	94.42 93	98.12 58

API-MVS		90.18 87	88.97 94	93.80 39	98.66 19	82.95 89	97.50 61	95.63 143	75.16 268	86.31 113	97.69 50	72.49 152	99.90 4	81.26 146	96.07 77	98.56 33

PatchFormer-LS_test		90.14 88	89.30 91	92.65 90	95.43 111	82.46 94	93.46 241	96.35 103	88.56 41	84.82 123	95.22 119	84.63 19	97.55 143	78.40 168	86.81 155	97.94 74

PVSNet_BlendedMVS		90.05 89	89.96 78	90.33 157	97.47 63	83.86 67	98.02 30	96.73 60	87.98 51	89.53 84	89.61 210	76.42 103	99.57 39	94.29 28	79.59 217	87.57 280

diffmvs1		89.92 90	89.20 92	92.09 107	94.14 150	80.52 139	93.69 234	94.25 211	84.71 111	89.91 76	91.00 191	74.90 137	97.55 143	86.71 104	93.48 104	98.18 50

TESTMET0.1,1		89.83 91	89.34 90	91.31 131	92.54 182	80.19 148	97.11 92	96.57 79	86.15 74	86.85 111	91.83 178	79.32 62	96.95 173	81.30 145	92.35 116	96.77 132

abl_6		89.80 92	89.71 86	90.07 167	96.53 82	75.52 257	94.48 214	95.04 170	81.12 179	89.22 87	97.00 81	68.83 177	98.96 87	89.86 72	95.27 85	95.73 156

EPP-MVSNet		89.76 93	89.72 85	89.87 177	93.78 158	76.02 254	97.22 76	96.51 85	79.35 219	85.11 120	95.01 132	84.82 15	97.10 168	87.46 98	88.21 147	96.50 139

CPTT-MVS		89.72 94	89.87 82	89.29 186	98.33 39	73.30 271	97.70 48	95.35 160	75.68 260	87.40 104	97.44 66	70.43 170	98.25 113	89.56 78	96.90 67	96.33 146

3Dnovator+	82.88 8	89.63 95	87.85 108	94.99 15	94.49 145	86.76 23	97.84 35	95.74 137	86.10 76	75.47 240	96.02 100	65.00 217	99.51 46	82.91 139	97.07 63	98.72 26

CDS-MVSNet		89.50 96	88.96 95	91.14 139	91.94 204	80.93 127	97.09 96	95.81 135	84.26 128	84.72 126	94.20 143	80.31 53	95.64 239	83.37 134	88.96 138	96.85 129
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PMMVS		89.46 97	89.92 80	88.06 209	94.64 131	69.57 307	96.22 157	94.95 174	87.27 61	91.37 62	96.54 94	65.88 205	97.39 152	88.54 85	93.89 99	97.23 119

HyFIR lowres test		89.36 98	88.60 99	91.63 127	94.91 128	80.76 131	95.60 183	95.53 146	82.56 161	84.03 133	91.24 185	78.03 82	96.81 181	87.07 101	88.41 145	97.32 111

3Dnovator	82.32 10	89.33 99	87.64 113	94.42 23	93.73 162	85.70 33	97.73 47	96.75 58	86.73 70	76.21 231	95.93 101	62.17 232	99.68 29	81.67 144	97.81 49	97.88 76

LFMVS		89.27 100	87.64 113	94.16 32	97.16 75	85.52 37	97.18 81	94.66 192	79.17 224	89.63 82	96.57 93	55.35 288	98.22 114	89.52 79	89.54 134	98.74 22

MVSTER		89.25 101	88.92 96	90.24 159	95.98 94	84.66 53	96.79 116	95.36 158	87.19 65	80.33 181	90.61 197	90.02 5	95.97 211	85.38 111	78.64 226	90.09 226

CostFormer		89.08 102	88.39 102	91.15 138	93.13 172	79.15 180	88.61 303	96.11 119	83.14 148	89.58 83	86.93 243	83.83 28	96.87 178	88.22 91	85.92 165	97.42 107

diffmvs		89.05 103	88.22 103	91.55 128	93.88 157	79.73 158	93.18 255	94.40 205	84.43 120	88.32 97	90.40 201	72.91 149	97.41 150	84.71 116	91.74 124	97.51 102

PVSNet	82.34 9	89.02 104	87.79 110	92.71 86	95.49 110	81.50 117	97.70 48	97.29 20	87.76 56	85.47 118	95.12 128	56.90 275	98.90 94	80.33 149	94.02 96	97.71 88

test-mter		88.95 105	88.60 99	89.98 172	92.26 189	77.23 240	97.11 92	95.96 127	85.32 92	86.30 114	91.38 182	76.37 105	96.78 183	80.82 147	91.92 122	95.94 151

1314		88.94 106	87.20 124	94.17 30	93.21 169	85.73 32	93.33 245	96.64 72	82.89 153	75.98 233	96.36 95	66.83 196	99.39 53	83.52 133	96.02 79	97.39 109

UA-Net		88.92 107	88.48 101	90.24 159	94.06 153	77.18 242	93.04 256	94.66 192	87.39 60	91.09 67	93.89 149	74.92 136	98.18 117	75.83 195	91.43 126	95.35 165

thres200		88.92 107	87.65 112	92.73 85	96.30 84	85.62 35	97.85 34	98.86 1	84.38 122	84.82 123	93.99 147	75.12 134	98.01 118	70.86 233	86.67 156	94.56 181

Vis-MVSNet (Re-imp)		88.88 109	88.87 97	88.91 192	93.89 156	74.43 265	96.93 110	94.19 215	84.39 121	83.22 146	95.67 107	78.24 79	94.70 273	78.88 165	94.40 94	97.61 96

AdaColmap		88.81 110	87.61 116	92.39 97	99.33 4	79.95 151	96.70 124	95.58 144	77.51 239	83.05 148	96.69 92	61.90 239	99.72 22	84.29 119	93.47 105	97.50 103

OMC-MVS		88.80 111	88.16 104	90.72 149	95.30 116	77.92 227	94.81 210	94.51 199	86.80 69	84.97 121	96.85 86	67.53 182	98.60 100	85.08 112	87.62 150	95.63 159

114514_t		88.79 112	87.57 117	92.45 95	98.21 43	81.74 111	96.99 102	95.45 155	75.16 268	82.48 151	95.69 106	68.59 179	98.50 105	80.33 149	95.18 88	97.10 121

mvs_anonymous		88.68 113	87.62 115	91.86 120	94.80 129	81.69 115	93.53 240	94.92 175	82.03 167	78.87 199	90.43 200	75.77 113	95.34 254	85.04 113	93.16 110	98.55 35

Vis-MVSNet		88.67 114	87.82 109	91.24 136	92.68 177	78.82 194	96.95 108	93.85 236	87.55 58	87.07 109	95.13 127	63.43 226	97.21 161	77.58 177	96.15 75	97.70 89
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		88.67 114	88.16 104	90.20 161	93.61 163	76.86 245	96.77 119	93.07 269	84.02 132	83.62 140	95.60 109	74.69 140	96.24 199	78.43 167	93.66 103	97.49 104

IB-MVS	85.34 4	88.67 114	87.14 128	93.26 64	93.12 173	84.32 60	98.76 10	97.27 21	87.19 65	79.36 195	90.45 199	83.92 27	98.53 104	84.41 118	69.79 274	96.93 125
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		88.60 117	87.47 120	92.00 112	93.21 169	80.97 126	96.47 135	92.46 275	83.64 142	80.86 174	97.30 71	80.24 55	97.62 139	77.60 176	85.49 171	97.40 108

tfpn200view9		88.48 118	87.15 126	92.47 94	96.21 85	85.30 40	97.44 64	98.85 2	83.37 145	83.99 134	93.82 150	75.36 129	97.93 120	69.04 243	86.24 161	94.17 182

test-LLR		88.48 118	87.98 106	89.98 172	92.26 189	77.23 240	97.11 92	95.96 127	83.76 139	86.30 114	91.38 182	72.30 155	96.78 183	80.82 147	91.92 122	95.94 151

