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.

SMA-MVS		94.64 11	94.66 11	94.58 21	98.02 48	85.42 38	97.47 61	96.74 57	85.49 88	98.01 1	98.70 5	82.85 35	99.84 12	95.79 17	98.92 14	98.49 35

TSAR-MVS + MP.		94.79 10	95.17 8	93.64 45	97.66 56	84.10 62	95.85 173	96.42 92	91.26 17	97.49 2	96.80 89	86.50 11	98.49 104	95.54 20	99.03 7	98.33 40

HSP-MVS		95.55 4	96.51 2	92.66 85	98.31 39	80.10 147	97.42 68	96.46 88	92.20 13	97.11 3	98.29 14	93.46 1	99.10 80	96.01 13	99.30 2	98.77 22

CNVR-MVS		96.30 1	96.54 1	95.55 10	99.31 5	87.69 17	99.06 5	97.12 25	94.66 3	96.79 4	98.78 4	86.42 12	99.95 2	97.59 4	99.18 3	99.00 14

SD-MVS		94.84 9	95.02 9	94.29 26	97.87 54	84.61 53	97.76 45	96.19 113	89.59 32	96.66 5	98.17 22	84.33 22	99.60 37	96.09 12	98.50 27	98.66 27

PS-MVSNAJ		94.17 19	93.52 29	96.10 4	95.65 102	92.35 1	98.21 23	95.79 134	92.42 12	96.24 6	98.18 18	71.04 162	99.17 74	96.77 9	97.39 59	96.79 126

旧先验2							96.97 105		74.06 280	96.10 7			97.76 130	88.38 87

test_part2					98.90 7	85.14 44				96.07 8

ESAPD		95.32 5	95.52 6	94.70 19	98.90 7	85.14 44	98.15 25	96.77 53	84.95 102	96.07 8	98.83 2	89.33 6	99.80 14	97.78 2	98.95 12	99.18 10

xiu_mvs_v2_base		93.92 26	93.26 31	95.91 6	95.07 118	92.02 2	98.19 24	95.68 138	92.06 14	96.01 10	98.14 23	70.83 165	98.96 88	96.74 10	96.57 73	96.76 129

HPM-MVS++		95.32 5	95.48 7	94.85 16	98.62 24	86.04 27	97.81 39	96.93 45	92.45 11	95.69 11	98.50 9	85.38 14	99.85 10	94.75 24	99.18 3	98.65 28

NCCC		95.63 3	95.94 5	94.69 20	99.21 6	85.15 43	99.16 3	96.96 42	94.11 6	95.59 12	98.64 7	85.07 15	99.91 3	95.61 19	99.10 5	99.00 14

EPNet		94.06 23	94.15 22	93.76 39	97.27 72	84.35 58	98.29 20	97.64 17	94.57 4	95.36 13	96.88 85	79.96 61	99.12 79	91.30 57	96.11 77	97.82 78
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		94.89 8	94.64 12	95.63 8	97.55 61	88.12 11	99.06 5	96.39 98	94.07 7	95.34 14	97.80 48	76.83 97	99.87 8	97.08 7	97.64 52	98.89 17

TEST9					98.64 21	83.71 69	97.82 37	96.65 67	84.29 122	95.16 15	98.09 29	84.39 21	99.36 56

train_agg		94.28 16	94.45 15	93.74 40	98.64 21	83.71 69	97.82 37	96.65 67	84.50 114	95.16 15	98.09 29	84.33 22	99.36 56	95.91 15	98.96 10	98.16 50

test_8					98.63 23	83.64 72	97.81 39	96.63 73	84.50 114	95.10 17	98.11 28	84.33 22	99.23 62

DeepPCF-MVS	89.82 1	94.61 12	96.17 3	89.91 170	97.09 75	70.21 294	98.99 8	96.69 64	95.57 1	95.08 18	99.23 1	86.40 13	99.87 8	97.84 1	98.66 23	99.65 1

agg_prior3		94.10 21	94.29 21	93.53 53	98.62 24	83.03 83	97.80 41	96.64 70	84.28 123	95.01 19	98.03 33	83.40 31	99.41 53	95.91 15	98.96 10	98.16 50

APDe-MVS		94.56 13	94.75 10	93.96 34	98.84 11	83.40 76	98.04 30	96.41 93	85.79 80	95.00 20	98.28 15	84.32 25	99.18 73	97.35 5	98.77 18	99.28 5

MVSFormer		91.36 66	90.57 68	93.73 42	93.00 166	88.08 12	94.80 205	94.48 195	80.74 184	94.90 21	97.13 77	78.84 71	95.10 257	83.77 120	97.46 54	98.02 61

lupinMVS		93.87 28	93.58 28	94.75 18	93.00 166	88.08 12	99.15 4	95.50 147	91.03 19	94.90 21	97.66 51	78.84 71	97.56 138	94.64 27	97.46 54	98.62 30

MVS_0304		93.82 29	93.11 35	95.95 5	96.79 77	89.15 7	98.56 15	95.30 159	93.61 9	94.82 23	98.02 34	66.60 197	99.88 7	96.94 8	97.39 59	98.81 20

testdata				90.13 160	95.92 92	74.17 260		96.49 87	73.49 284	94.82 23	97.99 37	78.80 73	97.93 118	83.53 128	97.52 53	98.29 44

APD-MVS		93.61 30	93.59 27	93.69 43	98.76 12	83.26 77	97.21 75	96.09 118	82.41 158	94.65 25	98.21 17	81.96 40	98.81 96	94.65 26	98.36 36	99.01 13
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_prior3		94.03 24	94.34 18	93.09 69	98.68 15	81.91 102	98.37 18	96.40 95	86.08 75	94.57 26	98.02 34	83.14 33	99.06 82	95.05 21	98.79 16	98.29 44

test_prior2							98.37 18		86.08 75	94.57 26	98.02 34	83.14 33		95.05 21	98.79 16

ACMMP_Plus		93.46 32	93.23 32	94.17 29	97.16 73	84.28 61	96.82 112	96.65 67	86.24 72	94.27 28	97.99 37	77.94 83	99.83 13	93.39 36	98.57 25	98.39 38

agg_prior1		94.10 21	94.31 19	93.48 56	98.59 26	83.13 79	97.77 42	96.56 78	84.38 118	94.19 29	98.13 24	84.66 19	99.16 75	95.74 18	98.74 20	98.15 52

agg_prior					98.59 26	83.13 79		96.56 78		94.19 29			99.16 75

SteuartSystems-ACMMP		94.13 20	94.44 16	93.20 64	95.41 108	81.35 118	99.02 7	96.59 76	89.50 33	94.18 31	98.36 13	83.68 30	99.45 51	94.77 23	98.45 29	98.81 20
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		93.59 31	93.63 26	93.48 56	98.05 47	81.76 108	98.64 13	97.13 24	82.60 156	94.09 32	98.49 10	80.35 53	99.85 10	94.74 25	98.62 24	98.83 19

TSAR-MVS + GP.		94.35 15	94.50 13	93.89 35	97.38 69	83.04 82	98.10 28	95.29 160	91.57 15	93.81 33	97.45 63	86.64 9	99.43 52	96.28 11	94.01 97	99.20 8

CANet_DTU		90.98 71	90.04 73	93.83 37	94.76 124	86.23 26	96.32 147	93.12 261	93.11 10	93.71 34	96.82 88	63.08 225	99.48 49	84.29 115	95.12 90	95.77 151

VNet		92.11 54	91.22 61	94.79 17	96.91 76	86.98 22	97.91 33	97.96 15	86.38 71	93.65 35	95.74 102	70.16 170	98.95 90	93.39 36	88.87 137	98.43 36

xiu_mvs_v1_base_debu		90.54 77	89.54 84	93.55 50	92.31 176	87.58 18	96.99 100	94.87 173	87.23 62	93.27 36	97.56 58	57.43 267	98.32 108	92.72 45	93.46 105	94.74 173

xiu_mvs_v1_base		90.54 77	89.54 84	93.55 50	92.31 176	87.58 18	96.99 100	94.87 173	87.23 62	93.27 36	97.56 58	57.43 267	98.32 108	92.72 45	93.46 105	94.74 173

xiu_mvs_v1_base_debi		90.54 77	89.54 84	93.55 50	92.31 176	87.58 18	96.99 100	94.87 173	87.23 62	93.27 36	97.56 58	57.43 267	98.32 108	92.72 45	93.46 105	94.74 173

CDPH-MVS		93.12 36	92.91 38	93.74 40	98.65 20	83.88 64	97.67 50	96.26 107	83.00 148	93.22 39	98.24 16	81.31 47	99.21 66	89.12 79	98.74 20	98.14 53

MSLP-MVS++		94.28 16	94.39 17	93.97 33	98.30 40	84.06 63	98.64 13	96.93 45	90.71 22	93.08 40	98.70 5	79.98 60	99.21 66	94.12 31	99.07 6	98.63 29

alignmvs		92.97 39	92.26 49	95.12 13	95.54 104	87.77 15	98.67 11	96.38 99	88.04 50	93.01 41	97.45 63	79.20 68	98.60 98	93.25 41	88.76 138	98.99 16

canonicalmvs		92.27 53	91.22 61	95.41 11	95.80 99	88.31 9	97.09 94	94.64 190	88.49 43	92.99 42	97.31 69	72.68 147	98.57 100	93.38 38	88.58 141	99.36 4

jason		92.73 44	92.23 50	94.21 28	90.50 214	87.30 21	98.65 12	95.09 164	90.61 23	92.76 43	97.13 77	75.28 130	97.30 151	93.32 39	96.75 72	98.02 61
jason: jason.
Regformer-1		94.00 25	94.04 23	93.87 36	98.41 34	84.29 60	97.43 66	97.04 36	89.50 33	92.75 44	98.13 24	82.60 37	99.26 61	93.55 34	96.99 65	98.06 58

Regformer-2		93.92 26	94.01 24	93.67 44	98.41 34	83.75 68	97.43 66	97.00 38	89.43 35	92.69 45	98.13 24	82.48 38	99.22 64	93.51 35	96.99 65	98.04 59

test12				94.25 27	98.34 37	85.55 35		96.35 101		92.36 46		80.84 49	99.22 64		98.31 37	97.98 68

MG-MVS		94.25 18	93.72 25	95.85 7	99.38 3	89.35 6	97.98 32	98.09 14	89.99 29	92.34 47	96.97 82	81.30 48	98.99 86	88.54 83	98.88 15	99.20 8

MCST-MVS		96.17 2	96.12 4	96.32 3	99.42 2	89.36 5	98.94 9	97.10 32	95.17 2	92.11 48	98.46 11	87.33 8	99.97 1	97.21 6	99.31 1	99.63 2

region2R		92.72 45	92.70 42	92.79 79	98.68 15	80.53 136	97.53 58	96.51 83	85.22 93	91.94 49	97.98 39	77.26 90	99.67 32	90.83 63	98.37 35	98.18 49

