High-res multi-view results

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

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

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

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

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

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

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

APDe-MVS		99.02 1	98.84 1	99.55 1	99.57 24	98.96 3	99.39 5	98.93 36	97.38 17	99.41 3	99.54 1	96.66 6	99.84 42	98.86 2	99.85 2	99.87 1

CHOSEN 1792x2688		97.12 87	96.80 82	98.08 101	99.30 53	94.56 205	98.05 205	99.71 1	93.57 173	97.09 102	98.91 76	88.17 181	99.89 27	96.87 77	99.56 62	99.81 2

EI-MVSNet-Vis-set		98.47 28	98.39 15	98.69 62	99.46 33	96.49 97	98.30 177	98.69 94	97.21 28	98.84 28	99.36 16	95.41 40	99.78 74	98.62 6	99.65 44	99.80 3

ACMMP_Plus		98.61 13	98.30 25	99.55 1	99.62 22	98.95 4	98.82 87	98.81 61	95.80 72	99.16 13	99.47 4	95.37 41	99.92 13	97.89 32	99.75 30	99.79 4

Regformer-4		98.64 10	98.53 7	98.99 47	99.43 36	97.37 64	98.40 164	98.79 69	97.46 12	99.09 14	99.31 21	95.86 32	99.80 57	98.64 4	99.76 24	99.79 4

HPM-MVS		98.36 34	98.10 36	99.13 38	99.74 7	97.82 50	99.53 1	98.80 68	94.63 127	98.61 41	98.97 65	95.13 50	99.77 79	97.65 44	99.83 7	99.79 4

region2R		98.61 13	98.38 16	99.29 18	99.74 7	98.16 35	99.23 22	98.93 36	96.15 60	98.94 22	99.17 39	95.91 29	99.94 3	97.55 50	99.79 10	99.78 7

Regformer-3		98.59 16	98.50 11	98.86 57	99.43 36	97.05 75	98.40 164	98.68 97	97.43 13	99.06 15	99.31 21	95.80 33	99.77 79	98.62 6	99.76 24	99.78 7

XVS		98.70 5	98.49 12	99.34 13	99.70 15	98.35 23	99.29 14	98.88 47	97.40 14	98.46 46	99.20 35	95.90 30	99.89 27	97.85 34	99.74 33	99.78 7

X-MVStestdata		94.06 236	92.30 254	99.34 13	99.70 15	98.35 23	99.29 14	98.88 47	97.40 14	98.46 46	43.50 343	95.90 30	99.89 27	97.85 34	99.74 33	99.78 7

ACMMPR		98.59 16	98.36 18	99.29 18	99.74 7	98.15 36	99.23 22	98.95 33	96.10 65	98.93 26	99.19 38	95.70 34	99.94 3	97.62 45	99.79 10	99.78 7

PGM-MVS		98.49 27	98.23 32	99.27 23	99.72 11	98.08 39	98.99 58	99.49 5	95.43 86	99.03 16	99.32 20	95.56 36	99.94 3	96.80 79	99.77 18	99.78 7

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 12	99.22 72	98.43 17	99.10 46	98.87 49	97.38 17	99.35 5	99.40 6	97.78 1	99.87 35	97.77 39	99.85 2	99.78 7
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MPTG		98.55 22	98.25 29	99.46 6	99.76 1	98.64 9	98.55 146	98.74 79	97.27 25	98.02 65	99.39 7	94.81 55	99.96 1	97.91 29	99.79 10	99.77 14

MTAPA		98.58 18	98.29 26	99.46 6	99.76 1	98.64 9	98.90 68	98.74 79	97.27 25	98.02 65	99.39 7	94.81 55	99.96 1	97.91 29	99.79 10	99.77 14

mPP-MVS		98.51 26	98.26 28	99.25 24	99.75 3	98.04 40	99.28 16	98.81 61	96.24 58	98.35 53	99.23 29	95.46 39	99.94 3	97.42 55	99.81 8	99.77 14

HPM-MVS_fast		98.38 32	98.13 35	99.12 40	99.75 3	97.86 47	99.44 4	98.82 58	94.46 134	98.94 22	99.20 35	95.16 49	99.74 85	97.58 47	99.85 2	99.77 14

CP-MVS		98.57 20	98.36 18	99.19 28	99.66 19	97.86 47	99.34 11	98.87 49	95.96 68	98.60 42	99.13 44	96.05 23	99.94 3	97.77 39	99.86 1	99.77 14

HyFIR lowres test		96.90 95	96.49 98	98.14 95	99.33 43	95.56 140	97.38 259	99.65 2	92.34 220	97.61 91	98.20 139	89.29 139	99.10 160	96.97 65	97.60 145	99.77 14

test_part1								98.84 54				97.38 2			99.78 14	99.76 20

HFP-MVS		98.63 12	98.40 14	99.32 16	99.72 11	98.29 26	99.23 22	98.96 31	96.10 65	98.94 22	99.17 39	96.06 21	99.92 13	97.62 45	99.78 14	99.75 21

#test#		98.54 24	98.27 27	99.32 16	99.72 11	98.29 26	98.98 61	98.96 31	95.65 78	98.94 22	99.17 39	96.06 21	99.92 13	97.21 60	99.78 14	99.75 21

Regformer-1		98.66 8	98.51 10	99.12 40	99.35 38	97.81 51	98.37 166	98.76 75	97.49 10	99.20 11	99.21 32	96.08 20	99.79 69	98.42 16	99.73 35	99.75 21

Regformer-2		98.69 7	98.52 8	99.19 28	99.35 38	98.01 42	98.37 166	98.81 61	97.48 11	99.21 10	99.21 32	96.13 17	99.80 57	98.40 18	99.73 35	99.75 21

CPTT-MVS		97.72 56	97.32 63	98.92 53	99.64 20	97.10 74	99.12 44	98.81 61	92.34 220	98.09 59	99.08 54	93.01 81	99.92 13	96.06 99	99.77 18	99.75 21

MCST-MVS		98.65 9	98.37 17	99.48 5	99.60 23	98.87 6	98.41 163	98.68 97	97.04 38	98.52 45	98.80 84	96.78 5	99.83 43	97.93 28	99.61 49	99.74 26

APD-MVS		98.35 35	98.00 40	99.42 9	99.51 27	98.72 8	98.80 96	98.82 58	94.52 130	99.23 9	99.25 28	95.54 38	99.80 57	96.52 89	99.77 18	99.74 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		98.78 3	98.62 4	99.24 25	99.69 17	98.28 28	99.14 39	98.66 107	96.84 43	99.56 2	99.31 21	96.34 11	99.70 91	98.32 20	99.73 35	99.73 28

EI-MVSNet-UG-set		98.41 30	98.34 21	98.61 67	99.45 34	96.32 104	98.28 179	98.68 97	97.17 31	98.74 35	99.37 12	95.25 46	99.79 69	98.57 8	99.54 65	99.73 28

MP-MVS		98.33 38	98.01 39	99.28 20	99.75 3	98.18 34	99.22 28	98.79 69	96.13 62	97.92 74	99.23 29	94.54 60	99.94 3	96.74 81	99.78 14	99.73 28

APD-MVS_3200maxsize		98.53 25	98.33 24	99.15 37	99.50 28	97.92 46	99.15 38	98.81 61	96.24 58	99.20 11	99.37 12	95.30 44	99.80 57	97.73 41	99.67 40	99.72 31

DeepPCF-MVS	96.37 2	97.93 48	98.48 13	96.30 224	99.00 87	89.54 286	97.43 256	98.87 49	98.16 2	99.26 7	99.38 11	96.12 18	99.64 100	98.30 21	99.77 18	99.72 31

NCCC		98.61 13	98.35 20	99.38 10	99.28 61	98.61 11	98.45 158	98.76 75	97.82 3	98.45 49	98.93 73	96.65 7	99.83 43	97.38 57	99.41 77	99.71 33

3Dnovator+	94.38 6	97.43 72	96.78 85	99.38 10	97.83 164	98.52 12	99.37 7	98.71 91	97.09 37	92.99 243	99.13 44	89.36 137	99.89 27	96.97 65	99.57 56	99.71 33

ACMMP		98.23 41	97.95 41	99.09 42	99.74 7	97.62 56	99.03 55	99.41 6	95.98 67	97.60 92	99.36 16	94.45 65	99.93 9	97.14 61	98.85 98	99.70 35
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
HSP-MVS		98.70 5	98.52 8	99.24 25	99.75 3	98.23 29	99.26 17	98.58 120	97.52 7	99.41 3	98.78 85	96.00 24	99.79 69	97.79 38	99.59 53	99.69 36

test9_res														96.39 94	99.57 56	99.69 36

abl_6		98.30 40	98.03 38	99.13 38	99.56 25	97.76 52	99.13 42	98.82 58	96.14 61	99.26 7	99.37 12	93.33 77	99.93 9	96.96 67	99.67 40	99.69 36

CNVR-MVS		98.78 3	98.56 6	99.45 8	99.32 46	98.87 6	98.47 157	98.81 61	97.72 4	98.76 34	99.16 42	97.05 3	99.78 74	98.06 25	99.66 43	99.69 36

MVS_111021_HR		98.47 28	98.34 21	98.88 56	99.22 72	97.32 65	97.91 220	99.58 3	97.20 29	98.33 54	99.00 63	95.99 25	99.64 100	98.05 26	99.76 24	99.69 36

DeepC-MVS_fast	96.70 1	98.55 22	98.34 21	99.18 32	99.25 65	98.04 40	98.50 154	98.78 71	97.72 4	98.92 27	99.28 25	95.27 45	99.82 48	97.55 50	99.77 18	99.69 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
train_agg		97.97 44	97.52 54	99.33 15	99.31 48	98.50 13	97.92 217	98.73 84	92.98 193	97.74 82	98.68 94	96.20 13	99.80 57	96.59 85	99.57 56	99.68 42

agg_prior3		97.87 50	97.42 60	99.23 27	99.29 56	98.23 29	97.92 217	98.72 86	92.38 219	97.59 93	98.64 99	96.09 19	99.79 69	96.59 85	99.57 56	99.68 42

agg_prior2														95.87 106	99.57 56	99.68 42

CDPH-MVS		97.94 47	97.49 56	99.28 20	99.47 32	98.44 15	97.91 220	98.67 104	92.57 206	98.77 33	98.85 79	95.93 28	99.72 86	95.56 118	99.69 39	99.68 42

DP-MVS		96.59 105	95.93 114	98.57 69	99.34 40	96.19 108	98.70 121	98.39 154	89.45 287	94.52 177	99.35 18	91.85 102	99.85 40	92.89 191	98.88 95	99.68 42