TAMVS		88.48 118	87.79 110	90.56 152	91.09 214	79.18 178	96.45 138	95.88 132	83.64 142	83.12 147	93.33 161	75.94 111	95.74 230	82.40 140	88.27 146	96.75 134

thres400		88.42 121	87.15 126	92.23 102	96.21 85	85.30 40	97.44 64	98.85 2	83.37 145	83.99 134	93.82 150	75.36 129	97.93 120	69.04 243	86.24 161	93.45 198

tpmrst		88.36 122	87.38 122	91.31 131	94.36 146	79.92 152	87.32 311	95.26 165	85.32 92	88.34 96	86.13 264	80.60 50	96.70 185	83.78 123	85.34 178	97.30 114

thres100view900		88.30 123	86.95 131	92.33 99	96.10 89	84.90 48	97.14 87	98.85 2	82.69 157	83.41 141	93.66 153	75.43 124	97.93 120	69.04 243	86.24 161	94.17 182

VDD-MVS		88.28 124	87.02 130	92.06 110	95.09 121	80.18 149	97.55 57	94.45 203	83.09 149	89.10 90	95.92 102	47.97 311	98.49 106	93.08 42	86.91 154	97.52 101

conf200view11		88.27 125	86.95 131	92.24 101	96.10 89	84.90 48	97.14 87	98.85 2	82.69 157	83.41 141	93.66 153	75.43 124	97.93 120	69.04 243	86.24 161	93.89 189

BH-w/o		88.24 126	87.47 120	90.54 153	95.03 124	78.54 203	97.41 71	93.82 237	84.08 130	78.23 203	94.51 139	69.34 176	97.21 161	80.21 152	94.58 91	95.87 153

tfpn111		88.08 127	86.70 135	92.20 104	96.10 89	84.90 48	97.14 87	98.85 2	82.69 157	83.41 141	93.66 153	75.43 124	97.82 129	67.13 258	85.88 166	93.89 189

thres600view7		88.06 128	86.70 135	92.15 106	96.10 89	85.17 43	97.14 87	98.85 2	82.70 156	83.41 141	93.66 153	75.43 124	97.82 129	67.13 258	85.88 166	93.45 198

Test_1112_low_res		88.03 129	86.73 134	91.94 114	93.15 171	80.88 128	96.44 140	92.41 276	83.59 144	80.74 176	91.16 186	80.18 56	97.59 141	77.48 178	85.40 172	97.36 110

PLC	83.97 7	88.00 130	87.38 122	89.83 179	98.02 49	76.46 249	97.16 85	94.43 204	79.26 223	81.98 165	96.28 96	69.36 175	99.27 58	77.71 175	92.25 119	93.77 193
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CLD-MVS		87.97 131	87.48 119	89.44 184	92.16 194	80.54 137	98.14 25	94.92 175	91.41 16	79.43 194	95.40 111	62.34 231	97.27 159	90.60 65	82.90 203	90.50 216
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Fast-Effi-MVS+		87.93 132	86.94 133	90.92 145	94.04 154	79.16 179	98.26 21	93.72 245	81.29 177	83.94 137	92.90 166	69.83 174	96.68 186	76.70 187	91.74 124	96.93 125

HQP-MVS		87.91 133	87.55 118	88.98 191	92.08 195	78.48 205	97.63 51	94.80 183	90.52 24	82.30 154	94.56 137	65.40 213	97.32 154	87.67 96	83.01 194	91.13 209

UGNet		87.73 134	86.55 137	91.27 134	95.16 120	79.11 181	96.35 148	96.23 111	88.14 49	87.83 103	90.48 198	50.65 300	99.09 80	80.13 153	94.03 95	95.60 160
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		87.65 135	87.89 107	86.93 237	94.57 134	71.37 292	96.72 120	96.50 87	88.56 41	87.12 108	95.02 131	75.91 112	94.01 286	66.62 262	90.00 133	95.42 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP_MVS		87.50 136	87.09 129	88.74 196	91.86 205	77.96 224	97.18 81	94.69 188	89.89 30	81.33 170	94.15 144	64.77 218	97.30 156	87.08 99	82.82 204	90.96 211

EPMVS		87.47 137	85.90 146	92.18 105	95.41 113	82.26 98	87.00 315	96.28 108	85.88 81	84.23 131	85.57 271	75.07 135	96.26 197	71.14 231	92.50 113	98.03 63

view600		87.45 138	85.98 141	91.88 116	95.90 96	84.52 55	96.68 125	98.85 2	81.85 169	82.30 154	93.39 157	75.44 120	97.66 134	64.02 278	85.36 173	93.45 198

view800		87.45 138	85.98 141	91.88 116	95.90 96	84.52 55	96.68 125	98.85 2	81.85 169	82.30 154	93.39 157	75.44 120	97.66 134	64.02 278	85.36 173	93.45 198

conf0.05thres1000		87.45 138	85.98 141	91.88 116	95.90 96	84.52 55	96.68 125	98.85 2	81.85 169	82.30 154	93.39 157	75.44 120	97.66 134	64.02 278	85.36 173	93.45 198

tfpn		87.45 138	85.98 141	91.88 116	95.90 96	84.52 55	96.68 125	98.85 2	81.85 169	82.30 154	93.39 157	75.44 120	97.66 134	64.02 278	85.36 173	93.45 198

tpm2		87.35 142	86.26 138	90.62 151	92.93 176	78.67 198	88.06 307	95.99 125	79.33 220	87.40 104	86.43 259	80.28 54	96.40 191	80.23 151	85.73 170	96.79 130

tfpn_ndepth		87.25 143	86.00 140	91.01 143	95.86 100	81.46 118	96.53 132	97.09 34	77.35 242	81.36 169	95.07 130	84.74 17	95.86 220	60.88 293	85.14 179	95.72 157

ab-mvs		87.08 144	84.94 159	93.48 57	93.34 168	83.67 73	88.82 300	95.70 139	81.18 178	84.55 129	90.14 206	62.72 229	98.94 92	85.49 110	82.54 208	97.85 79

CNLPA		86.96 145	85.37 150	91.72 125	97.59 60	79.34 172	97.21 77	91.05 295	74.22 282	78.90 197	96.75 91	67.21 186	98.95 90	74.68 205	90.77 130	96.88 128

BH-untuned		86.95 146	85.94 145	89.99 171	94.52 142	77.46 235	96.78 117	93.37 261	81.80 173	76.62 224	93.81 152	66.64 199	97.02 171	76.06 193	93.88 100	95.48 162

QAPM		86.88 147	84.51 163	93.98 33	94.04 154	85.89 30	97.19 80	96.05 123	73.62 286	75.12 243	95.62 108	62.02 235	99.74 19	70.88 232	96.06 78	96.30 148

BH-RMVSNet		86.84 148	85.28 151	91.49 129	95.35 115	80.26 146	96.95 108	92.21 277	82.86 154	81.77 168	95.46 110	59.34 251	97.64 138	69.79 240	93.81 101	96.57 138

mvs-test1		86.83 149	87.17 125	85.81 249	91.96 201	65.24 319	97.90 33	93.34 262	85.57 86	84.51 130	95.14 126	61.99 236	97.19 163	83.55 130	90.55 131	95.00 173

PatchmatchNet		86.83 149	85.12 154	91.95 113	94.12 151	82.27 97	86.55 319	95.64 142	84.59 115	82.98 149	84.99 281	77.26 90	95.96 215	68.61 251	91.34 127	97.64 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
nrg030		86.79 151	85.43 148	90.87 147	88.76 246	85.34 39	97.06 100	94.33 208	84.31 124	80.45 179	91.98 173	72.36 153	96.36 193	88.48 88	71.13 257	90.93 213

PCF-MVS	84.09 5	86.77 152	85.00 157	92.08 108	92.06 198	83.07 84	92.14 274	94.47 202	79.63 216	76.90 221	94.78 134	71.15 164	99.20 69	72.87 214	91.05 128	93.98 187
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FIs		86.73 153	86.10 139	88.61 198	90.05 230	80.21 147	96.14 160	96.95 44	85.56 89	78.37 202	92.30 170	76.73 99	95.28 257	79.51 157	79.27 221	90.35 218

cascas		86.50 154	84.48 165	92.55 93	92.64 181	85.95 28	97.04 101	95.07 169	75.32 265	80.50 177	91.02 189	54.33 295	97.98 119	86.79 103	87.62 150	93.71 194

tpmp4_e23		86.46 155	84.95 158	90.98 144	93.74 161	78.60 202	88.13 306	95.90 131	79.65 215	85.42 119	85.67 266	80.08 58	97.06 169	71.71 223	84.26 185	97.28 118

VDDNet		86.44 156	84.51 163	92.22 103	91.56 207	81.83 108	97.10 95	94.64 195	69.50 310	87.84 102	95.19 122	48.01 310	97.92 126	89.82 74	86.92 153	96.89 127

tfpn1000		86.43 157	85.10 155	90.41 155	95.56 108	80.51 140	95.90 172	97.09 34	75.91 257	80.02 185	94.82 133	84.78 16	95.47 249	57.36 303	84.46 182	95.26 168