Effi-MVS+		90.70 73	89.90 78	93.09 69	93.61 155	83.48 74	95.20 189	92.79 265	83.22 143	91.82 50	95.70 104	71.82 153	97.48 145	91.25 58	93.67 102	98.32 41

HFP-MVS		92.89 40	92.86 39	92.98 72	98.71 13	81.12 121	97.58 54	96.70 62	85.20 95	91.75 51	97.97 41	78.47 76	99.71 24	90.95 60	98.41 31	98.12 55

#test#		92.99 38	92.99 36	92.98 72	98.71 13	81.12 121	97.77 42	96.70 62	85.75 81	91.75 51	97.97 41	78.47 76	99.71 24	91.36 56	98.41 31	98.12 55

DeepC-MVS_fast	89.06 2	94.48 14	94.30 20	95.02 14	98.86 10	85.68 33	98.06 29	96.64 70	93.64 8	91.74 53	98.54 8	80.17 58	99.90 4	92.28 51	98.75 19	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
ACMMPR		92.69 46	92.67 43	92.75 81	98.66 18	80.57 133	97.58 54	96.69 64	85.20 95	91.57 54	97.92 43	77.01 94	99.67 32	90.95 60	98.41 31	98.00 66

DELS-MVS		94.98 7	94.49 14	96.44 2	96.42 80	90.59 3	99.21 2	97.02 37	94.40 5	91.46 55	97.08 79	83.32 32	99.69 28	92.83 44	98.70 22	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
XVS		92.69 46	92.71 40	92.63 88	98.52 29	80.29 140	97.37 70	96.44 90	87.04 67	91.38 56	97.83 47	77.24 92	99.59 38	90.46 66	98.07 43	98.02 61

X-MVStestdata		86.26 155	84.14 172	92.63 88	98.52 29	80.29 140	97.37 70	96.44 90	87.04 67	91.38 56	20.73 357	77.24 92	99.59 38	90.46 66	98.07 43	98.02 61

1121		90.66 74	89.82 80	93.16 66	97.39 66	81.71 112	93.33 238	96.66 66	74.45 277	91.38 56	97.55 61	79.27 65	99.52 44	79.95 150	98.43 30	98.26 47

PMMVS		89.46 93	89.92 77	88.06 203	94.64 125	69.57 300	96.22 154	94.95 170	87.27 61	91.37 59	96.54 94	65.88 202	97.39 147	88.54 83	93.89 99	97.23 114

Regformer-3		93.19 34	93.19 33	93.19 65	98.10 45	83.01 84	97.08 96	96.98 40	88.98 37	91.35 60	97.89 44	80.80 50	99.23 62	92.30 50	95.20 87	97.32 106

原ACMM1				91.22 132	97.77 55	78.10 215		96.61 74	81.05 176	91.28 61	97.42 67	77.92 84	98.98 87	79.85 152	98.51 26	96.59 133

Regformer-4		93.06 37	93.12 34	92.89 75	98.10 45	82.20 97	97.08 96	96.92 47	88.87 39	91.23 62	97.89 44	80.57 52	99.19 71	92.21 52	95.20 87	97.29 111

新几何1				93.12 67	97.44 64	81.60 114		96.71 61	74.54 276	91.22 63	97.57 57	79.13 69	99.51 47	77.40 174	98.46 28	98.26 47

UA-Net		88.92 102	88.48 97	90.24 153	94.06 146	77.18 236	93.04 248	94.66 187	87.39 60	91.09 64	93.89 147	74.92 134	98.18 115	75.83 190	91.43 124	95.35 161

APD-MVS_3200maxsize		91.23 69	91.35 60	90.89 141	97.89 52	76.35 244	96.30 151	95.52 146	79.82 207	91.03 65	97.88 46	74.70 136	98.54 101	92.11 54	96.89 69	97.77 81

PGM-MVS		91.93 56	91.80 54	92.32 98	98.27 41	79.74 154	95.28 186	97.27 20	83.83 133	90.89 66	97.78 49	76.12 108	99.56 42	88.82 81	97.93 49	97.66 87

MP-MVS		92.61 49	92.67 43	92.42 93	98.13 44	79.73 155	97.33 72	96.20 111	85.63 83	90.53 67	97.66 51	78.14 81	99.70 27	92.12 53	98.30 38	97.85 76
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HY-MVS	84.06 6	91.63 61	90.37 69	95.39 12	96.12 85	88.25 10	90.22 283	97.58 18	88.33 46	90.50 68	91.96 171	79.26 66	99.06 82	90.29 68	89.07 135	98.88 18

CP-MVS		92.54 51	92.60 45	92.34 96	98.50 31	79.90 150	98.40 17	96.40 95	84.75 107	90.48 69	98.09 29	77.40 89	99.21 66	91.15 59	98.23 40	97.92 72

MVS_Test		90.29 83	89.18 89	93.62 47	95.23 112	84.93 47	94.41 211	94.66 187	84.31 120	90.37 70	91.02 184	75.13 131	97.82 127	83.11 133	94.42 93	98.12 55

zzz-MVS		92.74 42	92.71 40	92.86 76	97.90 50	80.85 127	96.47 132	96.33 103	87.92 52	90.20 71	98.18 18	76.71 100	99.76 16	92.57 48	98.09 41	97.96 69

MTAPA		92.45 52	92.31 48	92.86 76	97.90 50	80.85 127	92.88 251	96.33 103	87.92 52	90.20 71	98.18 18	76.71 100	99.76 16	92.57 48	98.09 41	97.96 69

WTY-MVS		92.65 48	91.68 56	95.56 9	96.00 90	88.90 8	98.23 22	97.65 16	88.57 40	89.82 73	97.22 74	79.29 64	99.06 82	89.57 75	88.73 139	98.73 25

MVS_111021_HR		93.41 33	93.39 30	93.47 59	97.34 70	82.83 87	97.56 56	98.27 12	89.16 36	89.71 74	97.14 76	79.77 62	99.56 42	93.65 33	97.94 47	98.02 61

sss		90.87 72	89.96 75	93.60 48	94.15 143	83.84 67	97.14 85	98.13 13	85.93 78	89.68 75	96.09 98	71.67 154	99.30 58	87.69 93	89.16 134	97.66 87

test222					96.15 84	78.41 205	95.87 171	96.46 88	71.97 294	89.66 76	97.45 63	76.33 105			98.24 39	98.30 43

LFMVS		89.27 96	87.64 108	94.16 31	97.16 73	85.52 36	97.18 79	94.66 187	79.17 219	89.63 77	96.57 93	55.35 285	98.22 112	89.52 77	89.54 132	98.74 23

CostFormer		89.08 98	88.39 98	91.15 133	93.13 164	79.15 176	88.61 296	96.11 117	83.14 144	89.58 78	86.93 237	83.83 29	96.87 173	88.22 89	85.92 162	97.42 102

PVSNet_BlendedMVS		90.05 86	89.96 75	90.33 151	97.47 62	83.86 65	98.02 31	96.73 58	87.98 51	89.53 79	89.61 204	76.42 102	99.57 40	94.29 29	79.59 213	87.57 274

PVSNet_Blended		93.13 35	92.98 37	93.57 49	97.47 62	83.86 65	99.32 1	96.73 58	91.02 20	89.53 79	96.21 97	76.42 102	99.57 40	94.29 29	95.81 84	97.29 111

HPM-MVS		91.62 62	91.53 59	91.89 112	97.88 53	79.22 173	96.99 100	95.73 136	82.07 162	89.50 81	97.19 75	75.59 113	98.93 93	90.91 62	97.94 47	97.54 94
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
abl_6		89.80 88	89.71 83	90.07 161	96.53 79	75.52 250	94.48 208	95.04 167	81.12 175	89.22 82	97.00 81	68.83 174	98.96 88	89.86 71	95.27 86	95.73 152

EI-MVSNet-Vis-set		91.84 58	91.77 55	92.04 108	97.60 58	81.17 120	96.61 126	96.87 49	88.20 48	89.19 83	97.55 61	78.69 75	99.14 77	90.29 68	90.94 127	95.80 150

MP-MVS-pluss		92.58 50	92.35 47	93.29 61	97.30 71	82.53 91	96.44 137	96.04 122	84.68 109	89.12 84	98.37 12	77.48 88	99.74 21	93.31 40	98.38 34	97.59 93
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDD-MVS		88.28 119	87.02 125	92.06 107	95.09 116	80.18 146	97.55 57	94.45 198	83.09 145	89.10 85	95.92 101	47.97 308	98.49 104	93.08 43	86.91 151	97.52 97

diffmvs		87.96 127	86.47 133	92.42 93	94.26 140	82.70 88	92.79 255	94.03 221	77.94 229	88.99 86	89.98 201	70.72 166	97.56 138	77.75 164	91.80 122	96.98 118

HPM-MVS_fast		90.38 82	90.17 72	91.03 136	97.61 57	77.35 232	97.15 84	95.48 148	79.51 212	88.79 87	96.90 83	71.64 156	98.81 96	87.01 100	97.44 56	96.94 120

PAPM		92.87 41	92.40 46	94.30 25	92.25 183	87.85 14	96.40 143	96.38 99	91.07 18	88.72 88	96.90 83	82.11 39	97.37 148	90.05 70	97.70 51	97.67 86

MVS_111021_LR		91.60 63	91.64 58	91.47 125	95.74 100	78.79 193	96.15 156	96.77 53	88.49 43	88.64 89	97.07 80	72.33 150	99.19 71	93.13 42	96.48 74	96.43 137

mPP-MVS		91.88 57	91.82 53	92.07 106	98.38 36	78.63 196	97.29 73	96.09 118	85.12 97	88.45 90	97.66 51	75.53 114	99.68 30	89.83 72	98.02 46	97.88 73

PAPR		92.74 42	92.17 51	94.45 22	98.89 9	84.87 51	97.20 77	96.20 111	87.73 57	88.40 91	98.12 27	78.71 74	99.76 16	87.99 91	96.28 75	98.74 23

tpmrst		88.36 117	87.38 117	91.31 126	94.36 139	79.92 149	87.32 305	95.26 162	85.32 91	88.34 92	86.13 257	80.60 51	96.70 180	83.78 119	85.34 175	97.30 109

GG-mvs-BLEND				93.49 55	94.94 120	86.26 25	81.62 326	97.00 38		88.32 93	94.30 140	91.23 2	96.21 195	88.49 85	97.43 57	98.00 66

EI-MVSNet-UG-set		91.35 67	91.22 61	91.73 120	97.39 66	80.68 130	96.47 132	96.83 51	87.92 52	88.30 94	97.36 68	77.84 85	99.13 78	89.43 78	89.45 133	95.37 160