MP-MVS-pluss		98.31 39	97.92 42	99.49 4	99.72 11	98.88 5	98.43 161	98.78 71	94.10 140	97.69 86	99.42 5	95.25 46	99.92 13	98.09 24	99.80 9	99.67 47
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MG-MVS		97.81 53	97.60 49	98.44 80	99.12 81	95.97 116	97.75 237	98.78 71	96.89 42	98.46 46	99.22 31	93.90 74	99.68 95	94.81 137	99.52 67	99.67 47

agg_prior1		97.95 46	97.51 55	99.28 20	99.30 53	98.38 18	97.81 232	98.72 86	93.16 187	97.57 94	98.66 97	96.14 16	99.81 50	96.63 84	99.56 62	99.66 49

HPM-MVS++		98.58 18	98.25 29	99.55 1	99.50 28	99.08 2	98.72 117	98.66 107	97.51 8	98.15 56	98.83 81	95.70 34	99.92 13	97.53 52	99.67 40	99.66 49

UA-Net		97.96 45	97.62 48	98.98 49	98.86 102	97.47 61	98.89 72	99.08 20	96.67 49	98.72 36	99.54 1	93.15 80	99.81 50	94.87 134	98.83 99	99.65 51

test_prior3		98.22 42	97.90 43	99.19 28	99.31 48	98.22 31	97.80 233	98.84 54	96.12 63	97.89 76	98.69 92	95.96 26	99.70 91	96.89 71	99.60 50	99.65 51

test_prior				99.19 28	99.31 48	98.22 31		98.84 54					99.70 91			99.65 51

SD-MVS		98.64 10	98.68 3	98.53 73	99.33 43	98.36 22	98.90 68	98.85 53	97.28 21	99.72 1	99.39 7	96.63 8	97.60 288	98.17 23	99.85 2	99.64 54

3Dnovator	94.51 5	97.46 67	96.93 78	99.07 43	97.78 166	97.64 54	99.35 10	99.06 21	97.02 39	93.75 222	99.16 42	89.25 140	99.92 13	97.22 59	99.75 30	99.64 54

test12				99.18 32	99.16 77	98.19 33		98.53 128		98.07 60		95.13 50	99.72 86		99.56 62	99.63 56

旧先验1					99.29 56	97.48 60		98.70 93			99.09 52	95.56 36			99.47 70	99.61 57

test222					99.23 71	97.17 73	97.40 257	98.66 107	88.68 294	98.05 61	98.96 69	94.14 70			99.53 66	99.61 57

1121		97.37 77	96.77 87	99.16 35	99.34 40	97.99 45	98.19 189	98.68 97	90.14 268	98.01 67	98.97 65	94.80 57	99.87 35	93.36 172	99.46 73	99.61 57

无先验							97.58 249	98.72 86	91.38 244				99.87 35	93.36 172		99.60 60

CVMVSNet		95.43 153	96.04 111	93.57 295	97.93 158	83.62 318	98.12 198	98.59 115	95.68 75	96.56 131	99.02 58	87.51 202	97.51 291	93.56 169	97.44 146	99.60 60

新几何1				99.16 35	99.34 40	98.01 42		98.69 94	90.06 270	98.13 57	98.95 71	94.60 59	99.89 27	91.97 213	99.47 70	99.59 62

PHI-MVS		98.34 36	98.06 37	99.18 32	99.15 79	98.12 38	99.04 54	99.09 19	93.32 182	98.83 30	99.10 48	96.54 9	99.83 43	97.70 43	99.76 24	99.59 62

testdata				98.26 89	99.20 75	95.36 147		98.68 97	91.89 230	98.60 42	99.10 48	94.44 66	99.82 48	94.27 151	99.44 75	99.58 64

Test_1112_low_res		96.34 114	95.66 127	98.36 85	98.56 124	95.94 120	97.71 239	98.07 213	92.10 226	94.79 172	97.29 206	91.75 103	99.56 115	94.17 153	96.50 164	99.58 64

1112_ss		96.63 102	96.00 113	98.50 75	98.56 124	96.37 101	98.18 193	98.10 208	92.92 195	94.84 168	98.43 115	92.14 95	99.58 112	94.35 148	96.51 163	99.56 66

PAPM_NR		97.46 67	97.11 71	98.50 75	99.50 28	96.41 100	98.63 133	98.60 114	95.18 104	97.06 106	98.06 147	94.26 69	99.57 113	93.80 163	98.87 97	99.52 67

CSCG		97.85 52	97.74 46	98.20 92	99.67 18	95.16 154	99.22 28	99.32 7	93.04 190	97.02 108	98.92 75	95.36 42	99.91 22	97.43 54	99.64 46	99.52 67

DeepC-MVS	95.98 3	97.88 49	97.58 50	98.77 59	99.25 65	96.93 79	98.83 85	98.75 78	96.96 41	96.89 116	99.50 3	90.46 125	99.87 35	97.84 36	99.76 24	99.52 67
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet		98.05 43	97.76 45	98.90 55	98.73 110	97.27 67	98.35 168	98.78 71	97.37 19	97.72 84	98.96 69	91.53 111	99.92 13	98.79 3	99.65 44	99.51 70

TSAR-MVS + GP.		98.38 32	98.24 31	98.81 58	99.22 72	97.25 70	98.11 200	98.29 167	97.19 30	98.99 21	99.02 58	96.22 12	99.67 96	98.52 14	98.56 111	99.51 70

原ACMM1				98.65 65	99.32 46	96.62 90		98.67 104	93.27 185	97.81 78	98.97 65	95.18 48	99.83 43	93.84 161	99.46 73	99.50 72

VNet		97.79 54	97.40 61	98.96 51	98.88 100	97.55 58	98.63 133	98.93 36	96.74 46	99.02 17	98.84 80	90.33 128	99.83 43	98.53 10	96.66 157	99.50 72

EPNet		97.28 80	96.87 81	98.51 74	94.98 304	96.14 109	98.90 68	97.02 283	98.28 1	95.99 155	99.11 46	91.36 112	99.89 27	96.98 64	99.19 86	99.50 72
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		97.70 57	97.46 58	98.44 80	99.27 62	95.91 127	98.63 133	99.16 17	94.48 133	97.67 87	98.88 77	92.80 83	99.91 22	97.11 62	99.12 88	99.50 72

MVS_111021_LR		98.34 36	98.23 32	98.67 64	99.27 62	96.90 81	97.95 215	99.58 3	97.14 33	98.44 50	99.01 62	95.03 52	99.62 105	97.91 29	99.75 30	99.50 72

WTY-MVS		97.37 77	96.92 79	98.72 61	98.86 102	96.89 83	98.31 175	98.71 91	95.26 100	97.67 87	98.56 106	92.21 93	99.78 74	95.89 104	96.85 154	99.48 77

MSLP-MVS++		98.56 21	98.57 5	98.55 71	99.26 64	96.80 84	98.71 118	99.05 23	97.28 21	98.84 28	99.28 25	96.47 10	99.40 131	98.52 14	99.70 38	99.47 78

114514_t		96.93 93	96.27 104	98.92 53	99.50 28	97.63 55	98.85 81	98.90 42	84.80 315	97.77 79	99.11 46	92.84 82	99.66 97	94.85 135	99.77 18	99.47 78

IS-MVSNet		97.22 82	96.88 80	98.25 90	98.85 104	96.36 102	99.19 34	97.97 220	95.39 88	97.23 99	98.99 64	91.11 116	98.93 181	94.60 141	98.59 109	99.47 78

PAPR		96.84 97	96.24 106	98.65 65	98.72 112	96.92 80	97.36 263	98.57 121	93.33 181	96.67 126	97.57 189	94.30 68	99.56 115	91.05 234	98.59 109	99.47 78

LFMVS		95.86 129	94.98 151	98.47 78	98.87 101	96.32 104	98.84 84	96.02 312	93.40 179	98.62 40	99.20 35	74.99 314	99.63 103	97.72 42	97.20 149	99.46 82

Vis-MVSNet (Re-imp)		96.87 96	96.55 95	97.83 113	98.73 110	95.46 144	99.20 32	98.30 165	94.96 115	96.60 130	98.87 78	90.05 132	98.59 210	93.67 166	98.60 108	99.46 82

Vis-MVSNet		97.42 73	97.11 71	98.34 86	98.66 117	96.23 107	99.22 28	99.00 26	96.63 51	98.04 63	99.21 32	88.05 186	99.35 136	96.01 102	99.21 85	99.45 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS_0304		97.70 57	97.25 65	99.07 43	98.90 96	97.83 49	98.20 185	98.74 79	97.51 8	98.03 64	99.06 56	86.12 224	99.93 9	99.02 1	99.64 46	99.44 85

DELS-MVS		98.40 31	98.20 34	98.99 47	99.00 87	97.66 53	97.75 237	98.89 44	97.71 6	98.33 54	98.97 65	94.97 53	99.88 34	98.42 16	99.76 24	99.42 86
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
sss		97.39 75	96.98 77	98.61 67	98.60 123	96.61 92	98.22 183	98.93 36	93.97 148	98.01 67	98.48 112	91.98 100	99.85 40	96.45 91	98.15 127	99.39 87

EPP-MVSNet		97.46 67	97.28 64	97.99 106	98.64 119	95.38 146	99.33 13	98.31 162	93.61 172	97.19 100	99.07 55	94.05 71	99.23 143	96.89 71	98.43 118	99.37 88

MVSFormer		97.57 64	97.49 56	97.84 112	98.07 149	95.76 133	99.47 2	98.40 152	94.98 113	98.79 31	98.83 81	92.34 87	98.41 245	96.91 69	99.59 53	99.34 89

jason		97.32 79	97.08 73	98.06 104	97.45 188	95.59 137	97.87 227	97.91 223	94.79 120	98.55 44	98.83 81	91.12 115	99.23 143	97.58 47	99.60 50	99.34 89
jason: jason.
QAPM		96.29 116	95.40 129	98.96 51	97.85 163	97.60 57	99.23 22	98.93 36	89.76 279	93.11 240	99.02 58	89.11 144	99.93 9	91.99 212	99.62 48	99.34 89

mvs_anonymous		96.70 101	96.53 97	97.18 153	98.19 142	93.78 226	98.31 175	98.19 182	94.01 144	94.47 179	98.27 134	92.08 98	98.46 230	97.39 56	97.91 133	99.31 92

lupinMVS		97.44 71	97.22 68	98.12 98	98.07 149	95.76 133	97.68 242	97.76 227	94.50 131	98.79 31	98.61 100	92.34 87	99.30 137	97.58 47	99.59 53	99.31 92