TR-MVS		86.30 158	84.93 160	90.42 154	94.63 132	77.58 233	96.57 131	93.82 237	80.30 200	82.42 153	95.16 124	58.74 256	97.55 143	74.88 203	87.82 149	96.13 149

X-MVStestdata		86.26 159	84.14 176	92.63 91	98.52 30	80.29 143	97.37 72	96.44 92	87.04 67	91.38 59	20.73 363	77.24 92	99.59 37	90.46 66	98.07 42	98.02 64

OpenMVS	79.58 14	86.09 160	83.62 182	93.50 55	90.95 216	86.71 24	97.44 64	95.83 134	75.35 264	72.64 259	95.72 104	57.42 273	99.64 33	71.41 226	95.85 82	94.13 185

FC-MVSNet-test		85.96 161	85.39 149	87.66 222	89.38 242	78.02 222	95.65 182	96.87 50	85.12 99	77.34 214	91.94 176	76.28 107	94.74 272	77.09 183	78.82 224	90.21 221

DI_MVS_plusplus_test		85.92 162	83.61 183	92.86 79	86.43 277	83.20 81	95.57 184	95.46 152	85.10 102	65.99 293	86.84 247	56.70 277	97.89 127	88.10 92	92.33 117	97.48 105

OPM-MVS		85.84 163	85.10 155	88.06 209	88.34 252	77.83 230	95.72 179	94.20 213	87.89 55	80.45 179	94.05 146	58.57 257	97.26 160	83.88 122	82.76 206	89.09 242

test_normal		85.83 164	83.51 185	92.78 83	86.33 282	83.01 87	95.56 186	95.46 152	85.11 101	65.73 295	86.63 252	56.62 280	97.86 128	87.87 94	92.29 118	97.47 106

thresconf0.02		85.80 165	84.35 168	90.17 162	94.53 136	79.70 160	95.17 195	97.11 27	75.97 251	79.44 188	95.31 113	81.90 40	95.73 231	56.78 308	82.91 197	95.09 169

tfpn_n400		85.80 165	84.35 168	90.17 162	94.53 136	79.70 160	95.17 195	97.11 27	75.97 251	79.44 188	95.31 113	81.90 40	95.73 231	56.78 308	82.91 197	95.09 169

tfpnconf		85.80 165	84.35 168	90.17 162	94.53 136	79.70 160	95.17 195	97.11 27	75.97 251	79.44 188	95.31 113	81.90 40	95.73 231	56.78 308	82.91 197	95.09 169

tfpnview11		85.80 165	84.35 168	90.17 162	94.53 136	79.70 160	95.17 195	97.11 27	75.97 251	79.44 188	95.31 113	81.90 40	95.73 231	56.78 308	82.91 197	95.09 169

EI-MVSNet		85.80 165	85.20 152	87.59 224	91.55 208	77.41 236	95.13 201	95.36 158	80.43 197	80.33 181	94.71 135	73.72 145	95.97 211	76.96 186	78.64 226	89.39 235

GA-MVS		85.79 170	84.04 177	91.02 142	89.47 240	80.27 145	96.90 111	94.84 181	85.57 86	80.88 173	89.08 213	56.56 281	96.47 190	77.72 174	85.35 177	96.34 144

XVG-OURS-SEG-HR		85.74 171	85.16 153	87.49 228	90.22 226	71.45 291	91.29 284	94.09 227	81.37 176	83.90 138	95.22 119	60.30 243	97.53 148	85.58 109	84.42 184	93.50 196

conf0.01		85.70 172	84.35 168	89.77 181	94.53 136	79.70 160	95.17 195	97.11 27	75.97 251	79.44 188	95.31 113	81.90 40	95.73 231	56.78 308	82.91 197	93.89 189

conf0.002		85.70 172	84.35 168	89.77 181	94.53 136	79.70 160	95.17 195	97.11 27	75.97 251	79.44 188	95.31 113	81.90 40	95.73 231	56.78 308	82.91 197	93.89 189

tpm		85.55 174	84.47 166	88.80 195	90.19 227	75.39 259	88.79 301	94.69 188	84.83 108	83.96 136	85.21 275	78.22 80	94.68 274	76.32 191	78.02 232	96.34 144

UniMVSNet_NR-MVSNet		85.49 175	84.59 162	88.21 208	89.44 241	79.36 170	96.71 122	96.41 95	85.22 95	78.11 204	90.98 192	76.97 95	95.14 261	79.14 162	68.30 288	90.12 224

gg-mvs-nofinetune		85.48 176	82.90 192	93.24 65	94.51 144	85.82 31	79.22 336	96.97 42	61.19 332	87.33 106	53.01 350	90.58 3	96.07 204	86.07 106	97.23 61	97.81 82

VPA-MVSNet		85.32 177	83.83 178	89.77 181	90.25 225	82.63 92	96.36 147	97.07 36	83.03 151	81.21 172	89.02 215	61.58 240	96.31 195	85.02 114	70.95 259	90.36 217

UniMVSNet (Re)		85.31 178	84.23 175	88.55 199	89.75 233	80.55 136	96.72 120	96.89 49	85.42 90	78.40 201	88.93 216	75.38 128	95.52 246	78.58 166	68.02 291	89.57 233

XVG-OURS		85.18 179	84.38 167	87.59 224	90.42 224	71.73 287	91.06 287	94.07 228	82.00 168	83.29 145	95.08 129	56.42 282	97.55 143	83.70 128	83.42 190	93.49 197

TAPA-MVS	81.61 12	85.02 180	83.67 180	89.06 188	96.79 79	73.27 273	95.92 169	94.79 185	74.81 275	80.47 178	96.83 87	71.07 165	98.19 116	49.82 333	92.57 112	95.71 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		85.00 181	83.66 181	89.02 190	95.86 100	74.55 264	92.49 266	93.60 250	79.30 222	79.29 196	91.47 180	58.53 258	98.45 108	70.22 236	92.17 120	94.07 186

PS-MVSNAJss		84.91 182	84.30 174	86.74 238	85.89 296	74.40 266	94.95 207	94.16 220	83.93 134	76.45 226	90.11 207	71.04 166	95.77 225	83.16 136	79.02 223	90.06 228

Patchmatch-test1		84.89 183	82.76 195	91.27 134	92.30 187	81.86 107	92.88 259	95.56 145	84.85 107	82.52 150	85.19 276	58.04 264	94.21 282	65.93 268	87.58 152	97.74 86

CVMVSNet		84.83 184	85.57 147	82.63 300	91.55 208	60.38 332	95.13 201	95.03 171	80.60 192	82.10 164	94.71 135	66.40 202	90.19 329	74.30 208	90.32 132	97.31 113

FMVSNet3		84.71 185	82.71 196	90.70 150	94.55 135	87.71 16	95.92 169	94.67 191	81.73 174	75.82 236	88.08 228	66.99 194	94.47 277	71.23 228	75.38 240	89.91 230

VPNet		84.69 186	82.92 191	90.01 170	89.01 244	83.45 78	96.71 122	95.46 152	85.71 84	79.65 187	92.18 172	56.66 279	96.01 210	83.05 138	67.84 294	90.56 215

Effi-MVS+-dtu		84.61 187	84.90 161	83.72 290	91.96 201	63.14 326	94.95 207	93.34 262	85.57 86	79.79 186	87.12 240	61.99 236	95.61 242	83.55 130	85.83 168	92.41 205

DU-MVS		84.57 188	83.33 188	88.28 206	88.76 246	79.36 170	96.43 144	95.41 157	85.42 90	78.11 204	90.82 193	67.61 180	95.14 261	79.14 162	68.30 288	90.33 219

F-COLMAP		84.50 189	83.44 187	87.67 221	95.22 118	72.22 278	95.95 167	93.78 242	75.74 258	76.30 229	95.18 123	59.50 248	98.45 108	72.67 216	86.59 158	92.35 206

Anonymous202405211		84.41 190	81.93 205	91.85 122	96.78 81	78.41 209	97.44 64	91.34 290	70.29 307	84.06 132	94.26 142	41.09 331	98.96 87	79.46 158	82.65 207	98.17 52

WR-MVS		84.32 191	82.96 190	88.41 201	89.38 242	80.32 142	96.59 130	96.25 110	83.97 133	76.63 223	90.36 202	67.53 182	94.86 269	75.82 196	70.09 269	90.06 228

dp		84.30 192	82.31 200	90.28 158	94.24 148	77.97 223	86.57 318	95.53 146	79.94 210	80.75 175	85.16 278	71.49 162	96.39 192	63.73 283	83.36 191	96.48 140