MAR-MVS		90.63 75	90.22 70	91.86 117	98.47 33	78.20 213	97.18 79	96.61 74	83.87 132	88.18 95	98.18 18	68.71 175	99.75 19	83.66 125	97.15 63	97.63 90
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
DP-MVS Recon		91.72 59	90.85 65	94.34 24	99.50 1	85.00 46	98.51 16	95.96 125	80.57 188	88.08 96	97.63 56	76.84 96	99.89 6	85.67 105	94.88 91	98.13 54

VDDNet		86.44 152	84.51 159	92.22 101	91.56 199	81.83 106	97.10 93	94.64 190	69.50 305	87.84 97	95.19 121	48.01 307	97.92 124	89.82 73	86.92 150	96.89 123

UGNet		87.73 130	86.55 132	91.27 129	95.16 115	79.11 177	96.35 145	96.23 109	88.14 49	87.83 98	90.48 192	50.65 297	99.09 81	80.13 149	94.03 95	95.60 156
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
tpm2		87.35 138	86.26 134	90.62 145	92.93 168	78.67 194	88.06 300	95.99 123	79.33 215	87.40 99	86.43 253	80.28 55	96.40 186	80.23 147	85.73 167	96.79 126

CPTT-MVS		89.72 90	89.87 79	89.29 180	98.33 38	73.30 264	97.70 48	95.35 157	75.68 256	87.40 99	97.44 66	70.43 167	98.25 111	89.56 76	96.90 68	96.33 142

gg-mvs-nofinetune		85.48 172	82.90 188	93.24 63	94.51 137	85.82 30	79.22 330	96.97 41	61.19 327	87.33 101	53.01 344	90.58 3	96.07 199	86.07 103	97.23 62	97.81 79

CHOSEN 280x420		91.71 60	91.85 52	91.29 128	94.94 120	82.69 89	87.89 301	96.17 114	85.94 77	87.27 102	94.31 139	90.27 4	95.65 232	94.04 32	95.86 82	95.53 157

EPNet_dtu		87.65 131	87.89 102	86.93 230	94.57 127	71.37 285	96.72 117	96.50 85	88.56 41	87.12 103	95.02 130	75.91 111	94.01 280	66.62 256	90.00 131	95.42 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		88.67 109	87.82 104	91.24 131	92.68 169	78.82 190	96.95 106	93.85 229	87.55 58	87.07 104	95.13 126	63.43 223	97.21 156	77.58 172	96.15 76	97.70 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DWT-MVSNet_test		90.52 80	89.80 81	92.70 84	95.73 101	82.20 97	93.69 228	96.55 80	88.34 45	87.04 105	95.34 111	86.53 10	97.55 140	76.32 186	88.66 140	98.34 39

TESTMET0.1,1		89.83 87	89.34 87	91.31 126	92.54 174	80.19 145	97.11 90	96.57 77	86.15 73	86.85 106	91.83 175	79.32 63	96.95 168	81.30 141	92.35 114	96.77 128

PVSNet_Blended_VisFu		91.24 68	90.77 67	92.66 85	95.09 116	82.40 93	97.77 42	95.87 131	88.26 47	86.39 107	93.94 146	76.77 98	99.27 59	88.80 82	94.00 98	96.31 143

API-MVS		90.18 84	88.97 90	93.80 38	98.66 18	82.95 86	97.50 60	95.63 141	75.16 264	86.31 108	97.69 50	72.49 148	99.90 4	81.26 142	96.07 78	98.56 32

test-LLR		88.48 113	87.98 101	89.98 166	92.26 181	77.23 234	97.11 90	95.96 125	83.76 135	86.30 109	91.38 178	72.30 151	96.78 178	80.82 143	91.92 120	95.94 147

test-mter		88.95 100	88.60 95	89.98 166	92.26 181	77.23 234	97.11 90	95.96 125	85.32 91	86.30 109	91.38 178	76.37 104	96.78 178	80.82 143	91.92 120	95.94 147

PAPM_NR		91.46 65	90.82 66	93.37 60	98.50 31	81.81 107	95.03 200	96.13 115	84.65 110	86.10 111	97.65 55	79.24 67	99.75 19	83.20 131	96.88 70	98.56 32

MDTV_nov1_ep13_2view						81.74 109	86.80 310		80.65 186	85.65 112		74.26 139		76.52 184		96.98 118

PVSNet	82.34 9	89.02 99	87.79 105	92.71 83	95.49 105	81.50 115	97.70 48	97.29 19	87.76 56	85.47 113	95.12 127	56.90 272	98.90 94	80.33 145	94.02 96	97.71 84

tpmp4_e23		86.46 151	84.95 154	90.98 139	93.74 153	78.60 198	88.13 299	95.90 129	79.65 210	85.42 114	85.67 259	80.08 59	97.06 164	71.71 217	84.26 182	97.28 113

EPP-MVSNet		89.76 89	89.72 82	89.87 171	93.78 150	76.02 247	97.22 74	96.51 83	79.35 214	85.11 115	95.01 131	84.82 16	97.10 163	87.46 96	88.21 144	96.50 135

OMC-MVS		88.80 106	88.16 99	90.72 143	95.30 111	77.92 221	94.81 204	94.51 194	86.80 69	84.97 116	96.85 86	67.53 179	98.60 98	85.08 109	87.62 147	95.63 155

CHOSEN 1792x2688		91.07 70	90.21 71	93.64 45	95.18 114	83.53 73	96.26 153	96.13 115	88.92 38	84.90 117	93.10 163	72.86 146	99.62 36	88.86 80	95.67 85	97.79 80

thres200		88.92 102	87.65 107	92.73 82	96.30 81	85.62 34	97.85 35	98.86 1	84.38 118	84.82 118	93.99 145	75.12 132	98.01 116	70.86 227	86.67 153	94.56 176

PatchFormer-LS_test		90.14 85	89.30 88	92.65 87	95.43 106	82.46 92	93.46 234	96.35 101	88.56 41	84.82 118	95.22 118	84.63 20	97.55 140	78.40 162	86.81 152	97.94 71

MDTV_nov1_ep13			83.69 175		94.09 145	81.01 123	86.78 311	96.09 118	83.81 134	84.75 120	84.32 279	74.44 138	96.54 182	63.88 276	85.07 177

CDS-MVSNet		89.50 92	88.96 91	91.14 134	91.94 196	80.93 125	97.09 94	95.81 133	84.26 124	84.72 121	94.20 141	80.31 54	95.64 233	83.37 130	88.96 136	96.85 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP		90.39 81	89.97 74	91.64 122	97.58 60	78.21 212	96.78 114	96.72 60	84.73 108	84.72 121	97.23 73	71.22 159	99.63 35	88.37 88	92.41 113	97.08 117
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
CSCG		92.02 55	91.65 57	93.12 67	98.53 28	80.59 132	97.47 61	97.18 23	77.06 243	84.64 123	97.98 39	83.98 27	99.52 44	90.72 64	97.33 61	99.23 7

ab-mvs		87.08 140	84.94 155	93.48 56	93.34 160	83.67 71	88.82 293	95.70 137	81.18 174	84.55 124	90.14 199	62.72 226	98.94 92	85.49 107	82.54 204	97.85 76

mvs-test1		86.83 145	87.17 120	85.81 242	91.96 193	65.24 312	97.90 34	93.34 255	85.57 84	84.51 125	95.14 125	61.99 233	97.19 158	83.55 126	90.55 129	95.00 168

EPMVS		87.47 133	85.90 142	92.18 103	95.41 108	82.26 96	87.00 309	96.28 106	85.88 79	84.23 126	85.57 264	75.07 133	96.26 192	71.14 225	92.50 111	98.03 60

HyFIR lowres test		89.36 94	88.60 95	91.63 123	94.91 122	80.76 129	95.60 180	95.53 144	82.56 157	84.03 127	91.24 181	78.03 82	96.81 176	87.07 99	88.41 142	97.32 106

tfpn200view9		88.48 113	87.15 121	92.47 91	96.21 82	85.30 40	97.44 63	98.85 2	83.37 141	83.99 128	93.82 148	75.36 127	97.93 118	69.04 237	86.24 158	94.17 177

thres400		88.42 116	87.15 121	92.23 100	96.21 82	85.30 40	97.44 63	98.85 2	83.37 141	83.99 128	93.82 148	75.36 127	97.93 118	69.04 237	86.24 158	93.45 193

tpm		85.55 170	84.47 162	88.80 189	90.19 219	75.39 252	88.79 294	94.69 183	84.83 106	83.96 130	85.21 268	78.22 80	94.68 268	76.32 186	78.02 227	96.34 140

Fast-Effi-MVS+		87.93 128	86.94 128	90.92 140	94.04 147	79.16 175	98.26 21	93.72 238	81.29 173	83.94 131	92.90 164	69.83 171	96.68 181	76.70 182	91.74 123	96.93 121

XVG-OURS-SEG-HR		85.74 167	85.16 149	87.49 222	90.22 218	71.45 284	91.29 277	94.09 219	81.37 172	83.90 132	95.22 118	60.30 240	97.53 144	85.58 106	84.42 181	93.50 191

DeepC-MVS	86.58 3	91.53 64	91.06 64	92.94 74	94.52 135	81.89 104	95.95 164	95.98 124	90.76 21	83.76 133	96.76 90	73.24 144	99.71 24	91.67 55	96.96 67	97.22 115
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IS-MVSNet		88.67 109	88.16 99	90.20 155	93.61 155	76.86 238	96.77 116	93.07 262	84.02 128	83.62 134	95.60 108	74.69 137	96.24 194	78.43 161	93.66 103	97.49 99

tfpn111		88.08 122	86.70 130	92.20 102	96.10 86	84.90 48	97.14 85	98.85 2	82.69 153	83.41 135	93.66 151	75.43 122	97.82 127	67.13 252	85.88 163	93.89 184

conf200view11		88.27 120	86.95 126	92.24 99	96.10 86	84.90 48	97.14 85	98.85 2	82.69 153	83.41 135	93.66 151	75.43 122	97.93 118	69.04 237	86.24 158	93.89 184

thres100view900		88.30 118	86.95 126	92.33 97	96.10 86	84.90 48	97.14 85	98.85 2	82.69 153	83.41 135	93.66 151	75.43 122	97.93 118	69.04 237	86.24 158	94.17 177

thres600view7		88.06 123	86.70 130	92.15 104	96.10 86	85.17 42	97.14 85	98.85 2	82.70 152	83.41 135	93.66 151	75.43 122	97.82 127	67.13 252	85.88 163	93.45 193

XVG-OURS		85.18 175	84.38 163	87.59 218	90.42 216	71.73 280	91.06 280	94.07 220	82.00 164	83.29 139	95.08 128	56.42 279	97.55 140	83.70 124	83.42 187	93.49 192

Vis-MVSNet (Re-imp)		88.88 104	88.87 93	88.91 186	93.89 149	74.43 258	96.93 108	94.19 208	84.39 117	83.22 140	95.67 106	78.24 79	94.70 267	78.88 159	94.40 94	97.61 92