CDS-MVSNet		96.99 91	96.69 89	97.90 110	98.05 152	95.98 112	98.20 185	98.33 161	93.67 170	96.95 109	98.49 111	93.54 75	98.42 238	95.24 130	97.74 142	99.31 92
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Patchmatch-RL test		91.49 276	90.85 268	93.41 296	91.37 322	84.40 316	92.81 330	95.93 316	91.87 232	87.25 296	94.87 296	88.99 146	96.53 315	92.54 200	82.00 313	99.30 95

BH-RMVSNet		95.92 127	95.32 137	97.69 123	98.32 134	94.64 197	98.19 189	97.45 256	94.56 128	96.03 153	98.61 100	85.02 243	99.12 153	90.68 238	99.06 89	99.30 95

Patchmatch-test		94.42 215	93.68 224	96.63 192	97.60 175	91.76 259	94.83 322	97.49 253	89.45 287	94.14 207	97.10 216	88.99 146	98.83 194	85.37 305	98.13 128	99.29 97

TAMVS		97.02 90	96.79 84	97.70 122	98.06 151	95.31 151	98.52 149	98.31 162	93.95 149	97.05 107	98.61 100	93.49 76	98.52 221	95.33 124	97.81 138	99.29 97

PVSNet_Blended		97.38 76	97.12 70	98.14 95	99.25 65	95.35 149	97.28 269	99.26 8	93.13 188	97.94 72	98.21 138	92.74 84	99.81 50	96.88 74	99.40 79	99.27 99

PatchmatchNet		95.71 136	95.52 128	96.29 225	97.58 177	90.72 273	96.84 290	97.52 241	94.06 142	97.08 103	96.96 236	89.24 141	98.90 186	92.03 211	98.37 119	99.26 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CHOSEN 280x420		97.18 84	97.18 69	97.20 151	98.81 106	93.27 238	95.78 312	99.15 18	95.25 101	96.79 124	98.11 144	92.29 89	99.07 163	98.56 9	99.85 2	99.25 101

Patchmatch-test1		95.32 165	94.97 153	96.35 220	97.67 170	91.29 266	97.33 266	97.60 233	94.68 122	96.92 114	96.95 237	83.97 267	98.50 224	91.33 229	98.32 122	99.25 101

PLC	95.07 4	97.20 83	96.78 85	98.44 80	99.29 56	96.31 106	98.14 195	98.76 75	92.41 217	96.39 146	98.31 130	94.92 54	99.78 74	94.06 156	98.77 102	99.23 103
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LCM-MVSNet-Re		95.22 169	95.32 137	94.91 272	98.18 144	87.85 309	98.75 108	95.66 319	95.11 107	88.96 290	96.85 250	90.26 130	97.65 286	95.65 116	98.44 116	99.22 104

Effi-MVS+		97.12 87	96.69 89	98.39 84	98.19 142	96.72 88	97.37 261	98.43 149	93.71 163	97.65 90	98.02 149	92.20 94	99.25 141	96.87 77	97.79 139	99.19 105

alignmvs		97.56 65	97.07 74	99.01 46	98.66 117	98.37 21	98.83 85	98.06 215	96.74 46	98.00 69	97.65 182	90.80 122	99.48 129	98.37 19	96.56 161	99.19 105

DP-MVS Recon		97.86 51	97.46 58	99.06 45	99.53 26	98.35 23	98.33 170	98.89 44	92.62 203	98.05 61	98.94 72	95.34 43	99.65 98	96.04 100	99.42 76	99.19 105

OMC-MVS		97.55 66	97.34 62	98.20 92	99.33 43	95.92 125	98.28 179	98.59 115	95.52 83	97.97 70	99.10 48	93.28 79	99.49 125	95.09 132	98.88 95	99.19 105

MDTV_nov1_ep13_2view						84.26 317	96.89 287		90.97 258	97.90 75		89.89 133		93.91 159		99.18 109

MVS_Test		97.28 80	97.00 76	98.13 97	98.33 133	95.97 116	98.74 112	98.07 213	94.27 137	98.44 50	98.07 146	92.48 86	99.26 140	96.43 92	98.19 126	99.16 110

ab-mvs		96.42 111	95.71 123	98.55 71	98.63 120	96.75 87	97.88 226	98.74 79	93.84 154	96.54 135	98.18 140	85.34 240	99.75 83	95.93 103	96.35 171	99.15 111

PVSNet	91.96 18	96.35 113	96.15 108	96.96 166	99.17 76	92.05 254	96.08 304	98.68 97	93.69 166	97.75 81	97.80 171	88.86 154	99.69 94	94.26 152	99.01 90	99.15 111

tpm		94.13 232	93.80 214	95.12 268	96.50 240	87.91 308	97.44 254	95.89 317	92.62 203	96.37 147	96.30 270	84.13 265	98.30 258	93.24 175	91.66 245	99.14 113

F-COLMAP		97.09 89	96.80 82	97.97 107	99.45 34	94.95 166	98.55 146	98.62 113	93.02 191	96.17 150	98.58 105	94.01 72	99.81 50	93.95 158	98.90 94	99.14 113

PMMVS		96.60 103	96.33 102	97.41 143	97.90 160	93.93 222	97.35 264	98.41 150	92.84 199	97.76 80	97.45 195	91.10 117	99.20 146	96.26 95	97.91 133	99.11 115

GA-MVS		94.81 190	94.03 200	97.14 155	97.15 208	93.86 224	96.76 292	97.58 234	94.00 145	94.76 173	97.04 228	80.91 284	98.48 225	91.79 217	96.25 180	99.09 116

EPNet_dtu		95.21 170	94.95 154	95.99 234	96.17 270	90.45 278	98.16 194	97.27 272	96.77 44	93.14 239	98.33 128	90.34 127	98.42 238	85.57 302	98.81 101	99.09 116
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAPA-MVS	93.98 7	95.35 162	94.56 173	97.74 118	99.13 80	94.83 183	98.33 170	98.64 112	86.62 303	96.29 148	98.61 100	94.00 73	99.29 139	80.00 315	99.41 77	99.09 116
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		97.67 59	97.23 67	98.98 49	98.70 113	98.38 18	99.34 11	98.39 154	96.76 45	97.67 87	97.40 197	92.26 90	99.49 125	98.28 22	96.28 178	99.08 119

VDD-MVS		95.82 131	95.23 141	97.61 133	98.84 105	93.98 221	98.68 126	97.40 261	95.02 112	97.95 71	99.34 19	74.37 319	99.78 74	98.64 4	96.80 155	99.08 119

Test4		92.21 262	90.34 278	97.82 115	92.83 318	95.87 131	97.94 216	98.05 218	94.50 131	82.12 321	94.48 299	59.54 336	98.54 214	95.39 123	98.22 124	99.06 121

ADS-MVSNet2		94.58 208	94.40 181	95.11 269	98.00 153	88.74 297	96.04 305	97.30 269	90.15 266	96.47 143	96.64 259	87.89 190	97.56 290	90.08 248	97.06 150	99.02 122

ADS-MVSNet		95.00 176	94.45 179	96.63 192	98.00 153	91.91 256	96.04 305	97.74 229	90.15 266	96.47 143	96.64 259	87.89 190	98.96 176	90.08 248	97.06 150	99.02 122

CNLPA		97.45 70	97.03 75	98.73 60	99.05 82	97.44 63	98.07 204	98.53 128	95.32 98	96.80 123	98.53 107	93.32 78	99.72 86	94.31 150	99.31 83	99.02 122

test_normal		94.72 197	93.59 228	98.11 99	95.30 301	95.95 119	97.91 220	97.39 263	94.64 126	85.70 305	95.88 283	80.52 289	99.36 135	96.69 82	98.30 123	99.01 125

AdaColmap		97.15 86	96.70 88	98.48 77	99.16 77	96.69 89	98.01 209	98.89 44	94.44 135	96.83 119	98.68 94	90.69 123	99.76 81	94.36 147	99.29 84	98.98 126

Fast-Effi-MVS+		96.28 118	95.70 124	98.03 105	98.29 135	95.97 116	98.58 139	98.25 173	91.74 234	95.29 162	97.23 209	91.03 119	99.15 149	92.90 189	97.96 132	98.97 127

DI_MVS_plusplus_test		94.74 196	93.62 226	98.09 100	95.34 300	95.92 125	98.09 203	97.34 265	94.66 125	85.89 302	95.91 282	80.49 290	99.38 134	96.66 83	98.22 124	98.97 127

EPMVS		94.99 177	94.48 175	96.52 207	97.22 201	91.75 260	97.23 271	91.66 340	94.11 139	97.28 98	96.81 252	85.70 233	98.84 192	93.04 182	97.28 148	98.97 127

LS3D		97.16 85	96.66 92	98.68 63	98.53 127	97.19 72	98.93 66	98.90 42	92.83 200	95.99 155	99.37 12	92.12 96	99.87 35	93.67 166	99.57 56	98.97 127

HY-MVS	93.96 8	96.82 98	96.23 107	98.57 69	98.46 128	97.00 76	98.14 195	98.21 178	93.95 149	96.72 125	97.99 153	91.58 106	99.76 81	94.51 145	96.54 162	98.95 131

MIMVSNet		93.26 251	92.21 255	96.41 216	97.73 169	93.13 243	95.65 313	97.03 282	91.27 253	94.04 212	96.06 279	75.33 312	97.19 296	86.56 295	96.23 181	98.92 132

diffmvs		96.32 115	95.74 118	98.07 103	98.26 136	96.14 109	98.53 148	98.23 176	90.10 269	96.88 117	97.73 174	90.16 131	99.15 149	93.90 160	97.85 137	98.91 133

TESTMET0.1,1		94.18 228	93.69 223	95.63 247	96.92 218	89.12 292	96.91 283	94.78 328	93.17 186	94.88 167	96.45 266	78.52 298	98.92 182	93.09 179	98.50 113	98.85 134

dp		94.15 231	93.90 209	94.90 273	97.31 196	86.82 314	96.97 279	97.19 276	91.22 255	96.02 154	96.61 261	85.51 236	99.02 171	90.00 252	94.30 198	98.85 134

PAPM		94.95 181	94.00 202	97.78 117	97.04 212	95.65 136	96.03 307	98.25 173	91.23 254	94.19 204	97.80 171	91.27 114	98.86 191	82.61 310	97.61 144	98.84 136

VDDNet		95.36 161	94.53 174	97.86 111	98.10 148	95.13 156	98.85 81	97.75 228	90.46 261	98.36 52	99.39 7	73.27 321	99.64 100	97.98 27	96.58 160	98.81 137

CostFormer		94.95 181	94.73 166	95.60 248	97.28 197	89.06 293	97.53 251	96.89 296	89.66 283	96.82 121	96.72 255	86.05 227	98.95 180	95.53 119	96.13 185	98.79 138