LPG-MVS_test		84.20 193	83.49 186	86.33 241	90.88 217	73.06 274	95.28 190	94.13 221	82.20 164	76.31 227	93.20 162	54.83 293	96.95 173	83.72 126	80.83 211	88.98 246

ACMP	81.66 11	84.00 194	83.22 189	86.33 241	91.53 210	72.95 276	95.91 171	93.79 241	83.70 141	73.79 247	92.22 171	54.31 296	96.89 177	83.98 121	79.74 216	89.16 241
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 195	82.80 194	87.31 232	91.46 211	77.39 237	95.66 181	93.43 255	80.44 195	75.51 239	87.26 236	73.72 145	95.16 260	76.99 184	70.72 261	89.39 235
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		83.84 196	82.00 201	89.35 185	87.13 263	81.38 119	95.72 179	94.26 210	80.15 205	75.92 235	90.63 196	61.96 238	96.52 188	78.98 164	73.28 251	90.14 222

LCM-MVSNet-Re		83.75 197	83.54 184	84.39 280	93.54 165	64.14 322	92.51 265	84.03 345	83.90 135	66.14 292	86.59 253	67.36 184	92.68 298	84.89 115	92.87 111	96.35 143

ACMM	80.70 13	83.72 198	82.85 193	86.31 244	91.19 213	72.12 281	95.88 173	94.29 209	80.44 195	77.02 219	91.96 174	55.24 289	97.14 167	79.30 160	80.38 213	89.67 232
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm cat1		83.63 199	81.38 216	90.39 156	93.53 166	78.19 220	85.56 325	95.09 167	70.78 305	78.51 200	83.28 299	74.80 138	97.03 170	66.77 261	84.05 186	95.95 150

CR-MVSNet		83.53 200	81.36 217	90.06 168	90.16 228	79.75 155	79.02 338	91.12 292	84.24 129	82.27 162	80.35 311	75.45 118	93.67 292	63.37 286	86.25 159	96.75 134

v2v482		83.46 201	81.86 206	88.25 207	86.19 288	79.65 166	96.34 149	94.02 229	81.56 175	77.32 215	88.23 225	65.62 208	96.03 206	77.77 169	69.72 276	89.09 242

v1neww		83.45 202	81.95 202	87.95 214	86.66 267	79.04 185	96.32 150	94.17 217	80.76 186	77.56 207	87.25 237	67.02 192	96.08 202	77.73 171	70.07 270	88.74 256

v7new		83.45 202	81.95 202	87.95 214	86.66 267	79.04 185	96.32 150	94.17 217	80.76 186	77.56 207	87.25 237	67.02 192	96.08 202	77.73 171	70.07 270	88.74 256

v6		83.45 202	81.94 204	87.95 214	86.62 271	79.03 188	96.32 150	94.17 217	80.76 186	77.57 206	87.23 239	67.03 191	96.09 201	77.73 171	70.06 272	88.75 254

v1		83.37 205	81.82 207	88.01 211	86.58 275	79.24 174	96.45 138	94.13 221	80.88 182	77.48 211	86.88 244	67.15 187	96.04 205	77.15 180	69.67 278	88.76 252

v1141		83.36 206	81.81 209	88.01 211	86.61 273	79.26 173	96.44 140	94.12 224	80.88 182	77.48 211	86.87 245	67.08 189	96.03 206	77.14 181	69.69 277	88.75 254

divwei89l23v2f112		83.36 206	81.81 209	88.01 211	86.60 274	79.23 176	96.44 140	94.12 224	80.88 182	77.49 209	86.87 245	67.08 189	96.03 206	77.14 181	69.67 278	88.76 252

NR-MVSNet		83.35 208	81.52 214	88.84 193	88.76 246	81.31 121	94.45 216	95.16 166	84.65 113	67.81 284	90.82 193	70.36 171	94.87 268	74.75 204	66.89 301	90.33 219

Fast-Effi-MVS+-dtu		83.33 209	82.60 197	85.50 253	89.55 238	69.38 308	96.09 164	91.38 287	82.30 163	75.96 234	91.41 181	56.71 276	95.58 244	75.13 202	84.90 181	91.54 207

TranMVSNet+NR-MVSNet		83.24 210	81.71 211	87.83 217	87.71 259	78.81 196	96.13 162	94.82 182	84.52 116	76.18 232	90.78 195	64.07 221	94.60 275	74.60 206	66.59 305	90.09 226

Anonymous20240529		83.15 211	80.60 225	90.80 148	95.74 103	78.27 213	96.81 115	94.92 175	60.10 337	81.89 167	92.54 169	45.82 318	98.82 96	79.25 161	78.32 230	95.31 166

MS-PatchMatch		83.05 212	81.82 207	86.72 240	89.64 236	79.10 182	94.88 209	94.59 198	79.70 214	70.67 270	89.65 209	50.43 302	96.82 180	70.82 235	95.99 80	84.25 314

V42		83.04 213	81.53 213	87.57 226	86.27 286	79.09 183	95.87 174	94.11 226	80.35 199	77.22 217	86.79 250	65.32 215	96.02 209	77.74 170	70.14 265	87.61 279

tpmvs		83.04 213	80.77 222	89.84 178	95.43 111	77.96 224	85.59 324	95.32 161	75.31 266	76.27 230	83.70 295	73.89 143	97.41 150	59.53 295	81.93 209	94.14 184

test_djsdf		83.00 215	82.45 199	84.64 270	84.07 315	69.78 304	94.80 211	94.48 200	80.74 189	75.41 241	87.70 231	61.32 241	95.10 263	83.77 124	79.76 214	89.04 244

v7		82.99 216	81.41 215	87.73 220	86.41 278	78.86 193	96.10 163	93.98 230	79.88 211	77.49 209	87.11 241	65.44 211	95.97 211	75.69 198	70.59 263	88.36 264

v1144		82.90 217	81.27 218	87.78 219	86.29 284	79.07 184	96.14 160	93.93 232	80.05 207	77.38 213	86.80 249	65.50 209	95.93 217	75.21 201	70.13 266	88.33 266

test0.0.03 1		82.79 218	82.48 198	83.74 289	86.81 265	72.22 278	96.52 133	95.03 171	83.76 139	73.00 255	93.20 162	72.30 155	88.88 332	64.15 277	77.52 234	90.12 224

FMVSNet2		82.79 218	80.44 227	89.83 179	92.66 178	85.43 38	95.42 188	94.35 207	79.06 226	74.46 244	87.28 234	56.38 283	94.31 280	69.72 241	74.68 244	89.76 231

MVP-Stereo		82.65 220	81.67 212	85.59 252	86.10 292	78.29 212	93.33 245	92.82 271	77.75 236	69.17 282	87.98 229	59.28 252	95.76 226	71.77 222	96.88 69	82.73 332
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs4		82.54 221	80.79 221	87.79 218	86.11 291	80.49 141	93.55 239	93.18 265	77.29 243	73.35 251	89.40 212	65.26 216	95.05 266	75.32 200	73.61 247	87.83 274

v144192		82.43 222	80.73 223	87.54 227	85.81 297	78.22 215	95.98 165	93.78 242	79.09 225	77.11 218	86.49 255	64.66 220	95.91 218	74.20 209	69.42 281	88.49 259

GBi-Net		82.42 223	80.43 228	88.39 202	92.66 178	81.95 101	94.30 221	93.38 258	79.06 226	75.82 236	85.66 267	56.38 283	93.84 288	71.23 228	75.38 240	89.38 237

test1		82.42 223	80.43 228	88.39 202	92.66 178	81.95 101	94.30 221	93.38 258	79.06 226	75.82 236	85.66 267	56.38 283	93.84 288	71.23 228	75.38 240	89.38 237

v148		82.41 225	80.89 220	86.99 236	86.18 289	76.81 246	96.27 155	93.82 237	80.49 194	75.28 242	86.11 265	67.32 185	95.75 227	75.48 199	67.03 300	88.42 263

v1192		82.31 226	80.55 226	87.60 223	85.94 294	78.47 208	95.85 176	93.80 240	79.33 220	76.97 220	86.51 254	63.33 227	95.87 219	73.11 213	70.13 266	88.46 261

Test4		82.30 227	79.15 242	91.78 123	81.84 321	81.74 111	94.04 227	94.20 213	84.86 106	59.75 327	83.88 290	37.14 337	96.28 196	84.60 117	92.00 121	97.30 114