TAMVS		88.48 113	87.79 105	90.56 146	91.09 206	79.18 174	96.45 135	95.88 130	83.64 138	83.12 141	93.33 159	75.94 110	95.74 225	82.40 136	88.27 143	96.75 130

AdaColmap		88.81 105	87.61 111	92.39 95	99.33 4	79.95 148	96.70 121	95.58 142	77.51 235	83.05 142	96.69 92	61.90 236	99.72 23	84.29 115	93.47 104	97.50 98

PatchmatchNet		86.83 145	85.12 150	91.95 110	94.12 144	82.27 95	86.55 313	95.64 140	84.59 112	82.98 143	84.99 274	77.26 90	95.96 210	68.61 245	91.34 125	97.64 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test1		84.89 179	82.76 191	91.27 129	92.30 179	81.86 105	92.88 251	95.56 143	84.85 105	82.52 144	85.19 269	58.04 261	94.21 276	65.93 262	87.58 149	97.74 82

114514_t		88.79 107	87.57 112	92.45 92	98.21 42	81.74 109	96.99 100	95.45 152	75.16 264	82.48 145	95.69 105	68.59 176	98.50 103	80.33 145	95.18 89	97.10 116

PatchT		79.75 241	76.85 252	88.42 194	89.55 230	75.49 251	77.37 336	94.61 192	63.07 317	82.46 146	73.32 331	75.52 115	93.41 289	51.36 321	84.43 180	96.36 138

TR-MVS		86.30 154	84.93 156	90.42 148	94.63 126	77.58 227	96.57 128	93.82 230	80.30 195	82.42 147	95.16 123	58.74 253	97.55 140	74.88 198	87.82 146	96.13 145

view600		87.45 134	85.98 137	91.88 113	95.90 93	84.52 54	96.68 122	98.85 2	81.85 165	82.30 148	93.39 155	75.44 118	97.66 131	64.02 272	85.36 170	93.45 193

view800		87.45 134	85.98 137	91.88 113	95.90 93	84.52 54	96.68 122	98.85 2	81.85 165	82.30 148	93.39 155	75.44 118	97.66 131	64.02 272	85.36 170	93.45 193

conf0.05thres1000		87.45 134	85.98 137	91.88 113	95.90 93	84.52 54	96.68 122	98.85 2	81.85 165	82.30 148	93.39 155	75.44 118	97.66 131	64.02 272	85.36 170	93.45 193

tfpn		87.45 134	85.98 137	91.88 113	95.90 93	84.52 54	96.68 122	98.85 2	81.85 165	82.30 148	93.39 155	75.44 118	97.66 131	64.02 272	85.36 170	93.45 193

HQP-NCC					92.08 187		97.63 51		90.52 24	82.30 148

ACMP_Plane					92.08 187		97.63 51		90.52 24	82.30 148

HQP4-MVS										82.30 148			97.32 149			91.13 204

HQP-MVS		87.91 129	87.55 113	88.98 185	92.08 187	78.48 201	97.63 51	94.80 178	90.52 24	82.30 148	94.56 136	65.40 210	97.32 149	87.67 94	83.01 191	91.13 204

CR-MVSNet		83.53 195	81.36 212	90.06 162	90.16 220	79.75 152	79.02 332	91.12 284	84.24 125	82.27 156	80.35 304	75.45 116	93.67 286	63.37 280	86.25 156	96.75 130

RPMNet		79.32 247	75.75 259	90.06 162	90.16 220	79.75 152	79.02 332	93.92 226	58.43 334	82.27 156	72.55 332	73.03 145	93.67 286	46.10 333	86.25 156	96.75 130

CVMVSNet		84.83 180	85.57 143	82.63 293	91.55 200	60.38 325	95.13 196	95.03 168	80.60 187	82.10 158	94.71 134	66.40 199	90.19 323	74.30 202	90.32 130	97.31 108

PLC	83.97 7	88.00 125	87.38 117	89.83 173	98.02 48	76.46 242	97.16 83	94.43 199	79.26 218	81.98 159	96.28 96	69.36 172	99.27 59	77.71 170	92.25 117	93.77 188
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
JIA-IIPM		79.00 250	77.20 247	84.40 272	89.74 227	64.06 316	75.30 338	95.44 153	62.15 322	81.90 160	59.08 342	78.92 70	95.59 237	66.51 259	85.78 166	93.54 190

BH-RMVSNet		86.84 144	85.28 147	91.49 124	95.35 110	80.26 143	96.95 106	92.21 270	82.86 150	81.77 161	95.46 109	59.34 248	97.64 135	69.79 234	93.81 101	96.57 134

tfpn_ndepth		87.25 139	86.00 136	91.01 138	95.86 97	81.46 116	96.53 129	97.09 33	77.35 238	81.36 162	95.07 129	84.74 18	95.86 215	60.88 287	85.14 176	95.72 153

HQP_MVS		87.50 132	87.09 124	88.74 190	91.86 197	77.96 218	97.18 79	94.69 183	89.89 30	81.33 163	94.15 142	64.77 215	97.30 151	87.08 97	82.82 201	90.96 206

plane_prior3						77.75 225			90.17 28	81.33 163

VPA-MVSNet		85.32 173	83.83 174	89.77 175	90.25 217	82.63 90	96.36 144	97.07 35	83.03 147	81.21 165	89.02 209	61.58 237	96.31 190	85.02 111	70.95 254	90.36 212

GA-MVS		85.79 166	84.04 173	91.02 137	89.47 232	80.27 142	96.90 109	94.84 176	85.57 84	80.88 166	89.08 207	56.56 278	96.47 185	77.72 169	85.35 174	96.34 140

1112_ss		88.60 112	87.47 115	92.00 109	93.21 161	80.97 124	96.47 132	92.46 268	83.64 138	80.86 167	97.30 71	80.24 56	97.62 136	77.60 171	85.49 168	97.40 103

dp		84.30 187	82.31 196	90.28 152	94.24 142	77.97 217	86.57 312	95.53 144	79.94 205	80.75 168	85.16 271	71.49 158	96.39 187	63.73 277	83.36 188	96.48 136

Test_1112_low_res		88.03 124	86.73 129	91.94 111	93.15 163	80.88 126	96.44 137	92.41 269	83.59 140	80.74 169	91.16 182	80.18 57	97.59 137	77.48 173	85.40 169	97.36 105

cascas		86.50 150	84.48 161	92.55 90	92.64 173	85.95 28	97.04 99	95.07 166	75.32 261	80.50 170	91.02 184	54.33 292	97.98 117	86.79 101	87.62 147	93.71 189

TAPA-MVS	81.61 12	85.02 176	83.67 176	89.06 182	96.79 77	73.27 266	95.92 166	94.79 180	74.81 271	80.47 171	96.83 87	71.07 161	98.19 114	49.82 327	92.57 110	95.71 154
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		85.84 159	85.10 151	88.06 203	88.34 244	77.83 224	95.72 176	94.20 206	87.89 55	80.45 172	94.05 144	58.57 254	97.26 155	83.88 118	82.76 203	89.09 236

nrg030		86.79 147	85.43 144	90.87 142	88.76 238	85.34 39	97.06 98	94.33 202	84.31 120	80.45 172	91.98 170	72.36 149	96.36 188	88.48 86	71.13 252	90.93 208

EI-MVSNet		85.80 161	85.20 148	87.59 218	91.55 200	77.41 230	95.13 196	95.36 155	80.43 192	80.33 174	94.71 134	73.72 142	95.97 206	76.96 181	78.64 222	89.39 230

MVSTER		89.25 97	88.92 92	90.24 153	95.98 91	84.66 52	96.79 113	95.36 155	87.19 65	80.33 174	90.61 191	90.02 5	95.97 206	85.38 108	78.64 222	90.09 221

ADS-MVSNet2		79.57 243	77.53 245	85.71 243	93.78 150	72.13 273	79.48 328	86.11 328	73.09 287	80.14 176	79.99 306	62.15 230	90.14 324	59.49 290	83.52 185	94.85 170

ADS-MVSNet		81.26 232	78.36 238	89.96 168	93.78 150	79.78 151	79.48 328	93.60 243	73.09 287	80.14 176	79.99 306	62.15 230	95.24 253	59.49 290	83.52 185	94.85 170

tfpn1000		86.43 153	85.10 151	90.41 149	95.56 103	80.51 137	95.90 169	97.09 33	75.91 253	80.02 178	94.82 132	84.78 17	95.47 243	57.36 297	84.46 179	95.26 163

Effi-MVS+-dtu		84.61 183	84.90 157	83.72 283	91.96 193	63.14 319	94.95 201	93.34 255	85.57 84	79.79 179	87.12 234	61.99 233	95.61 236	83.55 126	85.83 165	92.41 200

VPNet		84.69 182	82.92 187	90.01 164	89.01 236	83.45 75	96.71 119	95.46 149	85.71 82	79.65 180	92.18 169	56.66 276	96.01 205	83.05 134	67.84 288	90.56 210

conf0.01		85.70 168	84.35 164	89.77 175	94.53 129	79.70 156	95.17 190	97.11 26	75.97 247	79.44 181	95.31 112	81.90 41	95.73 226	56.78 302	82.91 194	93.89 184

conf0.002		85.70 168	84.35 164	89.77 175	94.53 129	79.70 156	95.17 190	97.11 26	75.97 247	79.44 181	95.31 112	81.90 41	95.73 226	56.78 302	82.91 194	93.89 184

thresconf0.02		85.80 161	84.35 164	90.17 156	94.53 129	79.70 156	95.17 190	97.11 26	75.97 247	79.44 181	95.31 112	81.90 41	95.73 226	56.78 302	82.91 194	95.09 164

tfpn_n400		85.80 161	84.35 164	90.17 156	94.53 129	79.70 156	95.17 190	97.11 26	75.97 247	79.44 181	95.31 112	81.90 41	95.73 226	56.78 302	82.91 194	95.09 164

tfpnconf		85.80 161	84.35 164	90.17 156	94.53 129	79.70 156	95.17 190	97.11 26	75.97 247	79.44 181	95.31 112	81.90 41	95.73 226	56.78 302	82.91 194	95.09 164

tfpnview11		85.80 161	84.35 164	90.17 156	94.53 129	79.70 156	95.17 190	97.11 26	75.97 247	79.44 181	95.31 112	81.90 41	95.73 226	56.78 302	82.91 194	95.09 164

CLD-MVS		87.97 126	87.48 114	89.44 178	92.16 186	80.54 135	98.14 27	94.92 171	91.41 16	79.43 187	95.40 110	62.34 228	97.27 154	90.60 65	82.90 200	90.50 211
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IB-MVS	85.34 4	88.67 109	87.14 123	93.26 62	93.12 165	84.32 59	98.76 10	97.27 20	87.19 65	79.36 188	90.45 193	83.92 28	98.53 102	84.41 114	69.79 269	96.93 121
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
PatchMatch-RL		85.00 177	83.66 177	89.02 184	95.86 97	74.55 257	92.49 259	93.60 243	79.30 217	79.29 189	91.47 176	58.53 255	98.45 106	70.22 230	92.17 118	94.07 181