UGNet		96.78 99	96.30 103	98.19 94	98.24 137	95.89 129	98.88 74	98.93 36	97.39 16	96.81 122	97.84 165	82.60 276	99.90 25	96.53 88	99.49 68	98.79 138
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
test-LLR		95.10 174	94.87 161	95.80 242	96.77 226	89.70 284	96.91 283	95.21 323	95.11 107	94.83 170	95.72 288	87.71 196	98.97 173	93.06 180	98.50 113	98.72 140

test-mter		94.08 234	93.51 234	95.80 242	96.77 226	89.70 284	96.91 283	95.21 323	92.89 197	94.83 170	95.72 288	77.69 302	98.97 173	93.06 180	98.50 113	98.72 140

DWT-MVSNet_test		94.82 189	94.36 182	96.20 228	97.35 194	90.79 271	98.34 169	96.57 307	92.91 196	95.33 161	96.44 267	82.00 278	99.12 153	94.52 144	95.78 192	98.70 142

MAR-MVS		96.91 94	96.40 100	98.45 79	98.69 115	96.90 81	98.66 131	98.68 97	92.40 218	97.07 105	97.96 154	91.54 110	99.75 83	93.68 165	98.92 93	98.69 143
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
BH-untuned		95.95 125	95.72 120	96.65 189	98.55 126	92.26 251	98.23 182	97.79 226	93.73 161	94.62 174	98.01 151	88.97 150	99.00 172	93.04 182	98.51 112	98.68 144

PCF-MVS	93.45 11	94.68 201	93.43 237	98.42 83	98.62 121	96.77 86	95.48 314	98.20 181	84.63 316	93.34 232	98.32 129	88.55 173	99.81 50	84.80 306	98.96 92	98.68 144
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CANet_DTU		96.96 92	96.55 95	98.21 91	98.17 146	96.07 111	97.98 212	98.21 178	97.24 27	97.13 101	98.93 73	86.88 213	99.91 22	95.00 133	99.37 81	98.66 146

PatchMatch-RL		96.59 105	96.03 112	98.27 88	99.31 48	96.51 96	97.91 220	99.06 21	93.72 162	96.92 114	98.06 147	88.50 176	99.65 98	91.77 219	99.00 91	98.66 146

tpmrst		95.63 140	95.69 125	95.44 255	97.54 180	88.54 302	96.97 279	97.56 235	93.50 175	97.52 96	96.93 243	89.49 134	99.16 148	95.25 129	96.42 166	98.64 148

IB-MVS	91.98 17	93.27 250	91.97 257	97.19 152	97.47 184	93.41 237	97.09 277	95.99 313	93.32 182	92.47 256	95.73 286	78.06 300	99.53 122	94.59 142	82.98 311	98.62 149
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
DSMNet-mixed		92.52 259	92.58 250	92.33 303	94.15 312	82.65 322	98.30 177	94.26 333	89.08 292	92.65 249	95.73 286	85.01 244	95.76 320	86.24 297	97.76 141	98.59 150

tpm2		94.19 226	93.76 219	95.46 253	97.23 200	89.04 294	97.31 268	96.85 299	87.08 302	96.21 149	96.79 253	83.75 272	98.74 200	92.43 203	96.23 181	98.59 150

MSDG		95.93 126	95.30 139	97.83 113	98.90 96	95.36 147	96.83 291	98.37 157	91.32 249	94.43 187	98.73 91	90.27 129	99.60 106	90.05 250	98.82 100	98.52 152

tpmp4_e23		93.91 240	93.42 239	95.38 261	97.62 173	88.59 301	97.52 252	97.34 265	87.94 298	94.17 206	96.79 253	82.91 274	99.05 164	90.62 240	95.91 189	98.50 153

PatchT		93.06 255	91.97 257	96.35 220	96.69 232	92.67 247	94.48 325	97.08 278	86.62 303	97.08 103	92.23 325	87.94 188	97.90 279	78.89 319	96.69 156	98.49 154

CR-MVSNet		94.76 192	94.15 192	96.59 197	97.00 213	93.43 235	94.96 318	97.56 235	92.46 207	96.93 112	96.24 271	88.15 182	97.88 283	87.38 290	96.65 158	98.46 155

RPMNet		92.52 259	91.17 262	96.59 197	97.00 213	93.43 235	94.96 318	97.26 273	82.27 322	96.93 112	92.12 326	86.98 211	97.88 283	76.32 324	96.65 158	98.46 155

PatchFormer-LS_test		95.47 150	95.27 140	96.08 233	97.59 176	90.66 274	98.10 202	97.34 265	93.98 147	96.08 151	96.15 277	87.65 200	99.12 153	95.27 128	95.24 195	98.44 157

view600		95.60 143	94.93 155	97.62 128	99.05 82	94.85 172	99.09 47	97.01 285	95.36 92	96.52 137	97.37 198	84.55 251	99.59 107	89.07 269	96.39 167	98.40 158

view800		95.60 143	94.93 155	97.62 128	99.05 82	94.85 172	99.09 47	97.01 285	95.36 92	96.52 137	97.37 198	84.55 251	99.59 107	89.07 269	96.39 167	98.40 158

conf0.05thres1000		95.60 143	94.93 155	97.62 128	99.05 82	94.85 172	99.09 47	97.01 285	95.36 92	96.52 137	97.37 198	84.55 251	99.59 107	89.07 269	96.39 167	98.40 158

tfpn		95.60 143	94.93 155	97.62 128	99.05 82	94.85 172	99.09 47	97.01 285	95.36 92	96.52 137	97.37 198	84.55 251	99.59 107	89.07 269	96.39 167	98.40 158

thres600view7		95.49 149	94.77 164	97.67 125	98.98 90	95.02 159	98.85 81	96.90 293	95.38 89	96.63 127	96.90 245	84.29 258	99.59 107	88.65 278	96.33 172	98.40 158

thres400		95.38 158	94.62 170	97.65 127	98.94 94	94.98 163	98.68 126	96.93 291	95.33 96	96.55 133	96.53 262	84.23 262	99.56 115	88.11 284	96.29 174	98.40 158

TR-MVS		94.94 183	94.20 189	97.17 154	97.75 167	94.14 218	97.59 248	97.02 283	92.28 224	95.75 157	97.64 184	83.88 269	98.96 176	89.77 254	96.15 184	98.40 158

JIA-IIPM		93.35 247	92.49 251	95.92 236	96.48 242	90.65 275	95.01 317	96.96 289	85.93 309	96.08 151	87.33 330	87.70 198	98.78 199	91.35 228	95.58 193	98.34 165

PVSNet_0	88.72 19	91.28 278	90.03 281	95.00 271	97.99 155	87.29 312	94.84 321	98.50 137	92.06 227	89.86 282	95.19 292	79.81 293	99.39 133	92.27 204	69.79 335	98.33 166

1314		96.25 120	95.73 119	97.79 116	97.13 209	95.55 142	98.19 189	98.59 115	93.47 176	92.03 265	97.82 169	91.33 113	99.49 125	94.62 140	98.44 116	98.32 167

RPSCF		94.87 185	95.40 129	93.26 299	98.89 99	82.06 324	98.33 170	98.06 215	90.30 265	96.56 131	99.26 27	87.09 208	99.49 125	93.82 162	96.32 173	98.24 168

tpmvs		94.60 205	94.36 182	95.33 264	97.46 185	88.60 300	96.88 288	97.68 230	91.29 251	93.80 221	96.42 268	88.58 170	99.24 142	91.06 232	96.04 188	98.17 169

BH-w/o		95.38 158	95.08 147	96.26 226	98.34 132	91.79 258	97.70 240	97.43 258	92.87 198	94.24 201	97.22 210	88.66 169	98.84 192	91.55 223	97.70 143	98.16 170

tpm cat1		93.36 246	92.80 246	95.07 270	97.58 177	87.97 307	96.76 292	97.86 224	82.17 323	93.53 226	96.04 280	86.13 223	99.13 152	89.24 266	95.87 190	98.10 171

MVS		94.67 202	93.54 231	98.08 101	96.88 222	96.56 94	98.19 189	98.50 137	78.05 329	92.69 248	98.02 149	91.07 118	99.63 103	90.09 247	98.36 120	98.04 172

conf200view11		95.40 157	94.70 167	97.50 139	98.98 90	94.92 167	98.87 75	96.90 293	95.38 89	96.61 128	96.88 248	84.29 258	99.56 115	88.11 284	96.29 174	98.02 173

AllTest		95.24 168	94.65 169	96.99 163	99.25 65	93.21 241	98.59 137	98.18 185	91.36 245	93.52 227	98.77 87	84.67 248	99.72 86	89.70 258	97.87 135	98.02 173

TestCases				96.99 163	99.25 65	93.21 241		98.18 185	91.36 245	93.52 227	98.77 87	84.67 248	99.72 86	89.70 258	97.87 135	98.02 173

gg-mvs-nofinetune		92.21 262	90.58 276	97.13 156	96.75 229	95.09 157	95.85 310	89.40 343	85.43 312	94.50 178	81.98 334	80.80 287	98.40 251	92.16 205	98.33 121	97.88 176

mvs-test1		96.60 103	96.68 91	96.37 218	97.89 161	91.81 257	98.56 144	98.10 208	96.57 52	96.52 137	97.94 156	90.81 120	99.45 130	95.72 111	98.01 130	97.86 177

thres100view900		95.38 158	94.70 167	97.41 143	98.98 90	94.92 167	98.87 75	96.90 293	95.38 89	96.61 128	96.88 248	84.29 258	99.56 115	88.11 284	96.29 174	97.76 178

tfpn200view9		95.32 165	94.62 170	97.43 142	98.94 94	94.98 163	98.68 126	96.93 291	95.33 96	96.55 133	96.53 262	84.23 262	99.56 115	88.11 284	96.29 174	97.76 178

XVG-OURS-SEG-HR		96.51 108	96.34 101	97.02 162	98.77 108	93.76 227	97.79 235	98.50 137	95.45 85	96.94 111	99.09 52	87.87 192	99.55 121	96.76 80	95.83 191	97.74 180

OpenMVS	93.04 13	95.83 130	95.00 149	98.32 87	97.18 206	97.32 65	99.21 31	98.97 29	89.96 272	91.14 271	99.05 57	86.64 216	99.92 13	93.38 171	99.47 70	97.73 181

testgi		93.06 255	92.45 252	94.88 274	96.43 244	89.90 281	98.75 108	97.54 240	95.60 79	91.63 268	97.91 158	74.46 318	97.02 298	86.10 298	93.67 215	97.72 182

tfpn1000		95.72 134	95.11 145	97.58 134	99.00 87	95.73 135	99.24 20	95.49 321	94.08 141	96.87 118	97.45 195	85.81 231	99.30 137	91.78 218	96.22 183	97.71 183