LS3D		82.22 228	79.94 236	89.06 188	97.43 66	74.06 269	93.20 253	92.05 279	61.90 328	73.33 252	95.21 121	59.35 250	99.21 65	54.54 319	92.48 114	93.90 188

jajsoiax		82.12 229	81.15 219	85.03 258	84.19 313	70.70 297	94.22 225	93.95 231	83.07 150	73.48 249	89.75 208	49.66 305	95.37 253	82.24 142	79.76 214	89.02 245

v1921920		82.02 230	80.23 230	87.41 229	85.62 298	77.92 227	95.79 178	93.69 246	78.86 229	76.67 222	86.44 257	62.50 230	95.83 222	72.69 215	69.77 275	88.47 260

v8		81.88 231	80.06 234	87.32 231	86.63 270	79.04 185	94.41 217	93.65 248	78.77 230	73.19 254	85.57 271	66.87 195	95.81 223	73.84 212	67.61 296	87.11 288

mvs_tets		81.74 232	80.71 224	84.84 262	84.22 312	70.29 300	93.91 229	93.78 242	82.77 155	73.37 250	89.46 211	47.36 315	95.31 256	81.99 143	79.55 220	88.92 250

v1240		81.70 233	79.83 237	87.30 233	85.50 299	77.70 232	95.48 187	93.44 253	78.46 233	76.53 225	86.44 257	60.85 242	95.84 221	71.59 225	70.17 264	88.35 265

PVSNet_0	77.72 15	81.70 233	78.95 243	89.94 175	90.77 219	76.72 248	95.96 166	96.95 44	85.01 103	70.24 276	88.53 222	52.32 297	98.20 115	86.68 105	44.08 349	94.89 174

DP-MVS		81.47 235	78.28 245	91.04 140	98.14 44	78.48 205	95.09 204	86.97 331	61.14 333	71.12 267	92.78 168	59.59 246	99.38 54	53.11 323	86.61 157	95.27 167

v10		81.43 236	79.53 239	87.11 234	86.38 279	78.87 192	94.31 220	93.43 255	77.88 235	73.24 253	85.26 274	65.44 211	95.75 227	72.14 219	67.71 295	86.72 293

pmmvs5		81.34 237	79.54 238	86.73 239	85.02 306	76.91 244	96.22 157	91.65 285	77.65 237	73.55 248	88.61 219	55.70 286	94.43 278	74.12 210	73.35 250	88.86 251

ADS-MVSNet		81.26 238	78.36 244	89.96 174	93.78 158	79.78 154	79.48 334	93.60 250	73.09 291	80.14 183	79.99 313	62.15 233	95.24 259	59.49 296	83.52 188	94.85 175

Baseline_NR-MVSNet		81.22 239	80.07 233	84.68 268	85.32 304	75.12 261	96.48 134	88.80 320	76.24 250	77.28 216	86.40 260	67.61 180	94.39 279	75.73 197	66.73 304	84.54 312

WR-MVS_H		81.02 240	80.09 231	83.79 287	88.08 255	71.26 295	94.46 215	96.54 83	80.08 206	72.81 258	86.82 248	70.36 171	92.65 299	64.18 276	67.50 297	87.46 285

CP-MVSNet		81.01 241	80.08 232	83.79 287	87.91 257	70.51 298	94.29 224	95.65 141	80.83 185	72.54 261	88.84 217	63.71 222	92.32 302	68.58 252	68.36 287	88.55 258

anonymousdsp		80.98 242	79.97 235	84.01 282	81.73 322	70.44 299	92.49 266	93.58 252	77.10 246	72.98 256	86.31 261	57.58 269	94.90 267	79.32 159	78.63 228	86.69 294

IterMVS		80.67 243	79.16 241	85.20 256	89.79 232	76.08 253	92.97 258	91.86 281	80.28 201	71.20 266	85.14 279	57.93 268	91.34 320	72.52 217	70.74 260	88.18 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		80.62 244	77.77 250	89.14 187	93.43 167	77.24 239	91.89 278	90.18 309	69.86 309	68.02 283	91.94 176	52.21 298	98.84 95	59.32 298	83.12 192	91.35 208

PS-CasMVS		80.27 245	79.18 240	83.52 293	87.56 261	69.88 303	94.08 226	95.29 163	80.27 202	72.08 262	88.51 223	59.22 253	92.23 304	67.49 256	68.15 290	88.45 262

pm-mvs1		80.05 246	78.02 248	86.15 246	85.42 300	75.81 255	95.11 203	92.69 274	77.13 244	70.36 272	87.43 233	58.44 259	95.27 258	71.36 227	64.25 310	87.36 286

PatchT		79.75 247	76.85 259	88.42 200	89.55 238	75.49 258	77.37 342	94.61 197	63.07 322	82.46 152	73.32 338	75.52 117	93.41 295	51.36 327	84.43 183	96.36 142

Anonymous20240521		79.73 248	78.10 247	84.63 271	87.90 258	71.58 289	93.91 229	94.39 206	76.69 249	70.27 275	87.00 242	58.97 255	94.76 271	64.38 275	69.43 280	87.54 283

Anonymous20231211		79.72 249	77.19 254	87.33 230	95.59 107	77.16 243	95.18 194	94.18 216	59.31 339	72.57 260	86.20 263	47.89 312	95.66 237	74.53 207	69.24 282	89.18 240

ADS-MVSNet2		79.57 250	77.53 251	85.71 250	93.78 158	72.13 280	79.48 334	86.11 336	73.09 291	80.14 183	79.99 313	62.15 233	90.14 330	59.49 296	83.52 188	94.85 175

FMVSNet1		79.50 251	76.54 262	88.39 202	88.47 251	81.95 101	94.30 221	93.38 258	73.14 290	72.04 263	85.66 267	43.86 320	93.84 288	65.48 270	72.53 254	89.38 237

PEN-MVS		79.47 252	78.26 246	83.08 296	86.36 281	68.58 310	93.85 231	94.77 186	79.76 213	71.37 264	88.55 220	59.79 244	92.46 300	64.50 274	65.40 306	88.19 268

XVG-ACMP-BASELINE		79.38 253	77.90 249	83.81 286	84.98 307	67.14 316	89.03 299	93.18 265	80.26 203	72.87 257	88.15 227	38.55 334	96.26 197	76.05 194	78.05 231	88.02 271

v7n		79.32 254	77.34 252	85.28 255	84.05 316	72.89 277	93.38 243	93.87 235	75.02 271	70.68 269	84.37 285	59.58 247	95.62 241	67.60 255	67.50 297	87.32 287

RPMNet		79.32 254	75.75 266	90.06 168	90.16 228	79.75 155	79.02 338	93.92 233	58.43 341	82.27 162	72.55 339	73.03 148	93.67 292	46.10 339	86.25 159	96.75 134

MIMVSNet		79.18 256	75.99 265	88.72 197	87.37 262	80.66 133	79.96 333	91.82 282	77.38 241	74.33 245	81.87 304	41.78 328	90.74 325	66.36 267	83.10 193	94.76 177

JIA-IIPM		79.00 257	77.20 253	84.40 279	89.74 235	64.06 323	75.30 344	95.44 156	62.15 327	81.90 166	59.08 348	78.92 70	95.59 243	66.51 265	85.78 169	93.54 195

v52		78.70 258	76.95 256	83.95 283	81.71 323	71.34 293	91.93 277	93.43 255	74.69 278	70.36 272	83.71 294	58.04 264	95.50 247	71.84 220	66.82 303	85.00 309

V4		78.70 258	76.95 256	83.95 283	81.66 324	71.34 293	91.94 276	93.44 253	74.69 278	70.35 274	83.73 293	58.07 263	95.50 247	71.84 220	66.86 302	85.02 308

v748		78.69 260	76.79 260	84.39 280	83.40 319	70.78 296	93.25 251	93.62 249	74.96 272	69.40 279	83.74 292	59.40 249	95.39 251	68.74 249	64.59 308	86.99 291

USDC		78.65 261	76.25 263	85.85 248	87.58 260	74.60 263	89.58 294	90.58 308	84.05 131	63.13 307	88.23 225	40.69 333	96.86 179	66.57 264	75.81 238	86.09 302

DTE-MVSNet		78.37 262	77.06 255	82.32 304	85.22 305	67.17 315	93.40 242	93.66 247	78.71 231	70.53 271	88.29 224	59.06 254	92.23 304	61.38 291	63.28 314	87.56 281