CNLPA		86.96 141	85.37 146	91.72 121	97.59 59	79.34 168	97.21 75	91.05 287	74.22 278	78.90 190	96.75 91	67.21 183	98.95 90	74.68 200	90.77 128	96.88 124

MVS		90.60 76	88.64 94	96.50 1	94.25 141	90.53 4	93.33 238	97.21 22	77.59 234	78.88 191	97.31 69	71.52 157	99.69 28	89.60 74	98.03 45	99.27 6

mvs_anonymous		88.68 108	87.62 110	91.86 117	94.80 123	81.69 113	93.53 233	94.92 171	82.03 163	78.87 192	90.43 194	75.77 112	95.34 248	85.04 110	93.16 108	98.55 34

tpm cat1		83.63 194	81.38 211	90.39 150	93.53 158	78.19 214	85.56 319	95.09 164	70.78 301	78.51 193	83.28 292	74.80 135	97.03 165	66.77 255	84.05 183	95.95 146

UniMVSNet (Re)		85.31 174	84.23 171	88.55 193	89.75 225	80.55 134	96.72 117	96.89 48	85.42 89	78.40 194	88.93 210	75.38 126	95.52 240	78.58 160	68.02 285	89.57 228

FIs		86.73 149	86.10 135	88.61 192	90.05 222	80.21 144	96.14 157	96.95 43	85.56 87	78.37 195	92.30 167	76.73 99	95.28 251	79.51 153	79.27 217	90.35 213

BH-w/o		88.24 121	87.47 115	90.54 147	95.03 119	78.54 199	97.41 69	93.82 230	84.08 126	78.23 196	94.51 138	69.34 173	97.21 156	80.21 148	94.58 92	95.87 149

UniMVSNet_NR-MVSNet		85.49 171	84.59 158	88.21 202	89.44 233	79.36 166	96.71 119	96.41 93	85.22 93	78.11 197	90.98 186	76.97 95	95.14 255	79.14 156	68.30 282	90.12 219

DU-MVS		84.57 184	83.33 184	88.28 200	88.76 238	79.36 166	96.43 141	95.41 154	85.42 89	78.11 197	90.82 187	67.61 177	95.14 255	79.14 156	68.30 282	90.33 214

v6		83.45 197	81.94 200	87.95 208	86.62 263	79.03 184	96.32 147	94.17 209	80.76 181	77.57 199	87.23 233	67.03 188	96.09 196	77.73 166	70.06 267	88.75 248

v1neww		83.45 197	81.95 198	87.95 208	86.66 259	79.04 181	96.32 147	94.17 209	80.76 181	77.56 200	87.25 231	67.02 189	96.08 197	77.73 166	70.07 265	88.74 250

v7new		83.45 197	81.95 198	87.95 208	86.66 259	79.04 181	96.32 147	94.17 209	80.76 181	77.56 200	87.25 231	67.02 189	96.08 197	77.73 166	70.07 265	88.74 250

divwei89l23v2f112		83.36 201	81.81 204	88.01 205	86.60 266	79.23 172	96.44 137	94.12 216	80.88 177	77.49 202	86.87 239	67.08 186	96.03 201	77.14 176	69.67 273	88.76 246

v7		82.99 210	81.41 210	87.73 214	86.41 270	78.86 189	96.10 160	93.98 223	79.88 206	77.49 202	87.11 235	65.44 208	95.97 206	75.69 193	70.59 258	88.36 258

v1141		83.36 201	81.81 204	88.01 205	86.61 265	79.26 169	96.44 137	94.12 216	80.88 177	77.48 204	86.87 239	67.08 186	96.03 201	77.14 176	69.69 272	88.75 248

v1		83.37 200	81.82 202	88.01 205	86.58 267	79.24 170	96.45 135	94.13 213	80.88 177	77.48 204	86.88 238	67.15 184	96.04 200	77.15 175	69.67 273	88.76 246

v1144		82.90 211	81.27 213	87.78 213	86.29 276	79.07 180	96.14 157	93.93 225	80.05 202	77.38 206	86.80 243	65.50 206	95.93 212	75.21 196	70.13 261	88.33 260

FC-MVSNet-test		85.96 157	85.39 145	87.66 216	89.38 234	78.02 216	95.65 179	96.87 49	85.12 97	77.34 207	91.94 173	76.28 106	94.74 266	77.09 178	78.82 220	90.21 216

v2v482		83.46 196	81.86 201	88.25 201	86.19 280	79.65 162	96.34 146	94.02 222	81.56 171	77.32 208	88.23 219	65.62 205	96.03 201	77.77 163	69.72 271	89.09 236

Baseline_NR-MVSNet		81.22 233	80.07 227	84.68 261	85.32 296	75.12 254	96.48 131	88.80 312	76.24 246	77.28 209	86.40 254	67.61 177	94.39 273	75.73 192	66.73 298	84.54 306

V42		83.04 207	81.53 208	87.57 220	86.27 278	79.09 179	95.87 171	94.11 218	80.35 194	77.22 210	86.79 244	65.32 212	96.02 204	77.74 165	70.14 260	87.61 273

v144192		82.43 216	80.73 218	87.54 221	85.81 289	78.22 209	95.98 162	93.78 235	79.09 220	77.11 211	86.49 249	64.66 217	95.91 213	74.20 203	69.42 276	88.49 253

ACMM	80.70 13	83.72 193	82.85 189	86.31 237	91.19 205	72.12 274	95.88 170	94.29 203	80.44 190	77.02 212	91.96 171	55.24 286	97.14 162	79.30 155	80.38 209	89.67 227
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1192		82.31 220	80.55 220	87.60 217	85.94 286	78.47 204	95.85 173	93.80 233	79.33 215	76.97 213	86.51 248	63.33 224	95.87 214	73.11 207	70.13 261	88.46 255

PCF-MVS	84.09 5	86.77 148	85.00 153	92.08 105	92.06 190	83.07 81	92.14 267	94.47 197	79.63 211	76.90 214	94.78 133	71.15 160	99.20 70	72.87 208	91.05 126	93.98 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1921920		82.02 224	80.23 224	87.41 223	85.62 290	77.92 221	95.79 175	93.69 239	78.86 224	76.67 215	86.44 251	62.50 227	95.83 217	72.69 209	69.77 270	88.47 254

WR-MVS		84.32 186	82.96 186	88.41 195	89.38 234	80.32 139	96.59 127	96.25 108	83.97 129	76.63 216	90.36 195	67.53 179	94.86 263	75.82 191	70.09 264	90.06 223

BH-untuned		86.95 142	85.94 141	89.99 165	94.52 135	77.46 229	96.78 114	93.37 254	81.80 169	76.62 217	93.81 150	66.64 196	97.02 166	76.06 188	93.88 100	95.48 158

v1240		81.70 227	79.83 231	87.30 226	85.50 291	77.70 226	95.48 184	93.44 246	78.46 228	76.53 218	86.44 251	60.85 239	95.84 216	71.59 219	70.17 259	88.35 259

PS-MVSNAJss		84.91 178	84.30 170	86.74 231	85.89 288	74.40 259	94.95 201	94.16 212	83.93 130	76.45 219	90.11 200	71.04 162	95.77 220	83.16 132	79.02 219	90.06 223

LPG-MVS_test		84.20 188	83.49 182	86.33 234	90.88 209	73.06 267	95.28 186	94.13 213	82.20 160	76.31 220	93.20 160	54.83 290	96.95 168	83.72 122	80.83 207	88.98 240

LGP-MVS_train				86.33 234	90.88 209	73.06 267		94.13 213	82.20 160	76.31 220	93.20 160	54.83 290	96.95 168	83.72 122	80.83 207	88.98 240

F-COLMAP		84.50 185	83.44 183	87.67 215	95.22 113	72.22 271	95.95 164	93.78 235	75.74 254	76.30 222	95.18 122	59.50 245	98.45 106	72.67 210	86.59 155	92.35 201

tpmvs		83.04 207	80.77 217	89.84 172	95.43 106	77.96 218	85.59 318	95.32 158	75.31 262	76.27 223	83.70 288	73.89 140	97.41 146	59.53 289	81.93 205	94.14 179

3Dnovator	82.32 10	89.33 95	87.64 108	94.42 23	93.73 154	85.70 32	97.73 47	96.75 56	86.73 70	76.21 224	95.93 100	62.17 229	99.68 30	81.67 140	97.81 50	97.88 73

TranMVSNet+NR-MVSNet		83.24 205	81.71 206	87.83 211	87.71 251	78.81 192	96.13 159	94.82 177	84.52 113	76.18 225	90.78 189	64.07 218	94.60 269	74.60 201	66.59 299	90.09 221

1314		88.94 101	87.20 119	94.17 29	93.21 161	85.73 31	93.33 238	96.64 70	82.89 149	75.98 226	96.36 95	66.83 193	99.39 54	83.52 129	96.02 80	97.39 104

Fast-Effi-MVS+-dtu		83.33 204	82.60 193	85.50 246	89.55 230	69.38 301	96.09 161	91.38 280	82.30 159	75.96 227	91.41 177	56.71 273	95.58 238	75.13 197	84.90 178	91.54 202

XXY-MVS		83.84 191	82.00 197	89.35 179	87.13 255	81.38 117	95.72 176	94.26 204	80.15 200	75.92 228	90.63 190	61.96 235	96.52 183	78.98 158	73.28 246	90.14 217

GBi-Net		82.42 217	80.43 222	88.39 196	92.66 170	81.95 99	94.30 215	93.38 251	79.06 221	75.82 229	85.66 260	56.38 280	93.84 282	71.23 222	75.38 235	89.38 232

test1		82.42 217	80.43 222	88.39 196	92.66 170	81.95 99	94.30 215	93.38 251	79.06 221	75.82 229	85.66 260	56.38 280	93.84 282	71.23 222	75.38 235	89.38 232

FMVSNet3		84.71 181	82.71 192	90.70 144	94.55 128	87.71 16	95.92 166	94.67 186	81.73 170	75.82 229	88.08 222	66.99 191	94.47 271	71.23 222	75.38 235	89.91 225

IterMVS-LS		83.93 190	82.80 190	87.31 225	91.46 203	77.39 231	95.66 178	93.43 248	80.44 190	75.51 232	87.26 230	73.72 142	95.16 254	76.99 179	70.72 256	89.39 230
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator+	82.88 8	89.63 91	87.85 103	94.99 15	94.49 138	86.76 23	97.84 36	95.74 135	86.10 74	75.47 233	96.02 99	65.00 214	99.51 47	82.91 135	97.07 64	98.72 26

test_djsdf		83.00 209	82.45 195	84.64 263	84.07 307	69.78 297	94.80 205	94.48 195	80.74 184	75.41 234	87.70 225	61.32 238	95.10 257	83.77 120	79.76 210	89.04 238

v148		82.41 219	80.89 215	86.99 229	86.18 281	76.81 239	96.27 152	93.82 230	80.49 189	75.28 235	86.11 258	67.32 182	95.75 222	75.48 194	67.03 294	88.42 257