XVG-OURS		96.55 107	96.41 99	96.99 163	98.75 109	93.76 227	97.50 253	98.52 130	95.67 76	96.83 119	99.30 24	88.95 151	99.53 122	95.88 105	96.26 179	97.69 184

cascas		94.63 204	93.86 211	96.93 169	96.91 220	94.27 215	96.00 308	98.51 132	85.55 311	94.54 176	96.23 273	84.20 264	98.87 189	95.80 109	96.98 153	97.66 185

test0.0.03 1		94.08 234	93.51 234	95.80 242	95.53 296	92.89 246	97.38 259	95.97 314	95.11 107	92.51 255	96.66 257	87.71 196	96.94 299	87.03 293	93.67 215	97.57 186

LP		91.12 280	89.99 282	94.53 283	96.35 252	88.70 298	93.86 329	97.35 264	84.88 314	90.98 273	94.77 297	84.40 257	97.43 292	75.41 327	91.89 242	97.47 187

MVS-HIRNet		89.46 292	88.40 294	92.64 301	97.58 177	82.15 323	94.16 328	93.05 339	75.73 331	90.90 274	82.52 333	79.42 295	98.33 253	83.53 308	98.68 103	97.43 188

xiu_mvs_v2_base		97.66 60	97.70 47	97.56 136	98.61 122	95.46 144	97.44 254	98.46 142	97.15 32	98.65 39	98.15 141	94.33 67	99.80 57	97.84 36	98.66 107	97.41 189

Effi-MVS+-dtu		96.29 116	96.56 94	95.51 249	97.89 161	90.22 280	98.80 96	98.10 208	96.57 52	96.45 145	96.66 257	90.81 120	98.91 183	95.72 111	97.99 131	97.40 190

PS-MVSNAJ		97.73 55	97.77 44	97.62 128	98.68 116	95.58 138	97.34 265	98.51 132	97.29 20	98.66 38	97.88 161	94.51 61	99.90 25	97.87 33	99.17 87	97.39 191

thres200		95.25 167	94.57 172	97.28 149	98.81 106	94.92 167	98.20 185	97.11 277	95.24 103	96.54 135	96.22 275	84.58 250	99.53 122	87.93 288	96.50 164	97.39 191

tfpnview11		95.50 148	94.84 163	97.51 138	98.90 96	95.93 123	99.17 35	95.70 318	93.42 178	96.50 142	97.16 212	86.12 224	99.22 145	90.51 242	96.06 187	97.37 193

xiu_mvs_v1_base_debu		97.60 61	97.56 51	97.72 119	98.35 129	95.98 112	97.86 228	98.51 132	97.13 34	99.01 18	98.40 117	91.56 107	99.80 57	98.53 10	98.68 103	97.37 193

xiu_mvs_v1_base		97.60 61	97.56 51	97.72 119	98.35 129	95.98 112	97.86 228	98.51 132	97.13 34	99.01 18	98.40 117	91.56 107	99.80 57	98.53 10	98.68 103	97.37 193

xiu_mvs_v1_base_debi		97.60 61	97.56 51	97.72 119	98.35 129	95.98 112	97.86 228	98.51 132	97.13 34	99.01 18	98.40 117	91.56 107	99.80 57	98.53 10	98.68 103	97.37 193

API-MVS		97.41 74	97.25 65	97.91 109	98.70 113	96.80 84	98.82 87	98.69 94	94.53 129	98.11 58	98.28 131	94.50 64	99.57 113	94.12 155	99.49 68	97.37 193

Fast-Effi-MVS+-dtu		95.87 128	95.85 116	95.91 237	97.74 168	91.74 261	98.69 122	98.15 193	95.56 81	94.92 166	97.68 181	88.98 149	98.79 198	93.19 177	97.78 140	97.20 198

COLMAP_ROB	93.27 12	95.33 164	94.87 161	96.71 178	99.29 56	93.24 240	98.58 139	98.11 203	89.92 275	93.57 225	99.10 48	86.37 220	99.79 69	90.78 236	98.10 129	97.09 199
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pcd1.5k->3k		39.42 321	41.78 322	32.35 334	96.17 270	0.00 353	0.00 343	98.54 125	0.00 347	0.00 349	0.00 349	87.78 195	0.00 350	0.00 347	93.56 219	97.06 200

PS-MVSNAJss		96.43 110	96.26 105	96.92 171	95.84 286	95.08 158	99.16 37	98.50 137	95.87 70	93.84 220	98.34 127	94.51 61	98.61 207	96.88 74	93.45 222	97.06 200

nrg030		96.28 118	95.72 120	97.96 108	96.90 221	98.15 36	99.39 5	98.31 162	95.47 84	94.42 188	98.35 123	92.09 97	98.69 201	97.50 53	89.05 267	97.04 202

FIs		96.51 108	96.12 109	97.67 125	97.13 209	97.54 59	99.36 8	99.22 14	95.89 69	94.03 213	98.35 123	91.98 100	98.44 235	96.40 93	92.76 232	97.01 203

FC-MVSNet-test		96.42 111	96.05 110	97.53 137	96.95 216	97.27 67	99.36 8	99.23 12	95.83 71	93.93 215	98.37 121	92.00 99	98.32 254	96.02 101	92.72 233	97.00 204

EU-MVSNet		93.66 243	94.14 193	92.25 304	95.96 280	83.38 319	98.52 149	98.12 198	94.69 121	92.61 250	98.13 143	87.36 206	96.39 317	91.82 216	90.00 255	96.98 205

VPNet		94.99 177	94.19 190	97.40 145	97.16 207	96.57 93	98.71 118	98.97 29	95.67 76	94.84 168	98.24 137	80.36 291	98.67 204	96.46 90	87.32 292	96.96 206

XXY-MVS		95.20 171	94.45 179	97.46 140	96.75 229	96.56 94	98.86 80	98.65 111	93.30 184	93.27 233	98.27 134	84.85 247	98.87 189	94.82 136	91.26 249	96.96 206

TranMVSNet+NR-MVSNet		95.14 173	94.48 175	97.11 158	96.45 243	96.36 102	99.03 55	99.03 24	95.04 111	93.58 224	97.93 157	88.27 179	98.03 272	94.13 154	86.90 299	96.95 208

HQP_MVS		96.14 121	95.90 115	96.85 172	97.42 189	94.60 203	98.80 96	98.56 122	97.28 21	95.34 159	98.28 131	87.09 208	99.03 169	96.07 97	94.27 199	96.92 209

plane_prior5								98.56 122					99.03 169	96.07 97	94.27 199	96.92 209

UniMVSNet_NR-MVSNet		95.71 136	95.15 144	97.40 145	96.84 224	96.97 77	98.74 112	99.24 10	95.16 105	93.88 217	97.72 177	91.68 104	98.31 256	95.81 107	87.25 294	96.92 209

DU-MVS		95.42 154	94.76 165	97.40 145	96.53 238	96.97 77	98.66 131	98.99 28	95.43 86	93.88 217	97.69 178	88.57 171	98.31 256	95.81 107	87.25 294	96.92 209

NR-MVSNet		94.98 179	94.16 191	97.44 141	96.53 238	97.22 71	98.74 112	98.95 33	94.96 115	89.25 288	97.69 178	89.32 138	98.18 264	94.59 142	87.40 291	96.92 209

jajsoiax		95.45 152	95.03 148	96.73 177	95.42 299	94.63 198	99.14 39	98.52 130	95.74 73	93.22 234	98.36 122	83.87 270	98.65 205	96.95 68	94.04 208	96.91 214

mvs_tets		95.41 156	95.00 149	96.65 189	95.58 294	94.42 208	99.00 57	98.55 124	95.73 74	93.21 235	98.38 120	83.45 273	98.63 206	97.09 63	94.00 210	96.91 214

WR-MVS		95.15 172	94.46 177	97.22 150	96.67 234	96.45 98	98.21 184	98.81 61	94.15 138	93.16 236	97.69 178	87.51 202	98.30 258	95.29 127	88.62 278	96.90 216

tfpn_ndepth		95.53 147	94.90 160	97.39 148	98.96 93	95.88 130	99.05 52	95.27 322	93.80 157	96.95 109	96.93 243	85.53 235	99.40 131	91.54 224	96.10 186	96.89 217

VPA-MVSNet		95.75 133	95.11 145	97.69 123	97.24 199	97.27 67	98.94 65	99.23 12	95.13 106	95.51 158	97.32 204	85.73 232	98.91 183	97.33 58	89.55 261	96.89 217

test_djsdf		96.00 123	95.69 125	96.93 169	95.72 290	95.49 143	99.47 2	98.40 152	94.98 113	94.58 175	97.86 162	89.16 143	98.41 245	96.91 69	94.12 207	96.88 219

HQP4-MVS										94.45 180			98.96 176			96.87 220

ACMM	93.85 9	95.69 138	95.38 133	96.61 195	97.61 174	93.84 225	98.91 67	98.44 146	95.25 101	94.28 198	98.47 113	86.04 229	99.12 153	95.50 120	93.95 212	96.87 220
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP-MVS		95.72 134	95.40 129	96.69 181	97.20 203	94.25 216	98.05 205	98.46 142	96.43 54	94.45 180	97.73 174	86.75 214	98.96 176	95.30 125	94.18 203	96.86 222

testing_2		90.61 286	88.50 293	96.95 167	90.08 326	95.57 139	97.69 241	98.06 215	93.02 191	76.55 328	92.48 323	61.18 335	98.44 235	95.45 122	91.98 239	96.84 223

EI-MVSNet		95.96 124	95.83 117	96.36 219	97.93 158	93.70 231	98.12 198	98.27 168	93.70 165	95.07 163	99.02 58	92.23 92	98.54 214	94.68 138	93.46 220	96.84 223

IterMVS-LS		95.46 151	95.21 142	96.22 227	98.12 147	93.72 230	98.32 174	98.13 196	93.71 163	94.26 199	97.31 205	92.24 91	98.10 267	94.63 139	90.12 253	96.84 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		94.94 183	94.30 184	96.83 173	96.72 231	95.56 140	99.11 45	98.95 33	93.89 151	92.42 258	97.90 159	87.19 207	98.12 266	94.32 149	88.21 281	96.82 226

PS-CasMVS		94.67 202	93.99 204	96.71 178	96.68 233	95.26 152	99.13 42	99.03 24	93.68 168	92.33 259	97.95 155	85.35 239	98.10 267	93.59 168	88.16 283	96.79 227