Patchmatch-test		78.25 263	74.72 279	88.83 194	91.20 212	74.10 268	73.91 348	88.70 323	59.89 338	66.82 288	85.12 280	78.38 78	94.54 276	48.84 335	79.58 218	97.86 78

tfpnnormal		78.14 264	75.42 270	86.31 244	88.33 253	79.24 174	94.41 217	96.22 112	73.51 287	69.81 277	85.52 273	55.43 287	95.75 227	47.65 337	67.86 293	83.95 318

ACMH	75.40 17	77.99 265	74.96 275	87.10 235	90.67 220	76.41 250	93.19 254	91.64 286	72.47 297	63.44 305	87.61 232	43.34 323	97.16 164	58.34 300	73.94 246	87.72 275
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	73.68 18	77.99 265	75.74 267	84.74 265	90.45 223	72.02 282	86.41 320	91.12 292	72.57 296	66.63 289	87.27 235	54.95 292	96.98 172	56.29 314	75.98 236	85.21 307
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		77.96 267	75.61 268	84.98 259	86.66 267	79.01 189	93.02 257	90.94 297	75.69 259	63.19 306	77.62 320	67.11 188	92.07 307	70.05 237	56.24 326	83.87 319

our_test_3		77.90 268	75.37 272	85.48 254	85.39 301	76.74 247	93.63 236	91.67 284	73.39 289	65.72 296	84.65 284	58.20 261	93.13 297	57.82 302	67.87 292	86.57 295

v16		77.84 269	75.47 269	84.93 261	86.62 271	78.93 191	92.84 261	90.89 298	75.50 262	63.03 310	77.54 321	66.82 197	92.04 308	69.82 238	56.22 327	83.82 321

v17		77.79 270	75.41 271	84.94 260	86.53 276	78.94 190	92.83 262	90.88 299	75.51 261	62.97 311	77.50 322	66.69 198	92.03 309	69.80 239	56.01 328	83.83 320

RPSCF		77.73 271	76.63 261	81.06 309	88.66 250	55.76 341	87.77 309	87.88 326	64.82 321	74.14 246	92.79 167	49.22 307	96.81 181	67.47 257	76.88 235	90.62 214

v15		77.52 272	75.09 273	84.82 263	86.37 280	78.82 194	92.58 264	90.78 301	75.47 263	62.53 313	77.17 323	66.58 201	91.92 310	69.18 242	55.16 330	83.73 322

ACMH+	76.62 16	77.47 273	74.94 276	85.05 257	91.07 215	71.58 289	93.26 250	90.01 310	71.80 300	64.76 300	88.55 220	41.62 329	96.48 189	62.35 289	71.00 258	87.09 289

V14		77.43 274	74.99 274	84.75 264	86.32 283	78.67 198	92.44 268	90.77 302	75.28 267	62.42 314	77.13 324	66.40 202	91.88 311	69.01 247	55.14 331	83.70 323

Patchmtry		77.36 275	74.59 282	85.67 251	89.75 233	75.75 256	77.85 341	91.12 292	60.28 335	71.23 265	80.35 311	75.45 118	93.56 294	57.94 301	67.34 299	87.68 277

V9		77.32 276	74.87 277	84.69 267	86.26 287	78.52 204	92.33 271	90.72 303	75.11 270	62.21 316	77.08 326	66.19 204	91.87 312	68.84 248	55.06 333	83.69 324

v11		77.21 277	74.72 279	84.68 268	86.29 284	78.62 201	92.30 272	90.63 307	74.86 274	62.32 315	76.73 329	65.49 210	91.83 313	68.17 254	55.72 329	83.59 326

v12		77.20 278	74.73 278	84.63 271	86.15 290	78.41 209	92.17 273	90.71 304	74.92 273	62.05 318	77.00 327	65.83 206	91.83 313	68.69 250	55.01 334	83.64 325

ppachtmachnet_test		77.19 279	74.22 287	86.13 247	85.39 301	78.22 215	93.98 228	91.36 289	71.74 301	67.11 287	84.87 282	56.67 278	93.37 296	52.21 325	64.59 308	86.80 292

OurMVSNet-221017-0		77.18 280	76.06 264	80.55 311	83.78 317	60.00 333	90.35 289	91.05 295	77.01 248	66.62 290	87.92 230	47.73 313	94.03 285	71.63 224	68.44 286	87.62 278

v13		77.11 281	74.63 281	84.55 274	86.08 293	78.27 213	92.06 275	90.68 306	74.73 276	61.86 321	76.93 328	65.73 207	91.81 316	68.55 253	55.07 332	83.59 326

testing_2		76.96 282	73.18 292	88.30 205	75.87 342	79.64 167	89.92 292	94.21 212	80.16 204	51.23 341	75.94 331	33.94 342	95.81 223	82.28 141	75.12 243	89.46 234

TransMVSNet (Re)		76.94 283	74.38 285	84.62 273	85.92 295	75.25 260	95.28 190	89.18 317	73.88 285	67.22 285	86.46 256	59.64 245	94.10 284	59.24 299	52.57 340	84.50 313

EU-MVSNet		76.92 284	76.95 256	76.83 321	84.10 314	54.73 342	91.77 280	92.71 273	72.74 294	69.57 278	88.69 218	58.03 266	87.43 336	64.91 273	70.00 273	88.33 266

Patchmatch-RL test		76.65 285	74.01 289	84.55 274	77.37 337	64.23 321	78.49 340	82.84 350	78.48 232	64.63 301	73.40 337	76.05 110	91.70 318	76.99 184	57.84 322	97.72 87

FMVSNet5		76.46 286	74.16 288	83.35 295	90.05 230	76.17 252	89.58 294	89.85 311	71.39 304	65.29 299	80.42 310	50.61 301	87.70 335	61.05 292	69.24 282	86.18 300

SixPastTwentyTwo		76.04 287	74.32 286	81.22 308	84.54 309	61.43 331	91.16 285	89.30 316	77.89 234	64.04 302	86.31 261	48.23 308	94.29 281	63.54 285	63.84 312	87.93 273

AllTest		75.92 288	73.06 293	84.47 276	92.18 192	67.29 313	91.07 286	84.43 342	67.63 313	63.48 303	90.18 204	38.20 335	97.16 164	57.04 304	73.37 248	88.97 248

COLMAP_ROB	73.24 19	75.74 289	73.00 294	83.94 285	92.38 183	69.08 309	91.85 279	86.93 332	61.48 331	65.32 298	90.27 203	42.27 327	96.93 176	50.91 330	75.63 239	85.80 304
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	75.71 290	74.56 283	79.17 317	79.69 330	55.98 339	89.59 293	93.30 264	60.28 335	53.85 339	89.07 214	47.68 314	96.33 194	76.55 188	81.02 210	85.22 306
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		75.29 291	73.64 290	80.22 312	80.75 325	63.38 325	93.36 244	90.71 304	73.09 291	67.12 286	83.70 295	50.33 303	90.85 324	53.63 322	70.10 268	86.44 296

EG-PatchMatch MVS		74.92 292	72.02 296	83.62 291	83.76 318	73.28 272	93.62 237	92.04 280	68.57 312	58.88 329	83.80 291	31.87 346	95.57 245	56.97 306	78.67 225	82.00 338

testgi		74.88 293	73.40 291	79.32 316	80.13 329	61.75 329	93.21 252	86.64 334	79.49 218	66.56 291	91.06 188	35.51 340	88.67 333	56.79 307	71.25 256	87.56 281

pmmvs6		74.65 294	71.67 297	83.60 292	79.13 332	69.94 302	93.31 249	90.88 299	61.05 334	65.83 294	84.15 288	43.43 322	94.83 270	66.62 262	60.63 318	86.02 303

test2356		74.41 295	74.53 284	74.07 328	76.13 341	54.45 343	94.74 213	92.08 278	71.96 299	65.51 297	83.05 301	56.96 274	83.71 346	52.74 324	77.58 233	84.06 316

K. test v3		73.62 296	71.59 298	79.69 314	82.98 320	59.85 334	90.85 288	88.83 319	77.13 244	58.90 328	82.11 302	43.62 321	91.72 317	65.83 269	54.10 337	87.50 284

pmmvs-eth3d		73.59 297	70.66 301	82.38 302	76.40 339	73.38 270	89.39 298	89.43 314	72.69 295	60.34 326	77.79 319	46.43 317	91.26 322	66.42 266	57.06 323	82.51 333