QAPM		86.88 143	84.51 159	93.98 32	94.04 147	85.89 29	97.19 78	96.05 121	73.62 282	75.12 236	95.62 107	62.02 232	99.74 21	70.88 226	96.06 79	96.30 144

FMVSNet2		82.79 212	80.44 221	89.83 173	92.66 170	85.43 37	95.42 185	94.35 201	79.06 221	74.46 237	87.28 228	56.38 280	94.31 274	69.72 235	74.68 239	89.76 226

MIMVSNet		79.18 249	75.99 258	88.72 191	87.37 254	80.66 131	79.96 327	91.82 275	77.38 237	74.33 238	81.87 297	41.78 323	90.74 319	66.36 261	83.10 190	94.76 172

RPSCF		77.73 264	76.63 254	81.06 302	88.66 242	55.76 334	87.77 302	87.88 318	64.82 316	74.14 239	92.79 165	49.22 304	96.81 176	67.47 251	76.88 230	90.62 209

ACMP	81.66 11	84.00 189	83.22 185	86.33 234	91.53 202	72.95 269	95.91 168	93.79 234	83.70 137	73.79 240	92.22 168	54.31 293	96.89 172	83.98 117	79.74 212	89.16 235
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs5		81.34 231	79.54 232	86.73 232	85.02 298	76.91 237	96.22 154	91.65 278	77.65 233	73.55 241	88.61 213	55.70 283	94.43 272	74.12 204	73.35 245	88.86 245

jajsoiax		82.12 223	81.15 214	85.03 251	84.19 305	70.70 290	94.22 219	93.95 224	83.07 146	73.48 242	89.75 202	49.66 302	95.37 247	82.24 138	79.76 210	89.02 239

mvs_tets		81.74 226	80.71 219	84.84 255	84.22 304	70.29 293	93.91 223	93.78 235	82.77 151	73.37 243	89.46 205	47.36 311	95.31 250	81.99 139	79.55 216	88.92 244

pmmvs4		82.54 215	80.79 216	87.79 212	86.11 283	80.49 138	93.55 232	93.18 258	77.29 239	73.35 244	89.40 206	65.26 213	95.05 260	75.32 195	73.61 242	87.83 268

LS3D		82.22 222	79.94 230	89.06 182	97.43 65	74.06 262	93.20 246	92.05 272	61.90 323	73.33 245	95.21 120	59.35 247	99.21 66	54.54 313	92.48 112	93.90 183

v10		81.43 230	79.53 233	87.11 227	86.38 271	78.87 188	94.31 214	93.43 248	77.88 231	73.24 246	85.26 267	65.44 208	95.75 222	72.14 213	67.71 289	86.72 287

v8		81.88 225	80.06 228	87.32 224	86.63 262	79.04 181	94.41 211	93.65 241	78.77 225	73.19 247	85.57 264	66.87 192	95.81 218	73.84 206	67.61 290	87.11 282

test0.0.03 1		82.79 212	82.48 194	83.74 282	86.81 257	72.22 271	96.52 130	95.03 168	83.76 135	73.00 248	93.20 160	72.30 151	88.88 326	64.15 271	77.52 229	90.12 219

anonymousdsp		80.98 236	79.97 229	84.01 275	81.73 314	70.44 292	92.49 259	93.58 245	77.10 242	72.98 249	86.31 255	57.58 266	94.90 261	79.32 154	78.63 224	86.69 288

XVG-ACMP-BASELINE		79.38 246	77.90 243	83.81 279	84.98 299	67.14 309	89.03 292	93.18 258	80.26 198	72.87 250	88.15 221	38.55 328	96.26 192	76.05 189	78.05 226	88.02 265

WR-MVS_H		81.02 234	80.09 225	83.79 280	88.08 247	71.26 288	94.46 209	96.54 81	80.08 201	72.81 251	86.82 242	70.36 168	92.65 293	64.18 270	67.50 291	87.46 279

OpenMVS	79.58 14	86.09 156	83.62 178	93.50 54	90.95 208	86.71 24	97.44 63	95.83 132	75.35 260	72.64 252	95.72 103	57.42 270	99.64 34	71.41 220	95.85 83	94.13 180

CP-MVSNet		81.01 235	80.08 226	83.79 280	87.91 249	70.51 291	94.29 218	95.65 139	80.83 180	72.54 253	88.84 211	63.71 219	92.32 296	68.58 246	68.36 281	88.55 252

PS-CasMVS		80.27 239	79.18 234	83.52 286	87.56 253	69.88 296	94.08 220	95.29 160	80.27 197	72.08 254	88.51 217	59.22 250	92.23 298	67.49 250	68.15 284	88.45 256

FMVSNet1		79.50 244	76.54 255	88.39 196	88.47 243	81.95 99	94.30 215	93.38 251	73.14 286	72.04 255	85.66 260	43.86 315	93.84 282	65.48 264	72.53 249	89.38 232

PEN-MVS		79.47 245	78.26 240	83.08 289	86.36 273	68.58 303	93.85 225	94.77 181	79.76 208	71.37 256	88.55 214	59.79 241	92.46 294	64.50 268	65.40 300	88.19 262

Patchmtry		77.36 268	74.59 275	85.67 244	89.75 225	75.75 249	77.85 335	91.12 284	60.28 330	71.23 257	80.35 304	75.45 116	93.56 288	57.94 295	67.34 293	87.68 271

IterMVS		80.67 237	79.16 235	85.20 249	89.79 224	76.08 246	92.97 250	91.86 274	80.28 196	71.20 258	85.14 272	57.93 265	91.34 314	72.52 211	70.74 255	88.18 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		81.47 229	78.28 239	91.04 135	98.14 43	78.48 201	95.09 199	86.97 323	61.14 328	71.12 259	92.78 166	59.59 243	99.38 55	53.11 317	86.61 154	95.27 162

semantic-postprocess				84.73 259	89.63 229	74.66 255		91.81 276	80.05 202	71.06 260	85.18 270	57.98 264	91.40 313	72.48 212	70.70 257	88.12 264

v7n		79.32 247	77.34 246	85.28 248	84.05 308	72.89 270	93.38 236	93.87 228	75.02 267	70.68 261	84.37 278	59.58 244	95.62 235	67.60 249	67.50 291	87.32 281

MS-PatchMatch		83.05 206	81.82 202	86.72 233	89.64 228	79.10 178	94.88 203	94.59 193	79.70 209	70.67 262	89.65 203	50.43 299	96.82 175	70.82 229	95.99 81	84.25 308

DTE-MVSNet		78.37 255	77.06 248	82.32 297	85.22 297	67.17 308	93.40 235	93.66 240	78.71 226	70.53 263	88.29 218	59.06 251	92.23 298	61.38 285	63.28 308	87.56 275

pm-mvs1		80.05 240	78.02 242	86.15 239	85.42 292	75.81 248	95.11 198	92.69 267	77.13 240	70.36 264	87.43 227	58.44 256	95.27 252	71.36 221	64.25 304	87.36 280

v52		78.70 251	76.95 249	83.95 276	81.71 315	71.34 286	91.93 270	93.43 248	74.69 274	70.36 264	83.71 287	58.04 261	95.50 241	71.84 214	66.82 297	85.00 303

V4		78.70 251	76.95 249	83.95 276	81.66 316	71.34 286	91.94 269	93.44 246	74.69 274	70.35 266	83.73 286	58.07 260	95.50 241	71.84 214	66.86 296	85.02 302

Anonymous20240521		79.73 242	78.10 241	84.63 264	87.90 250	71.58 282	93.91 223	94.39 200	76.69 245	70.27 267	87.00 236	58.97 252	94.76 265	64.38 269	69.43 275	87.54 277

PVSNet_0	77.72 15	81.70 227	78.95 237	89.94 169	90.77 211	76.72 241	95.96 163	96.95 43	85.01 101	70.24 268	88.53 216	52.32 294	98.20 113	86.68 102	44.08 344	94.89 169

tfpnnormal		78.14 257	75.42 263	86.31 237	88.33 245	79.24 170	94.41 211	96.22 110	73.51 283	69.81 269	85.52 266	55.43 284	95.75 222	47.65 331	67.86 287	83.95 312

EU-MVSNet		76.92 277	76.95 249	76.83 314	84.10 306	54.73 335	91.77 273	92.71 266	72.74 290	69.57 270	88.69 212	58.03 263	87.43 331	64.91 267	70.00 268	88.33 260

v748		78.69 253	76.79 253	84.39 273	83.40 311	70.78 289	93.25 244	93.62 242	74.96 268	69.40 271	83.74 285	59.40 246	95.39 245	68.74 243	64.59 302	86.99 285

ITE_SJBPF				82.38 295	87.00 256	65.59 311		89.55 305	79.99 204	69.37 272	91.30 180	41.60 325	95.33 249	62.86 282	74.63 240	86.24 293

DSMNet-mixed		73.13 294	72.45 288	75.19 319	77.51 328	46.82 343	85.09 320	82.01 343	67.61 312	69.27 273	81.33 299	50.89 296	86.28 334	54.54 313	83.80 184	92.46 199

MVP-Stereo		82.65 214	81.67 207	85.59 245	86.10 284	78.29 207	93.33 238	92.82 264	77.75 232	69.17 274	87.98 223	59.28 249	95.76 221	71.77 216	96.88 70	82.73 326
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MSDG		80.62 238	77.77 244	89.14 181	93.43 159	77.24 233	91.89 271	90.18 301	69.86 304	68.02 275	91.94 173	52.21 295	98.84 95	59.32 292	83.12 189	91.35 203

NR-MVSNet		83.35 203	81.52 209	88.84 187	88.76 238	81.31 119	94.45 210	95.16 163	84.65 110	67.81 276	90.82 187	70.36 168	94.87 262	74.75 199	66.89 295	90.33 214

TransMVSNet (Re)		76.94 276	74.38 278	84.62 266	85.92 287	75.25 253	95.28 186	89.18 309	73.88 281	67.22 277	86.46 250	59.64 242	94.10 278	59.24 293	52.57 334	84.50 307

Anonymous20231206		75.29 284	73.64 283	80.22 305	80.75 317	63.38 318	93.36 237	90.71 296	73.09 287	67.12 278	83.70 288	50.33 300	90.85 318	53.63 316	70.10 263	86.44 290

ppachtmachnet_test		77.19 272	74.22 280	86.13 240	85.39 293	78.22 209	93.98 222	91.36 282	71.74 297	67.11 279	84.87 275	56.67 275	93.37 290	52.21 319	64.59 302	86.80 286