UniMVSNet (Re)		95.78 132	95.19 143	97.58 134	96.99 215	97.47 61	98.79 101	99.18 16	95.60 79	93.92 216	97.04 228	91.68 104	98.48 225	95.80 109	87.66 289	96.79 227

MVSTER		96.06 122	95.72 120	97.08 160	98.23 138	95.93 123	98.73 115	98.27 168	94.86 119	95.07 163	98.09 145	88.21 180	98.54 214	96.59 85	93.46 220	96.79 227

LPG-MVS_test		95.62 141	95.34 134	96.47 211	97.46 185	93.54 232	98.99 58	98.54 125	94.67 123	94.36 190	98.77 87	85.39 237	99.11 157	95.71 113	94.15 205	96.76 230

LGP-MVS_train				96.47 211	97.46 185	93.54 232		98.54 125	94.67 123	94.36 190	98.77 87	85.39 237	99.11 157	95.71 113	94.15 205	96.76 230

GG-mvs-BLEND				96.59 197	96.34 253	94.98 163	96.51 302	88.58 344		93.10 241	94.34 302	80.34 292	98.05 271	89.53 261	96.99 152	96.74 232

PEN-MVS		94.42 215	93.73 221	96.49 209	96.28 263	94.84 181	99.17 35	99.00 26	93.51 174	92.23 261	97.83 168	86.10 226	97.90 279	92.55 199	86.92 298	96.74 232

OurMVSNet-221017-0		94.21 224	94.00 202	94.85 275	95.60 293	89.22 291	98.89 72	97.43 258	95.29 99	92.18 263	98.52 110	82.86 275	98.59 210	93.46 170	91.76 243	96.74 232

v2v482		94.69 198	94.03 200	96.65 189	96.17 270	94.79 191	98.67 129	98.08 212	92.72 201	94.00 214	97.16 212	87.69 199	98.45 232	92.91 188	88.87 273	96.72 235

GBi-Net		94.49 211	93.80 214	96.56 202	98.21 139	95.00 160	98.82 87	98.18 185	92.46 207	94.09 209	97.07 220	81.16 281	97.95 276	92.08 207	92.14 236	96.72 235

test1		94.49 211	93.80 214	96.56 202	98.21 139	95.00 160	98.82 87	98.18 185	92.46 207	94.09 209	97.07 220	81.16 281	97.95 276	92.08 207	92.14 236	96.72 235

FMVSNet1		93.19 253	92.07 256	96.56 202	97.54 180	95.00 160	98.82 87	98.18 185	90.38 264	92.27 260	97.07 220	73.68 320	97.95 276	89.36 265	91.30 247	96.72 235

v1192		94.32 219	93.58 229	96.53 206	96.10 274	94.45 207	98.50 154	98.17 190	91.54 238	94.19 204	97.06 223	86.95 212	98.43 237	90.14 246	89.57 259	96.70 239

v1240		94.06 236	93.29 240	96.34 222	96.03 278	93.90 223	98.44 159	98.17 190	91.18 256	94.13 208	97.01 232	86.05 227	98.42 238	89.13 268	89.50 262	96.70 239

FMVSNet3		94.97 180	94.26 185	97.11 158	98.18 144	96.62 90	98.56 144	98.26 172	93.67 170	94.09 209	97.10 216	84.25 261	98.01 273	92.08 207	92.14 236	96.70 239

FMVSNet2		94.47 213	93.61 227	97.04 161	98.21 139	96.43 99	98.79 101	98.27 168	92.46 207	93.50 229	97.09 218	81.16 281	98.00 274	91.09 230	91.93 240	96.70 239

ACMH	92.88 16	94.55 209	93.95 206	96.34 222	97.63 172	93.26 239	98.81 93	98.49 141	93.43 177	89.74 283	98.53 107	81.91 279	99.08 162	93.69 164	93.30 226	96.70 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1921920		94.20 225	93.47 236	96.40 217	95.98 279	94.08 219	98.52 149	98.15 193	91.33 248	94.25 200	97.20 211	86.41 219	98.42 238	90.04 251	89.39 264	96.69 244

v1141		94.75 194	94.11 197	96.67 187	96.27 265	94.86 171	98.69 122	98.12 198	92.43 215	94.31 194	96.94 239	88.78 164	98.48 225	92.63 196	88.85 275	96.67 245

divwei89l23v2f112		94.76 192	94.12 196	96.67 187	96.28 263	94.85 172	98.69 122	98.12 198	92.44 214	94.29 197	96.94 239	88.85 156	98.48 225	92.67 194	88.79 277	96.67 245

v1		94.75 194	94.11 197	96.69 181	96.27 265	94.87 170	98.69 122	98.12 198	92.43 215	94.32 193	96.94 239	88.71 168	98.54 214	92.66 195	88.84 276	96.67 245

ACMP	93.49 10	95.34 163	94.98 151	96.43 215	97.67 170	93.48 234	98.73 115	98.44 146	94.94 118	92.53 253	98.53 107	84.50 256	99.14 151	95.48 121	94.00 210	96.66 248
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CLD-MVS		95.62 141	95.34 134	96.46 214	97.52 182	93.75 229	97.27 270	98.46 142	95.53 82	94.42 188	98.00 152	86.21 222	98.97 173	96.25 96	94.37 197	96.66 248
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1neww		94.83 186	94.22 186	96.68 184	96.39 246	94.85 172	98.87 75	98.11 203	92.45 212	94.45 180	97.06 223	88.82 159	98.54 214	92.93 186	88.91 271	96.65 250

v7new		94.83 186	94.22 186	96.68 184	96.39 246	94.85 172	98.87 75	98.11 203	92.45 212	94.45 180	97.06 223	88.82 159	98.54 214	92.93 186	88.91 271	96.65 250

v6		94.83 186	94.21 188	96.69 181	96.36 250	94.85 172	98.87 75	98.11 203	92.46 207	94.44 186	97.05 227	88.76 165	98.57 212	92.95 185	88.92 270	96.65 250

v144192		94.39 217	93.70 222	96.48 210	96.06 276	94.35 212	98.58 139	98.16 192	91.45 240	94.33 192	97.02 230	87.50 204	98.45 232	91.08 231	89.11 266	96.63 253

IterMVS		94.09 233	93.85 212	94.80 278	97.99 155	90.35 279	97.18 274	98.12 198	93.68 168	92.46 257	97.34 202	84.05 266	97.41 293	92.51 201	91.33 246	96.62 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1144		94.59 207	93.92 207	96.60 196	96.21 267	94.78 193	98.59 137	98.14 195	91.86 233	94.21 203	97.02 230	87.97 187	98.41 245	91.72 220	89.57 259	96.61 255

v7		94.69 198	94.04 199	96.62 194	96.41 245	94.79 191	98.78 103	98.13 196	91.89 230	94.30 196	97.16 212	88.13 184	98.45 232	91.96 214	89.65 258	96.61 255

OPM-MVS		95.69 138	95.33 136	96.76 176	96.16 273	94.63 198	98.43 161	98.39 154	96.64 50	95.02 165	98.78 85	85.15 242	99.05 164	95.21 131	94.20 202	96.60 257

LTVRE_ROB	92.95 15	94.60 205	93.90 209	96.68 184	97.41 192	94.42 208	98.52 149	98.59 115	91.69 235	91.21 270	98.35 123	84.87 246	99.04 168	91.06 232	93.44 223	96.60 257
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
semantic-postprocess				94.85 275	97.98 157	90.56 277		98.11 203	93.75 158	92.58 251	97.48 192	83.91 268	97.41 293	92.48 202	91.30 247	96.58 259

pmmvs5		93.65 245	92.97 244	95.68 246	95.49 297	92.37 250	98.20 185	97.28 271	89.66 283	92.58 251	97.26 207	82.14 277	98.09 269	93.18 178	90.95 250	96.58 259

K. test v3		92.55 258	91.91 259	94.48 285	95.64 292	89.24 290	99.07 51	94.88 327	94.04 143	86.78 298	97.59 187	77.64 305	97.64 287	92.08 207	89.43 263	96.57 261

SixPastTwentyTwo		93.34 248	92.86 245	94.75 279	95.67 291	89.41 289	98.75 108	96.67 304	93.89 151	90.15 281	98.25 136	80.87 285	98.27 261	90.90 235	90.64 251	96.57 261

MDA-MVSNet_test_wron		90.71 284	89.38 287	94.68 280	94.83 307	90.78 272	97.19 273	97.46 254	87.60 299	72.41 333	95.72 288	86.51 217	96.71 312	85.92 300	86.80 300	96.56 263

ACMH+	92.99 14	94.30 220	93.77 217	95.88 239	97.81 165	92.04 255	98.71 118	98.37 157	93.99 146	90.60 278	98.47 113	80.86 286	99.05 164	92.75 193	92.40 235	96.55 264

YYNet1		90.70 285	89.39 286	94.62 282	94.79 308	90.65 275	97.20 272	97.46 254	87.54 300	72.54 332	95.74 285	86.51 217	96.66 313	86.00 299	86.76 301	96.54 265

Patchmtry		93.22 252	92.35 253	95.84 240	96.77 226	93.09 244	94.66 324	97.56 235	87.37 301	92.90 244	96.24 271	88.15 182	97.90 279	87.37 291	90.10 254	96.53 266

v7n		94.19 226	93.43 237	96.47 211	95.90 282	94.38 211	99.26 17	98.34 160	91.99 228	92.76 247	97.13 215	88.31 178	98.52 221	89.48 263	87.70 288	96.52 267

MDA-MVSNet-bldmvs		89.97 289	88.35 295	94.83 277	95.21 302	91.34 264	97.64 245	97.51 244	88.36 296	71.17 334	96.13 278	79.22 296	96.63 314	83.65 307	86.27 302	96.52 267

lessismore_v0				94.45 288	94.93 306	88.44 303		91.03 341		86.77 299	97.64 184	76.23 309	98.42 238	90.31 245	85.64 307	96.51 269

anonymousdsp		95.42 154	94.91 159	96.94 168	95.10 303	95.90 128	99.14 39	98.41 150	93.75 158	93.16 236	97.46 193	87.50 204	98.41 245	95.63 117	94.03 209	96.50 270

v148		94.29 221	93.76 219	95.91 237	96.10 274	92.93 245	98.58 139	97.97 220	92.59 205	93.47 230	96.95 237	88.53 174	98.32 254	92.56 198	87.06 296	96.49 271

XVG-ACMP-BASELINE		94.54 210	94.14 193	95.75 245	96.55 237	91.65 262	98.11 200	98.44 146	94.96 115	94.22 202	97.90 159	79.18 297	99.11 157	94.05 157	93.85 213	96.48 272