MDA-MVSNet_test_wron		73.54 298	70.43 304	82.86 297	84.55 308	71.85 283	91.74 281	91.32 291	67.63 313	46.73 346	81.09 308	55.11 290	90.42 328	55.91 316	59.76 320	86.31 298

YYNet1		73.53 299	70.43 304	82.85 298	84.52 310	71.73 287	91.69 282	91.37 288	67.63 313	46.79 345	81.21 307	55.04 291	90.43 327	55.93 315	59.70 321	86.38 297

UnsupCasMVSNet_eth		73.25 300	70.57 302	81.30 307	77.53 335	66.33 317	87.24 312	93.89 234	80.38 198	57.90 334	81.59 305	42.91 326	90.56 326	65.18 272	48.51 344	87.01 290

DSMNet-mixed		73.13 301	72.45 295	75.19 326	77.51 336	46.82 349	85.09 326	82.01 351	67.61 317	69.27 281	81.33 306	50.89 299	86.28 339	54.54 319	83.80 187	92.46 204

OpenMVS_ROB	68.52 20	73.02 302	69.57 306	83.37 294	80.54 328	71.82 284	93.60 238	88.22 324	62.37 326	61.98 319	83.15 300	35.31 341	95.47 249	45.08 340	75.88 237	82.82 330

test_0402		72.68 303	69.54 307	82.09 305	88.67 249	71.81 285	92.72 263	86.77 333	61.52 330	62.21 316	83.91 289	43.22 324	93.76 291	34.60 349	72.23 255	80.72 340

TinyColmap		72.41 304	68.99 309	82.68 299	88.11 254	69.59 306	88.41 304	85.20 339	65.55 319	57.91 333	84.82 283	30.80 348	95.94 216	51.38 326	68.70 284	82.49 335

test20.03		72.36 305	71.15 299	75.98 325	77.79 334	59.16 336	92.40 269	89.35 315	74.09 283	61.50 322	84.32 286	48.09 309	85.54 344	50.63 331	62.15 316	83.24 328

LF4IMVS		72.36 305	70.82 300	76.95 320	79.18 331	56.33 338	86.12 321	86.11 336	69.30 311	63.06 309	86.66 251	33.03 344	92.25 303	65.33 271	68.64 285	82.28 336

MDA-MVSNet-bldmvs		71.45 307	67.94 310	81.98 306	85.33 303	68.50 311	92.35 270	88.76 321	70.40 306	42.99 347	81.96 303	46.57 316	91.31 321	48.75 336	54.39 336	86.11 301

MVS-HIRNet		71.36 308	67.00 311	84.46 278	90.58 221	69.74 305	79.15 337	87.74 327	46.09 350	61.96 320	50.50 351	45.14 319	95.64 239	53.74 321	88.11 148	88.00 272

testpf		70.88 309	70.47 303	72.08 330	88.92 245	59.57 335	48.62 358	93.15 267	63.05 323	63.07 308	79.51 316	58.33 260	86.63 338	66.93 260	72.69 253	70.05 349

testus		70.06 310	69.09 308	72.98 329	74.54 344	51.28 347	93.78 232	87.34 328	71.49 303	62.69 312	83.46 297	24.44 351	84.77 345	51.22 329	72.85 252	82.90 329

MIMVSNet1		69.44 311	66.65 313	77.84 318	76.48 338	62.84 327	87.42 310	88.97 318	66.96 318	57.75 335	79.72 315	32.77 345	85.83 341	46.32 338	63.42 313	84.85 311

PM-MVS		69.32 312	66.93 312	76.49 322	73.60 345	55.84 340	85.91 322	79.32 356	74.72 277	61.09 323	78.18 318	21.76 352	91.10 323	70.86 233	56.90 324	82.51 333

TDRefinement		69.20 313	65.78 315	79.48 315	66.04 352	62.21 328	88.21 305	86.12 335	62.92 324	61.03 324	85.61 270	33.23 343	94.16 283	55.82 317	53.02 338	82.08 337

new-patchmatchnet		68.85 314	65.93 314	77.61 319	73.57 346	63.94 324	90.11 291	88.73 322	71.62 302	55.08 337	73.60 334	40.84 332	87.22 337	51.35 328	48.49 345	81.67 339

UnsupCasMVSNet_bld		68.60 315	64.50 316	80.92 310	74.63 343	67.80 312	83.97 327	92.94 270	65.12 320	54.63 338	68.23 345	35.97 338	92.17 306	60.13 294	44.83 347	82.78 331

LP		68.54 316	63.92 318	82.39 301	87.93 256	71.79 286	72.37 351	86.01 338	55.89 344	58.33 332	71.46 342	49.58 306	90.10 331	32.25 351	61.48 317	85.27 305

new_pmnet		66.18 317	63.18 319	75.18 327	76.27 340	61.74 330	83.79 328	84.66 341	56.64 343	51.57 340	71.85 341	31.29 347	87.93 334	49.98 332	62.55 315	75.86 344

pmmvs3		65.75 318	62.18 321	76.45 323	67.12 350	64.54 320	88.68 302	85.05 340	54.77 348	57.54 336	73.79 333	29.40 350	86.21 340	55.49 318	47.77 346	78.62 341

1111		65.60 319	64.33 317	69.41 332	68.26 347	45.11 352	87.06 313	87.32 329	54.99 345	51.20 342	73.45 335	63.57 223	85.70 342	36.53 347	56.59 325	77.42 343

test1235678		64.50 320	62.19 320	71.42 331	66.82 351	48.00 348	89.44 296	87.90 325	62.82 325	49.12 344	71.31 343	30.14 349	82.19 348	41.88 343	60.32 319	84.06 316

N_pmnet		61.30 321	60.20 322	64.60 337	84.32 311	17.00 367	91.67 283	10.98 368	61.77 329	58.45 331	78.55 317	49.89 304	91.83 313	42.27 342	63.94 311	84.97 310

test12356		58.24 322	56.06 324	64.77 335	60.65 353	39.42 358	82.78 331	84.34 344	57.47 342	42.65 348	69.10 344	19.21 353	81.18 349	38.97 346	49.40 341	73.69 345

FPMVS		55.09 323	52.93 325	61.57 340	55.98 354	40.51 357	83.11 330	83.41 349	37.61 352	34.95 352	71.95 340	14.40 358	76.95 352	29.81 353	65.16 307	67.25 351

.test1245		54.61 324	58.07 323	44.24 347	68.26 347	45.11 352	87.06 313	87.32 329	54.99 345	51.20 342	73.45 335	63.57 223	85.70 342	36.53 347	0.21 362	1.91 362

testmv		54.58 325	51.53 327	63.74 339	53.58 358	40.82 356	83.26 329	83.92 346	54.07 349	36.35 351	61.26 346	14.80 357	77.07 351	33.00 350	43.53 350	73.33 346

LCM-MVSNet		52.52 326	48.24 328	65.35 334	47.63 362	41.45 355	72.55 350	83.62 348	31.75 353	37.66 350	57.92 349	9.19 364	76.76 353	49.26 334	44.60 348	77.84 342

no-one		51.12 327	45.81 330	67.03 333	53.16 360	52.22 344	75.21 345	80.40 353	54.89 347	28.26 355	48.50 353	15.54 356	82.81 347	39.29 345	17.06 355	66.07 352

PMMVS2		50.90 328	46.31 329	64.67 336	55.53 355	46.67 350	77.30 343	71.02 358	40.89 351	34.16 353	59.32 347	9.83 363	76.14 355	40.09 344	28.63 352	71.21 347

ANet_high		46.22 329	41.28 333	61.04 341	39.91 365	46.25 351	70.59 352	76.18 357	58.87 340	23.09 357	48.00 354	12.58 360	66.54 358	28.65 354	13.62 358	70.35 348

Gipuma		45.11 330	42.05 331	54.30 343	80.69 326	51.30 346	35.80 359	83.81 347	28.13 355	27.94 356	34.53 357	11.41 362	76.70 354	21.45 356	54.65 335	34.90 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		41.54 331	41.93 332	40.38 348	20.10 367	26.84 363	61.93 354	59.09 364	14.81 361	28.51 354	80.58 309	35.53 339	48.33 363	63.70 284	13.11 359	45.96 357

PNet_i23d		41.20 332	38.13 334	50.41 344	55.23 356	36.24 361	73.80 349	65.45 363	29.75 354	21.36 358	47.05 355	3.43 365	63.23 359	28.17 355	18.92 354	51.76 354

v1.0		39.63 333	52.84 326	0.00 355	98.90 7	0.00 370	0.00 361	96.77 54	84.95 105	96.07 9	98.83 3	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