Patchmatch-test		78.25 256	74.72 272	88.83 188	91.20 204	74.10 261	73.91 342	88.70 315	59.89 332	66.82 280	85.12 273	78.38 78	94.54 270	48.84 329	79.58 214	97.86 75

LTVRE_ROB	73.68 18	77.99 258	75.74 260	84.74 258	90.45 215	72.02 275	86.41 314	91.12 284	72.57 292	66.63 281	87.27 229	54.95 289	96.98 167	56.29 308	75.98 231	85.21 301
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
OurMVSNet-221017-0		77.18 273	76.06 257	80.55 304	83.78 309	60.00 326	90.35 282	91.05 287	77.01 244	66.62 282	87.92 224	47.73 309	94.03 279	71.63 218	68.44 280	87.62 272

testgi		74.88 286	73.40 284	79.32 309	80.13 321	61.75 322	93.21 245	86.64 326	79.49 213	66.56 283	91.06 183	35.51 334	88.67 327	56.79 301	71.25 251	87.56 275

LCM-MVSNet-Re		83.75 192	83.54 180	84.39 273	93.54 157	64.14 315	92.51 258	84.03 337	83.90 131	66.14 284	86.59 247	67.36 181	92.68 292	84.89 112	92.87 109	96.35 139

DI_MVS_plusplus_test		85.92 158	83.61 179	92.86 76	86.43 269	83.20 78	95.57 181	95.46 149	85.10 100	65.99 285	86.84 241	56.70 274	97.89 125	88.10 90	92.33 115	97.48 100

pmmvs6		74.65 287	71.67 290	83.60 285	79.13 324	69.94 295	93.31 242	90.88 291	61.05 329	65.83 286	84.15 281	43.43 317	94.83 264	66.62 256	60.63 312	86.02 297

test_normal		85.83 160	83.51 181	92.78 80	86.33 274	83.01 84	95.56 183	95.46 149	85.11 99	65.73 287	86.63 246	56.62 277	97.86 126	87.87 92	92.29 116	97.47 101

our_test_3		77.90 261	75.37 265	85.48 247	85.39 293	76.74 240	93.63 229	91.67 277	73.39 285	65.72 288	84.65 277	58.20 258	93.13 291	57.82 296	67.87 286	86.57 289

test2356		74.41 288	74.53 277	74.07 321	76.13 333	54.45 336	94.74 207	92.08 271	71.96 295	65.51 289	83.05 294	56.96 271	83.71 341	52.74 318	77.58 228	84.06 310

COLMAP_ROB	73.24 19	75.74 282	73.00 287	83.94 278	92.38 175	69.08 302	91.85 272	86.93 324	61.48 326	65.32 290	90.27 196	42.27 322	96.93 171	50.91 324	75.63 234	85.80 298
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet5		76.46 279	74.16 281	83.35 288	90.05 222	76.17 245	89.58 287	89.85 303	71.39 300	65.29 291	80.42 303	50.61 298	87.70 330	61.05 286	69.24 277	86.18 294

ACMH+	76.62 16	77.47 266	74.94 269	85.05 250	91.07 207	71.58 282	93.26 243	90.01 302	71.80 296	64.76 292	88.55 214	41.62 324	96.48 184	62.35 283	71.00 253	87.09 283

Patchmatch-RL test		76.65 278	74.01 282	84.55 267	77.37 329	64.23 314	78.49 334	82.84 342	78.48 227	64.63 293	73.40 330	76.05 109	91.70 312	76.99 179	57.84 316	97.72 83

SixPastTwentyTwo		76.04 280	74.32 279	81.22 301	84.54 301	61.43 324	91.16 278	89.30 308	77.89 230	64.04 294	86.31 255	48.23 305	94.29 275	63.54 279	63.84 306	87.93 267

AllTest		75.92 281	73.06 286	84.47 269	92.18 184	67.29 306	91.07 279	84.43 334	67.63 308	63.48 295	90.18 197	38.20 329	97.16 159	57.04 298	73.37 243	88.97 242

TestCases				84.47 269	92.18 184	67.29 306		84.43 334	67.63 308	63.48 295	90.18 197	38.20 329	97.16 159	57.04 298	73.37 243	88.97 242

ACMH	75.40 17	77.99 258	74.96 268	87.10 228	90.67 212	76.41 243	93.19 247	91.64 279	72.47 293	63.44 297	87.61 226	43.34 318	97.16 159	58.34 294	73.94 241	87.72 269
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v18		77.96 260	75.61 261	84.98 252	86.66 259	79.01 185	93.02 249	90.94 289	75.69 255	63.19 298	77.62 313	67.11 185	92.07 301	70.05 231	56.24 320	83.87 313

USDC		78.65 254	76.25 256	85.85 241	87.58 252	74.60 256	89.58 287	90.58 300	84.05 127	63.13 299	88.23 219	40.69 327	96.86 174	66.57 258	75.81 233	86.09 296

testpf		70.88 302	70.47 296	72.08 324	88.92 237	59.57 328	48.62 352	93.15 260	63.05 318	63.07 300	79.51 309	58.33 257	86.63 333	66.93 254	72.69 248	70.05 344

LF4IMVS		72.36 298	70.82 293	76.95 313	79.18 323	56.33 331	86.12 315	86.11 328	69.30 306	63.06 301	86.66 245	33.03 338	92.25 297	65.33 265	68.64 279	82.28 330

v16		77.84 262	75.47 262	84.93 254	86.62 263	78.93 187	92.84 253	90.89 290	75.50 258	63.03 302	77.54 314	66.82 194	92.04 302	69.82 232	56.22 321	83.82 315

v17		77.79 263	75.41 264	84.94 253	86.53 268	78.94 186	92.83 254	90.88 291	75.51 257	62.97 303	77.50 315	66.69 195	92.03 303	69.80 233	56.01 322	83.83 314

testus		70.06 303	69.09 301	72.98 323	74.54 336	51.28 341	93.78 226	87.34 320	71.49 299	62.69 304	83.46 290	24.44 345	84.77 340	51.22 323	72.85 247	82.90 323

v15		77.52 265	75.09 266	84.82 256	86.37 272	78.82 190	92.58 257	90.78 293	75.47 259	62.53 305	77.17 316	66.58 198	91.92 304	69.18 236	55.16 324	83.73 316

V14		77.43 267	74.99 267	84.75 257	86.32 275	78.67 194	92.44 261	90.77 294	75.28 263	62.42 306	77.13 317	66.40 199	91.88 305	69.01 241	55.14 325	83.70 317

v11		77.21 270	74.72 272	84.68 261	86.29 276	78.62 197	92.30 265	90.63 299	74.86 270	62.32 307	76.73 322	65.49 207	91.83 307	68.17 248	55.72 323	83.59 320

V9		77.32 269	74.87 270	84.69 260	86.26 279	78.52 200	92.33 264	90.72 295	75.11 266	62.21 308	77.08 319	66.19 201	91.87 306	68.84 242	55.06 327	83.69 318

test_0402		72.68 296	69.54 300	82.09 298	88.67 241	71.81 278	92.72 256	86.77 325	61.52 325	62.21 308	83.91 282	43.22 319	93.76 285	34.60 344	72.23 250	80.72 334

v12		77.20 271	74.73 271	84.63 264	86.15 282	78.41 205	92.17 266	90.71 296	74.92 269	62.05 310	77.00 320	65.83 203	91.83 307	68.69 244	55.01 328	83.64 319

OpenMVS_ROB	68.52 20	73.02 295	69.57 299	83.37 287	80.54 320	71.82 277	93.60 231	88.22 316	62.37 321	61.98 311	83.15 293	35.31 335	95.47 243	45.08 334	75.88 232	82.82 324

MVS-HIRNet		71.36 301	67.00 304	84.46 271	90.58 213	69.74 298	79.15 331	87.74 319	46.09 343	61.96 312	50.50 345	45.14 314	95.64 233	53.74 315	88.11 145	88.00 266

v13		77.11 274	74.63 274	84.55 267	86.08 285	78.27 208	92.06 268	90.68 298	74.73 272	61.86 313	76.93 321	65.73 204	91.81 310	68.55 247	55.07 326	83.59 320

test20.03		72.36 298	71.15 292	75.98 318	77.79 326	59.16 329	92.40 262	89.35 307	74.09 279	61.50 314	84.32 279	48.09 306	85.54 339	50.63 325	62.15 310	83.24 322

PM-MVS		69.32 305	66.93 305	76.49 315	73.60 337	55.84 333	85.91 316	79.32 349	74.72 273	61.09 315	78.18 311	21.76 346	91.10 317	70.86 227	56.90 318	82.51 327

TDRefinement		69.20 306	65.78 308	79.48 308	66.04 345	62.21 321	88.21 298	86.12 327	62.92 319	61.03 316	85.61 263	33.23 337	94.16 277	55.82 311	53.02 332	82.08 331

ambc				76.02 317	68.11 342	51.43 339	64.97 347	89.59 304		60.49 317	74.49 325	17.17 350	92.46 294	61.50 284	52.85 333	84.17 309

pmmvs-eth3d		73.59 290	70.66 294	82.38 295	76.40 331	73.38 263	89.39 291	89.43 306	72.69 291	60.34 318	77.79 312	46.43 313	91.26 316	66.42 260	57.06 317	82.51 327

Test4		82.30 221	79.15 236	91.78 119	81.84 313	81.74 109	94.04 221	94.20 206	84.86 104	59.75 319	83.88 283	37.14 331	96.28 191	84.60 113	92.00 119	97.30 109

K. test v3		73.62 289	71.59 291	79.69 307	82.98 312	59.85 327	90.85 281	88.83 311	77.13 240	58.90 320	82.11 295	43.62 316	91.72 311	65.83 263	54.10 331	87.50 278

EG-PatchMatch MVS		74.92 285	72.02 289	83.62 284	83.76 310	73.28 265	93.62 230	92.04 273	68.57 307	58.88 321	83.80 284	31.87 340	95.57 239	56.97 300	78.67 221	82.00 332

lessismore_v0				79.98 306	80.59 319	58.34 330		80.87 345		58.49 322	83.46 290	43.10 320	93.89 281	63.11 281	48.68 337	87.72 269

N_pmnet		61.30 314	60.20 315	64.60 331	84.32 303	17.00 361	91.67 276	10.98 361	61.77 324	58.45 323	78.55 310	49.89 301	91.83 307	42.27 336	63.94 305	84.97 304

LP		68.54 309	63.92 311	82.39 294	87.93 248	71.79 279	72.37 345	86.01 330	55.89 337	58.33 324	71.46 336	49.58 303	90.10 325	32.25 346	61.48 311	85.27 299

TinyColmap		72.41 297	68.99 302	82.68 292	88.11 246	69.59 299	88.41 297	85.20 331	65.55 314	57.91 325	84.82 276	30.80 342	95.94 211	51.38 320	68.70 278	82.49 329