DTE-MVSNet		93.98 238	93.26 241	96.14 230	96.06 276	94.39 210	99.20 32	98.86 52	93.06 189	91.78 266	97.81 170	85.87 230	97.58 289	90.53 241	86.17 303	96.46 273

v8		94.47 213	93.77 217	96.57 201	96.36 250	94.83 183	99.05 52	98.19 182	91.92 229	93.16 236	96.97 235	88.82 159	98.48 225	91.69 221	87.79 287	96.39 274

WR-MVS_H		95.05 175	94.46 177	96.81 174	96.86 223	95.82 132	99.24 20	99.24 10	93.87 153	92.53 253	96.84 251	90.37 126	98.24 262	93.24 175	87.93 284	96.38 275

v748		93.75 242	93.06 242	95.82 241	95.73 289	92.64 248	99.25 19	98.24 175	91.60 237	92.22 262	96.52 264	87.60 201	98.46 230	90.64 239	85.72 306	96.36 276

V42		94.78 191	94.14 193	96.70 180	96.33 257	95.22 153	98.97 62	98.09 211	92.32 222	94.31 194	97.06 223	88.39 177	98.55 213	92.90 189	88.87 273	96.34 277

v10		94.29 221	93.55 230	96.51 208	96.39 246	94.80 188	98.99 58	98.19 182	91.35 247	93.02 242	96.99 233	88.09 185	98.41 245	90.50 243	88.41 280	96.33 278

pmmvs4		94.69 198	93.99 204	96.81 174	95.74 288	95.94 120	97.40 257	97.67 231	90.42 263	93.37 231	97.59 187	89.08 145	98.20 263	92.97 184	91.67 244	96.30 279

PVSNet_BlendedMVS		96.73 100	96.60 93	97.12 157	99.25 65	95.35 149	98.26 181	99.26 8	94.28 136	97.94 72	97.46 193	92.74 84	99.81 50	96.88 74	93.32 225	96.20 280

pm-mvs1		93.94 239	93.06 242	96.59 197	96.49 241	95.16 154	98.95 64	98.03 219	92.32 222	91.08 272	97.84 165	84.54 255	98.41 245	92.16 205	86.13 305	96.19 281

Anonymous20231206		91.66 275	91.10 263	93.33 297	94.02 314	87.35 311	98.58 139	97.26 273	90.48 260	90.16 280	96.31 269	83.83 271	96.53 315	79.36 317	89.90 256	96.12 282

ITE_SJBPF				95.44 255	97.42 189	91.32 265		97.50 247	95.09 110	93.59 223	98.35 123	81.70 280	98.88 188	89.71 257	93.39 224	96.12 282

FMVSNet5		91.81 273	90.92 267	94.49 284	97.21 202	92.09 253	98.00 211	97.55 239	89.31 290	90.86 275	95.61 291	74.48 317	95.32 322	85.57 302	89.70 257	96.07 284

UnsupCasMVSNet_eth		90.99 282	89.92 283	94.19 291	94.08 313	89.83 282	97.13 276	98.67 104	93.69 166	85.83 304	96.19 276	75.15 313	96.74 309	89.14 267	79.41 322	96.00 285

USDC		93.33 249	92.71 248	95.21 265	96.83 225	90.83 270	96.91 283	97.50 247	93.84 154	90.72 276	98.14 142	77.69 302	98.82 195	89.51 262	93.21 229	95.97 286

pmmvs6		91.77 274	90.63 275	95.17 267	94.69 310	91.24 267	98.67 129	97.92 222	86.14 306	89.62 284	97.56 190	75.79 311	98.34 252	90.75 237	84.56 310	95.94 287

N_pmnet		87.12 300	87.77 297	85.17 320	95.46 298	61.92 343	97.37 261	70.66 351	85.83 310	88.73 292	96.04 280	85.33 241	97.76 285	80.02 314	90.48 252	95.84 288

MIMVSNet1		89.67 291	88.28 296	93.82 293	92.81 319	91.08 269	98.01 209	97.45 256	87.95 297	87.90 295	95.87 284	67.63 330	94.56 325	78.73 320	88.18 282	95.83 289

test2356		88.68 296	88.61 292	88.87 311	89.90 327	78.23 327	95.11 316	96.66 306	88.66 295	89.06 289	94.33 303	73.14 322	92.56 333	75.56 326	95.11 196	95.81 290

test1235678		86.26 302	85.81 301	87.62 314	86.97 332	75.00 334	96.55 300	96.32 311	86.08 308	81.32 324	92.98 315	73.10 323	92.05 334	71.64 330	87.32 292	95.81 290

TransMVSNet (Re)		92.67 257	91.51 261	96.15 229	96.58 236	94.65 196	98.90 68	96.73 300	90.86 259	89.46 286	97.86 162	85.62 234	98.09 269	86.45 296	81.12 316	95.71 292

Baseline_NR-MVSNet		94.35 218	93.81 213	95.96 235	96.20 268	94.05 220	98.61 136	96.67 304	91.44 241	93.85 219	97.60 186	88.57 171	98.14 265	94.39 146	86.93 297	95.68 293

testus		88.91 294	89.08 289	88.40 312	91.39 321	76.05 330	96.56 298	96.48 308	89.38 289	89.39 287	95.17 294	70.94 324	93.56 329	77.04 323	95.41 194	95.61 294

V4		94.18 228	93.52 232	96.13 231	95.89 283	94.31 213	99.23 22	98.22 177	91.42 242	92.82 246	96.89 246	87.93 189	98.52 221	91.51 225	87.81 285	95.58 295

v52		94.18 228	93.52 232	96.13 231	95.95 281	94.29 214	99.23 22	98.21 178	91.42 242	92.84 245	96.89 246	87.85 193	98.53 220	91.51 225	87.81 285	95.57 296

TinyColmap		92.31 261	91.53 260	94.65 281	96.92 218	89.75 283	96.92 281	96.68 303	90.45 262	89.62 284	97.85 164	76.06 310	98.81 196	86.74 294	92.51 234	95.41 297

MS-PatchMatch		93.84 241	93.63 225	94.46 287	96.18 269	89.45 287	97.76 236	98.27 168	92.23 225	92.13 264	97.49 191	79.50 294	98.69 201	89.75 256	99.38 80	95.25 298

LF4IMVS		93.14 254	92.79 247	94.20 290	95.88 284	88.67 299	97.66 244	97.07 279	93.81 156	91.71 267	97.65 182	77.96 301	98.81 196	91.47 227	91.92 241	95.12 299

tfpnnormal		93.66 243	92.70 249	96.55 205	96.94 217	95.94 120	98.97 62	99.19 15	91.04 257	91.38 269	97.34 202	84.94 245	98.61 207	85.45 304	89.02 269	95.11 300

EG-PatchMatch MVS		91.13 279	90.12 280	94.17 292	94.73 309	89.00 295	98.13 197	97.81 225	89.22 291	85.32 307	96.46 265	67.71 329	98.42 238	87.89 289	93.82 214	95.08 301

TDRefinement		91.06 281	89.68 284	95.21 265	85.35 334	91.49 263	98.51 153	97.07 279	91.47 239	88.83 291	97.84 165	77.31 306	99.09 161	92.79 192	77.98 328	95.04 302

MVP-Stereo		94.28 223	93.92 207	95.35 263	94.95 305	92.60 249	97.97 213	97.65 232	91.61 236	90.68 277	97.09 218	86.32 221	98.42 238	89.70 258	99.34 82	95.02 303
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test20.03		90.89 283	90.38 277	92.43 302	93.48 315	88.14 306	98.33 170	97.56 235	93.40 179	87.96 294	96.71 256	80.69 288	94.13 326	79.15 318	86.17 303	95.01 304

ambc				89.49 310	86.66 333	75.78 331	92.66 331	96.72 301		86.55 300	92.50 322	46.01 340	97.90 279	90.32 244	82.09 312	94.80 305

v18		92.10 264	90.97 264	95.50 250	96.34 253	94.85 172	98.82 87	97.52 241	89.99 271	85.31 309	93.26 307	88.90 153	96.92 300	88.82 274	79.77 320	94.73 306

v16		92.08 265	90.94 265	95.49 251	96.38 249	94.84 181	98.81 93	97.51 244	89.94 274	85.25 310	93.28 306	88.86 154	96.91 301	88.70 276	79.78 319	94.72 307

v17		92.08 265	90.94 265	95.48 252	96.34 253	94.83 183	98.81 93	97.52 241	89.95 273	85.32 307	93.24 308	88.91 152	96.91 301	88.76 275	79.63 321	94.71 308

v13		91.88 271	90.69 273	95.43 257	96.33 257	94.78 193	98.75 108	97.50 247	89.68 282	84.93 316	92.98 315	88.84 157	96.83 305	88.14 283	79.09 324	94.69 309

V9		91.91 269	90.73 271	95.45 254	96.32 260	94.80 188	98.77 104	97.50 247	89.81 278	85.03 314	93.08 311	88.76 165	96.86 303	88.24 281	79.03 326	94.69 309

v12		91.89 270	90.70 272	95.43 257	96.31 261	94.80 188	98.76 107	97.50 247	89.76 279	84.95 315	93.00 314	88.82 159	96.82 307	88.23 282	79.00 327	94.68 311

v15		91.94 267	90.77 269	95.43 257	96.31 261	94.83 183	98.77 104	97.50 247	89.92 275	85.13 311	93.08 311	88.76 165	96.86 303	88.40 279	79.10 323	94.61 312

V14		91.93 268	90.76 270	95.42 260	96.33 257	94.81 187	98.77 104	97.51 244	89.86 277	85.09 312	93.13 309	88.80 163	96.83 305	88.32 280	79.06 325	94.60 313

v11		91.85 272	90.68 274	95.36 262	96.34 253	94.74 195	98.80 96	97.43 258	89.60 285	85.09 312	93.03 313	88.53 174	96.75 308	87.37 291	79.96 318	94.58 314

Anonymous20231211		83.69 304	81.50 306	90.26 308	89.23 328	80.10 326	97.97 213	97.06 281	72.79 333	82.05 322	92.57 321	50.28 338	96.32 318	76.15 325	75.38 332	94.37 315

test_0402		91.32 277	90.27 279	94.48 285	96.60 235	91.12 268	98.50 154	97.22 275	86.10 307	88.30 293	96.98 234	77.65 304	97.99 275	78.13 321	92.94 231	94.34 316

new_pmnet		90.06 288	89.00 291	93.22 300	94.18 311	88.32 305	96.42 303	96.89 296	86.19 305	85.67 306	93.62 304	77.18 307	97.10 297	81.61 312	89.29 265	94.23 317