PMVS	34.80 23	39.19 334	35.53 335	50.18 345	29.72 366	30.30 362	59.60 356	66.20 362	26.06 356	17.91 360	49.53 352	3.12 366	74.09 356	18.19 358	49.40 341	46.14 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		37.75 335	31.85 338	55.46 342	40.00 364	38.01 359	59.81 355	69.47 359	25.46 357	12.42 363	30.55 361	2.06 368	67.08 357	31.81 352	15.03 356	61.29 353

pcd1.5k->3k		34.11 336	35.46 336	30.05 351	86.70 266	0.00 370	0.00 361	94.74 187	0.00 365	0.00 367	0.00 367	58.13 262	0.00 367	0.00 364	79.56 219	90.14 222

MVE	35.65 22	33.85 337	29.49 340	46.92 346	41.86 363	36.28 360	50.45 357	56.52 365	18.75 360	18.28 359	37.84 356	2.41 367	58.41 360	18.71 357	20.62 353	46.06 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		32.70 338	32.39 337	33.65 349	53.35 359	25.70 364	74.07 347	53.33 366	21.08 358	17.17 361	33.63 359	11.85 361	54.84 361	12.98 359	14.04 357	20.42 359

EMVS		31.70 339	31.45 339	32.48 350	50.72 361	23.95 365	74.78 346	52.30 367	20.36 359	16.08 362	31.48 360	12.80 359	53.60 362	11.39 360	13.10 360	19.88 360

cdsmvs_eth3d_5k		21.43 340	28.57 341	0.00 355	0.00 370	0.00 370	0.00 361	95.93 130	0.00 365	0.00 367	97.66 51	63.57 223	0.00 367	0.00 364	0.00 365	0.00 365

wuyk23d		14.10 341	13.89 342	14.72 352	55.23 356	22.91 366	33.83 360	3.56 369	4.94 362	4.11 364	2.28 366	2.06 368	19.66 364	10.23 361	8.74 361	1.59 364

testmvs		9.92 342	12.94 343	0.84 354	0.65 368	0.29 369	93.78 232	0.39 370	0.42 363	2.85 365	15.84 364	0.17 371	0.30 366	2.18 362	0.21 362	1.91 362

test123		9.07 343	11.73 344	1.11 353	0.50 369	0.77 368	89.44 296	0.20 371	0.34 364	2.15 366	10.72 365	0.34 370	0.32 365	1.79 363	0.08 364	2.23 361

ab-mvs-re		8.11 344	10.81 345	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	97.30 71	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

pcd_1.5k_mvsjas		5.92 345	7.89 346	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	71.04 166	0.00 367	0.00 364	0.00 365	0.00 365

sosnet-low-res		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sosnet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uncertanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

Regformer		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

GSMVS																97.54 98

test_part2					98.90 7	85.14 45				96.07 9

test_part1				0.00 355		0.00 370	0.00 361	96.77 54				0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sam_mvs1												77.59 87				97.54 98

sam_mvs												75.35 131

semantic-postprocess				84.73 266	89.63 237	74.66 262		91.81 283	80.05 207	71.06 268	85.18 277	57.98 267	91.40 319	72.48 218	70.70 262	88.12 270

ambc				76.02 324	68.11 349	51.43 345	64.97 353	89.59 312		60.49 325	74.49 332	17.17 355	92.46 300	61.50 290	52.85 339	84.17 315

MTGPA								96.33 105

test_post1							85.88 323				30.24 362	73.77 144	95.07 265	73.89 211

test_post											33.80 358	76.17 108	95.97 211

patchmatchnet-post											77.09 325	77.78 86	95.39 251

GG-mvs-BLEND				93.49 56	94.94 126	86.26 25	81.62 332	97.00 39		88.32 97	94.30 141	91.23 2	96.21 200	88.49 87	97.43 56	98.00 69

MTMP							97.53 58	68.16 360

gm-plane-assit					92.27 188	79.64 167			84.47 119		95.15 125		97.93 120	85.81 107

test9_res														96.00 13	99.03 9	98.31 43

TEST9					98.64 22	83.71 71	97.82 36	96.65 69	84.29 126	95.16 16	98.09 29	84.39 20	99.36 55

test_8					98.63 24	83.64 74	97.81 38	96.63 75	84.50 117	95.10 18	98.11 28	84.33 21	99.23 61

agg_prior2														94.30 27	99.00 11	98.57 31

agg_prior					98.59 27	83.13 82		96.56 80		94.19 31			99.16 74

TestCases				84.47 276	92.18 192	67.29 313		84.43 342	67.63 313	63.48 303	90.18 204	38.20 335	97.16 164	57.04 304	73.37 248	88.97 248

test_prior4						82.34 96	97.75 46

test_prior2							98.37 18		86.08 77	94.57 27	98.02 34	83.14 32		95.05 20	98.79 15

test_prior				93.09 72	98.68 16	81.91 104		96.40 97					99.06 81			98.29 45

旧先验2							96.97 107		74.06 284	96.10 8			97.76 132	88.38 89

新几何2							96.42 145

新几何1				93.12 70	97.44 65	81.60 116		96.71 63	74.54 280	91.22 66	97.57 57	79.13 68	99.51 46	77.40 179	98.46 27	98.26 48

旧先验1					97.39 68	79.58 169		96.54 83			98.08 32	84.00 25			97.42 57	97.62 95

无先验							96.87 112	96.78 53	77.39 240				99.52 43	79.95 154		98.43 37

原ACMM2							96.84 113

原ACMM1				91.22 137	97.77 56	78.10 221		96.61 76	81.05 180	91.28 64	97.42 67	77.92 84	98.98 86	79.85 156	98.51 25	96.59 137

test222					96.15 87	78.41 209	95.87 174	96.46 90	71.97 298	89.66 81	97.45 63	76.33 106			98.24 38	98.30 44

testdata2													99.48 48	76.45 190

segment_acmp												82.69 35

testdata				90.13 166	95.92 95	74.17 267		96.49 89	73.49 288	94.82 24	97.99 37	78.80 73	97.93 120	83.53 132	97.52 52	98.29 45

testdata1							95.57 184		87.44 59

test12				94.25 27	98.34 38	85.55 36		96.35 103		92.36 48		80.84 48	99.22 63		98.31 36	97.98 71

plane_prior7					91.86 205	77.55 234

plane_prior6					91.98 200	77.92 227						64.77 218

plane_prior5								94.69 188					97.30 156	87.08 99	82.82 204	90.96 211

plane_prior4											94.15 144

plane_prior3						77.75 231			90.17 28	81.33 170

plane_prior2							97.18 81		89.89 30

plane_prior1					91.95 203

plane_prior						77.96 224	97.52 60		90.36 27						82.96 196

n2								0.00 372

nn								0.00 372

door-mid								79.75 355

lessismore_v0				79.98 313	80.59 327	58.34 337		80.87 352		58.49 330	83.46 297	43.10 325	93.89 287	63.11 287	48.68 343	87.72 275

LGP-MVS_train				86.33 241	90.88 217	73.06 274		94.13 221	82.20 164	76.31 227	93.20 162	54.83 293	96.95 173	83.72 126	80.83 211	88.98 246

test11								96.50 87

door								80.13 354

HQP5-MVS						78.48 205

HQP-NCC					92.08 195		97.63 51		90.52 24	82.30 154

ACMP_Plane					92.08 195		97.63 51		90.52 24	82.30 154

BP-MVS														87.67 96

HQP4-MVS										82.30 154			97.32 154			91.13 209

HQP3-MVS								94.80 183							83.01 194

HQP2-MVS												65.40 213

NP-MVS					92.04 199	78.22 215					94.56 137

MDTV_nov1_ep13_2view						81.74 111	86.80 316		80.65 191	85.65 117		74.26 142		76.52 189		96.98 123

MDTV_nov1_ep13			83.69 179		94.09 152	81.01 125	86.78 317	96.09 120	83.81 138	84.75 125	84.32 286	74.44 141	96.54 187	63.88 282	85.07 180

ACMMP++_ref															78.45 229

ACMMP++															79.05 222

Test By Simon												71.65 159

ITE_SJBPF				82.38 302	87.00 264	65.59 318		89.55 313	79.99 209	69.37 280	91.30 184	41.60 330	95.33 255	62.86 288	74.63 245	86.24 299

DeepMVS_CX				64.06 338	78.53 333	43.26 354		68.11 361	69.94 308	38.55 349	76.14 330	18.53 354	79.34 350	43.72 341	41.62 351	69.57 350

High-resolution multi-view benchmark