UnsupCasMVSNet_eth		73.25 293	70.57 295	81.30 300	77.53 327	66.33 310	87.24 306	93.89 227	80.38 193	57.90 326	81.59 298	42.91 321	90.56 320	65.18 266	48.51 338	87.01 284

MIMVSNet1		69.44 304	66.65 306	77.84 311	76.48 330	62.84 320	87.42 304	88.97 310	66.96 313	57.75 327	79.72 308	32.77 339	85.83 336	46.32 332	63.42 307	84.85 305

pmmvs3		65.75 311	62.18 314	76.45 316	67.12 343	64.54 313	88.68 295	85.05 332	54.77 341	57.54 328	73.79 326	29.40 344	86.21 335	55.49 312	47.77 340	78.62 336

new-patchmatchnet		68.85 307	65.93 307	77.61 312	73.57 338	63.94 317	90.11 284	88.73 314	71.62 298	55.08 329	73.60 327	40.84 326	87.22 332	51.35 322	48.49 339	81.67 333

UnsupCasMVSNet_bld		68.60 308	64.50 309	80.92 303	74.63 335	67.80 305	83.97 321	92.94 263	65.12 315	54.63 330	68.23 339	35.97 332	92.17 300	60.13 288	44.83 342	82.78 325

CMPMVS	54.94 21	75.71 283	74.56 276	79.17 310	79.69 322	55.98 332	89.59 286	93.30 257	60.28 330	53.85 331	89.07 208	47.68 310	96.33 189	76.55 183	81.02 206	85.22 300
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		66.18 310	63.18 312	75.18 320	76.27 332	61.74 323	83.79 322	84.66 333	56.64 336	51.57 332	71.85 334	31.29 341	87.93 329	49.98 326	62.55 309	75.86 339

testing_2		76.96 275	73.18 285	88.30 199	75.87 334	79.64 163	89.92 285	94.21 205	80.16 199	51.23 333	75.94 324	33.94 336	95.81 218	82.28 137	75.12 238	89.46 229

1111		65.60 312	64.33 310	69.41 326	68.26 340	45.11 346	87.06 307	87.32 321	54.99 338	51.20 334	73.45 328	63.57 220	85.70 337	36.53 341	56.59 319	77.42 338

.test1245		54.61 318	58.07 316	44.24 341	68.26 340	45.11 346	87.06 307	87.32 321	54.99 338	51.20 334	73.45 328	63.57 220	85.70 337	36.53 341	0.21 357	1.91 357

Anonymous20231211		61.03 315	56.76 317	73.82 322	71.24 339	53.47 337	87.60 303	81.65 344	44.19 344	51.08 336	71.82 335	20.79 347	88.46 328	35.45 343	47.07 341	79.52 335

test1235678		64.50 313	62.19 313	71.42 325	66.82 344	48.00 342	89.44 289	87.90 317	62.82 320	49.12 337	71.31 337	30.14 343	82.19 343	41.88 337	60.32 313	84.06 310

YYNet1		73.53 292	70.43 297	82.85 291	84.52 302	71.73 280	91.69 275	91.37 281	67.63 308	46.79 338	81.21 300	55.04 288	90.43 321	55.93 309	59.70 315	86.38 291

MDA-MVSNet_test_wron		73.54 291	70.43 297	82.86 290	84.55 300	71.85 276	91.74 274	91.32 283	67.63 308	46.73 339	81.09 301	55.11 287	90.42 322	55.91 310	59.76 314	86.31 292

MDA-MVSNet-bldmvs		71.45 300	67.94 303	81.98 299	85.33 295	68.50 304	92.35 263	88.76 313	70.40 302	42.99 340	81.96 296	46.57 312	91.31 315	48.75 330	54.39 330	86.11 295

test12356		58.24 316	56.06 318	64.77 329	60.65 346	39.42 352	82.78 325	84.34 336	57.47 335	42.65 341	69.10 338	19.21 348	81.18 344	38.97 340	49.40 335	73.69 340

DeepMVS_CX				64.06 332	78.53 325	43.26 348		68.11 354	69.94 303	38.55 342	76.14 323	18.53 349	79.34 345	43.72 335	41.62 346	69.57 345

LCM-MVSNet		52.52 320	48.24 321	65.35 328	47.63 355	41.45 349	72.55 344	83.62 340	31.75 347	37.66 343	57.92 343	9.19 359	76.76 348	49.26 328	44.60 343	77.84 337

testmv		54.58 319	51.53 320	63.74 333	53.58 351	40.82 350	83.26 323	83.92 338	54.07 342	36.35 344	61.26 340	14.80 352	77.07 346	33.00 345	43.53 345	73.33 341

FPMVS		55.09 317	52.93 319	61.57 334	55.98 347	40.51 351	83.11 324	83.41 341	37.61 346	34.95 345	71.95 333	14.40 353	76.95 347	29.81 348	65.16 301	67.25 346

PMMVS2		50.90 322	46.31 322	64.67 330	55.53 348	46.67 344	77.30 337	71.02 351	40.89 345	34.16 346	59.32 341	9.83 358	76.14 350	40.09 338	28.63 347	71.21 342

tmp_tt		41.54 325	41.93 325	40.38 342	20.10 360	26.84 357	61.93 348	59.09 357	14.81 355	28.51 347	80.58 302	35.53 333	48.33 358	63.70 278	13.11 354	45.96 352

no-one		51.12 321	45.81 323	67.03 327	53.16 353	52.22 338	75.21 339	80.40 346	54.89 340	28.26 348	48.50 347	15.54 351	82.81 342	39.29 339	17.06 350	66.07 347

Gipuma		45.11 324	42.05 324	54.30 337	80.69 318	51.30 340	35.80 353	83.81 339	28.13 349	27.94 349	34.53 351	11.41 357	76.70 349	21.45 351	54.65 329	34.90 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		46.22 323	41.28 326	61.04 335	39.91 358	46.25 345	70.59 346	76.18 350	58.87 333	23.09 350	48.00 348	12.58 355	66.54 353	28.65 349	13.62 353	70.35 343

PNet_i23d		41.20 326	38.13 327	50.41 338	55.23 349	36.24 355	73.80 343	65.45 356	29.75 348	21.36 351	47.05 349	3.43 360	63.23 354	28.17 350	18.92 349	51.76 349

MVE	35.65 22	33.85 330	29.49 333	46.92 340	41.86 356	36.28 354	50.45 351	56.52 358	18.75 354	18.28 352	37.84 350	2.41 362	58.41 355	18.71 352	20.62 348	46.06 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	34.80 23	39.19 327	35.53 328	50.18 339	29.72 359	30.30 356	59.60 350	66.20 355	26.06 350	17.91 353	49.53 346	3.12 361	74.09 351	18.19 353	49.40 335	46.14 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		32.70 331	32.39 330	33.65 343	53.35 352	25.70 358	74.07 341	53.33 359	21.08 352	17.17 354	33.63 353	11.85 356	54.84 356	12.98 354	14.04 352	20.42 354

EMVS		31.70 332	31.45 332	32.48 344	50.72 354	23.95 359	74.78 340	52.30 360	20.36 353	16.08 355	31.48 354	12.80 354	53.60 357	11.39 355	13.10 355	19.88 355

wuykxyi23d		37.75 328	31.85 331	55.46 336	40.00 357	38.01 353	59.81 349	69.47 352	25.46 351	12.42 356	30.55 355	2.06 363	67.08 352	31.81 347	15.03 351	61.29 348

wuyk23d		14.10 334	13.89 335	14.72 346	55.23 349	22.91 360	33.83 354	3.56 362	4.94 356	4.11 357	2.28 360	2.06 363	19.66 359	10.23 356	8.74 356	1.59 359

testmvs		9.92 335	12.94 336	0.84 348	0.65 361	0.29 363	93.78 226	0.39 363	0.42 357	2.85 358	15.84 358	0.17 366	0.30 361	2.18 357	0.21 357	1.91 357

test123		9.07 336	11.73 337	1.11 347	0.50 362	0.77 362	89.44 289	0.20 364	0.34 358	2.15 359	10.72 359	0.34 365	0.32 360	1.79 358	0.08 359	2.23 356

cdsmvs_eth3d_5k		21.43 333	28.57 334	0.00 349	0.00 363	0.00 364	0.00 355	95.93 128	0.00 359	0.00 360	97.66 51	63.57 220	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		5.92 338	7.89 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	71.04 162	0.00 362	0.00 359	0.00 360	0.00 360

pcd1.5k->3k		34.11 329	35.46 329	30.05 345	86.70 258	0.00 364	0.00 355	94.74 182	0.00 359	0.00 360	0.00 361	58.13 259	0.00 362	0.00 359	79.56 215	90.14 217

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

ab-mvs-re		8.11 337	10.81 338	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	97.30 71	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

GSMVS																97.54 94

test_part3							98.15 25		84.95 102		98.83 2		99.80 14	97.78 2

test_part1								96.77 53				89.33 6			98.95 12	99.18 10

sam_mvs1												77.59 87				97.54 94

sam_mvs												75.35 129

MTGPA								96.33 103

test_post1							85.88 317				30.24 356	73.77 141	95.07 259	73.89 205

test_post											33.80 352	76.17 107	95.97 206

patchmatchnet-post											77.09 318	77.78 86	95.39 245

MTMP								68.16 353

gm-plane-assit					92.27 180	79.64 163			84.47 116		95.15 124		97.93 118	85.81 104

test9_res														96.00 14	99.03 7	98.31 42

agg_prior2														94.30 28	99.00 9	98.57 31

test_prior4						82.34 94	97.75 46

test_prior				93.09 69	98.68 15	81.91 102		96.40 95					99.06 82			98.29 44

新几何2							96.42 142

旧先验1					97.39 66	79.58 165		96.54 81			98.08 32	84.00 26			97.42 58	97.62 91

无先验							96.87 110	96.78 52	77.39 236				99.52 44	79.95 150		98.43 36

原ACMM2							96.84 111

testdata2													99.48 49	76.45 185

segment_acmp												82.69 36

testdata1							95.57 181		87.44 59

plane_prior7					91.86 197	77.55 228

plane_prior6					91.98 192	77.92 221						64.77 215

plane_prior5								94.69 183					97.30 151	87.08 97	82.82 201	90.96 206

plane_prior4											94.15 142

plane_prior2							97.18 79		89.89 30

plane_prior1					91.95 195

plane_prior						77.96 218	97.52 59		90.36 27						82.96 193

n2								0.00 365

nn								0.00 365

door-mid								79.75 348

test11								96.50 85

door								80.13 347

HQP5-MVS						78.48 201

BP-MVS														87.67 94

HQP3-MVS								94.80 178							83.01 191

HQP2-MVS												65.40 210

NP-MVS					92.04 191	78.22 209					94.56 136

ACMMP++_ref															78.45 225

ACMMP++															79.05 218

Test By Simon												71.65 155

High-resolution multi-view benchmark