CMPMVS	66.06 21	89.70 290	89.67 285	89.78 309	93.19 316	76.56 329	97.00 278	98.35 159	80.97 325	81.57 323	97.75 173	74.75 316	98.61 207	89.85 253	93.63 217	94.17 318
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PM-MVS		87.77 298	86.55 300	91.40 307	91.03 324	83.36 320	96.92 281	95.18 325	91.28 252	86.48 301	93.42 305	53.27 337	96.74 309	89.43 264	81.97 314	94.11 319

pmmvs-eth3d		90.36 287	89.05 290	94.32 289	91.10 323	92.12 252	97.63 247	96.95 290	88.86 293	84.91 317	93.13 309	78.32 299	96.74 309	88.70 276	81.81 315	94.09 320

new-patchmatchnet		88.50 297	87.45 298	91.67 306	90.31 325	85.89 315	97.16 275	97.33 268	89.47 286	83.63 319	92.77 319	76.38 308	95.06 324	82.70 309	77.29 329	94.06 321

pmmvs3		86.67 301	84.86 303	92.11 305	88.16 329	87.19 313	96.63 295	94.75 329	79.88 327	87.22 297	92.75 320	66.56 331	95.20 323	81.24 313	76.56 331	93.96 322

UnsupCasMVSNet_bld		87.17 299	85.12 302	93.31 298	91.94 320	88.77 296	94.92 320	98.30 165	84.30 317	82.30 320	90.04 327	63.96 334	97.25 295	85.85 301	74.47 334	93.93 323

LCM-MVSNet		78.70 307	76.24 311	86.08 317	77.26 344	71.99 337	94.34 326	96.72 301	61.62 337	76.53 329	89.33 328	33.91 347	92.78 332	81.85 311	74.60 333	93.46 324

OpenMVS_ROB	86.42 20	89.00 293	87.43 299	93.69 294	93.08 317	89.42 288	97.91 220	96.89 296	78.58 328	85.86 303	94.69 298	69.48 326	98.29 260	77.13 322	93.29 227	93.36 325

DeepMVS_CX				86.78 316	97.09 211	72.30 336		95.17 326	75.92 330	84.34 318	95.19 292	70.58 325	95.35 321	79.98 316	89.04 268	92.68 326

1111		84.94 303	84.30 304	86.86 315	87.59 330	75.10 332	96.63 295	96.43 309	82.53 320	80.75 325	92.91 317	68.94 327	93.79 327	68.24 333	84.66 309	91.70 327

PMMVS2		77.95 309	75.44 312	85.46 318	82.54 336	74.95 335	94.23 327	93.08 338	72.80 332	74.68 330	87.38 329	36.36 345	91.56 335	73.95 328	63.94 336	89.87 328

test12356		83.47 305	83.37 305	83.78 321	84.43 335	70.09 339	95.12 315	95.60 320	82.98 318	78.89 327	92.43 324	64.99 332	91.41 336	70.36 331	85.55 308	89.82 329

testmv		78.74 306	77.35 307	82.89 323	78.16 343	69.30 340	95.87 309	94.65 330	81.11 324	70.98 335	87.11 331	46.31 339	90.42 337	65.28 336	76.72 330	88.95 330

FPMVS		77.62 310	77.14 308	79.05 325	79.25 340	60.97 344	95.79 311	95.94 315	65.96 334	67.93 336	94.40 300	37.73 344	88.88 339	68.83 332	88.46 279	87.29 331

tmp_tt		68.90 314	66.97 314	74.68 329	50.78 349	59.95 345	87.13 336	83.47 349	38.80 343	62.21 338	96.23 273	64.70 333	76.91 346	88.91 273	30.49 344	87.19 332

ANet_high		69.08 313	65.37 315	80.22 324	65.99 347	71.96 338	90.91 334	90.09 342	82.62 319	49.93 343	78.39 338	29.36 348	81.75 342	62.49 339	38.52 342	86.95 333

no-one		74.41 311	70.76 313	85.35 319	79.88 339	76.83 328	94.68 323	94.22 334	80.33 326	63.81 337	79.73 337	35.45 346	93.36 330	71.78 329	36.99 343	85.86 334

testpf		88.74 295	89.09 288	87.69 313	95.78 287	83.16 321	84.05 340	94.13 336	85.22 313	90.30 279	94.39 301	74.92 315	95.80 319	89.77 254	93.28 228	84.10 335

PNet_i23d		67.70 315	65.07 316	75.60 327	78.61 341	59.61 346	89.14 335	88.24 345	61.83 336	52.37 341	80.89 335	18.91 349	84.91 341	62.70 338	52.93 338	82.28 336

wuykxyi23d		63.73 319	58.86 321	78.35 326	67.62 346	67.90 341	86.56 337	87.81 346	58.26 338	42.49 345	70.28 342	11.55 352	85.05 340	63.66 337	41.50 339	82.11 337

MVE	62.14 22	63.28 320	59.38 320	74.99 328	74.33 345	65.47 342	85.55 338	80.50 350	52.02 341	51.10 342	75.00 341	10.91 354	80.50 343	51.60 341	53.40 337	78.99 338
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	61.03 23	65.95 316	63.57 318	73.09 330	57.90 348	51.22 349	85.05 339	93.93 337	54.45 339	44.32 344	83.57 332	13.22 350	89.15 338	58.68 340	81.00 317	78.91 339
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		78.40 308	76.75 309	83.38 322	95.54 295	80.43 325	79.42 341	97.40 261	64.67 335	73.46 331	80.82 336	45.65 341	93.14 331	66.32 335	87.43 290	76.56 340
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		64.07 318	63.26 319	66.53 332	81.73 338	58.81 348	91.85 332	84.75 348	51.93 342	59.09 340	75.13 340	43.32 342	79.09 345	42.03 343	39.47 341	61.69 341

E-PMN		64.94 317	64.25 317	67.02 331	82.28 337	59.36 347	91.83 333	85.63 347	52.69 340	60.22 339	77.28 339	41.06 343	80.12 344	46.15 342	41.14 340	61.57 342

test123		20.95 325	23.72 326	12.64 336	13.54 351	8.19 351	96.55 300	6.13 354	7.48 346	16.74 347	37.98 345	12.97 351	6.05 348	16.69 345	5.43 348	23.68 343

.test1245		73.05 312	76.31 310	63.27 333	87.59 330	75.10 332	96.63 295	96.43 309	82.53 320	80.75 325	92.91 317	68.94 327	93.79 327	68.24 333	12.72 346	20.91 344

testmvs		21.48 324	24.95 325	11.09 337	14.89 350	6.47 352	96.56 298	9.87 353	7.55 345	17.93 346	39.02 344	9.43 355	5.90 349	16.56 346	12.72 346	20.91 344

wuyk23d		30.17 322	30.18 324	30.16 335	78.61 341	43.29 350	66.79 342	14.21 352	17.31 344	14.82 348	11.93 348	11.55 352	41.43 347	37.08 344	19.30 345	5.76 346

cdsmvs_eth3d_5k		23.98 323	31.98 323	0.00 338	0.00 352	0.00 353	0.00 343	98.59 115	0.00 347	0.00 349	98.61 100	90.60 124	0.00 350	0.00 347	0.00 349	0.00 347

pcd_1.5k_mvsjas		7.88 327	10.50 328	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	0.00 349	94.51 61	0.00 350	0.00 347	0.00 349	0.00 347

sosnet-low-res		0.00 328	0.00 329	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	0.00 349	0.00 356	0.00 350	0.00 347	0.00 349	0.00 347

sosnet		0.00 328	0.00 329	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	0.00 349	0.00 356	0.00 350	0.00 347	0.00 349	0.00 347

uncertanet		0.00 328	0.00 329	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	0.00 349	0.00 356	0.00 350	0.00 347	0.00 349	0.00 347

Regformer		0.00 328	0.00 329	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	0.00 349	0.00 356	0.00 350	0.00 347	0.00 349	0.00 347

ab-mvs-re		8.20 326	10.94 327	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	98.43 115	0.00 356	0.00 350	0.00 347	0.00 349	0.00 347

uanet		0.00 328	0.00 329	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	0.00 349	0.00 356	0.00 350	0.00 347	0.00 349	0.00 347

test_part2					99.63 21	99.18 1				99.27 6

test_full								98.84 54

sam_mvs1												89.45 135

sam_mvs												88.99 146

MTGPA								98.74 79

test_post1							96.68 294				30.43 347	87.85 193	98.69 201	92.59 197

test_post											31.83 346	88.83 158	98.91 183

patchmatchnet-post											95.10 295	89.42 136	98.89 187

MTMP								94.14 335

gm-plane-assit					95.88 284	87.47 310			89.74 281		96.94 239		99.19 147	93.32 174

TEST9					99.31 48	98.50 13	97.92 217	98.73 84	92.63 202	97.74 82	98.68 94	96.20 13	99.80 57

test_8					99.29 56	98.44 15	97.89 225	98.72 86	92.98 193	97.70 85	98.66 97	96.20 13	99.80 57

agg_prior					99.30 53	98.38 18		98.72 86		97.57 94			99.81 50

test_prior4						98.01 42	97.86 228

test_prior2							97.80 233		96.12 63	97.89 76	98.69 92	95.96 26		96.89 71	99.60 50

旧先验2							97.57 250		91.30 250	98.67 37			99.80 57	95.70 115

新几何2							97.64 245

原ACMM2							97.67 243

testdata2													99.89 27	91.65 222

segment_acmp												96.85 4

testdata1							97.32 267		96.34 57

plane_prior7					97.42 189	94.63 198

plane_prior6					97.35 194	94.61 201						87.09 208

plane_prior4											98.28 131

plane_prior3						94.61 201			97.02 39	95.34 159

plane_prior2							98.80 96		97.28 21

plane_prior1					97.37 193

plane_prior						94.60 203	98.44 159		96.74 46						94.22 201

n2								0.00 355

nn								0.00 355

door-mid								94.37 332

test11								98.66 107

door								94.64 331

HQP5-MVS						94.25 216

HQP-NCC					97.20 203		98.05 205		96.43 54	94.45 180

ACMP_Plane					97.20 203		98.05 205		96.43 54	94.45 180

BP-MVS														95.30 125

HQP3-MVS								98.46 142							94.18 203

HQP2-MVS												86.75 214

NP-MVS					97.28 197	94.51 206					97.73 174

MDTV_nov1_ep13			95.40 129		97.48 183	88.34 304	96.85 289	97.29 270	93.74 160	97.48 97	97.26 207	89.18 142	99.05 164	91.92 215	97.43 147

ACMMP++_ref															92.97 230

ACMMP++															93.61 218

Test By Simon												94.64 58

High-resolution multi-view benchmark