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.

LCM-MVSNet		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 1

Anonymous20231211		97.78 3	98.31 2	96.16 46	99.55 2	89.37 80	98.40 5	98.89 4	98.75 2	99.48 3	99.62 2	98.70 2	99.40 36	91.60 105	99.84 5	99.71 3

XVG-OURS-SEG-HR		95.38 64	95.00 88	96.51 43	98.10 69	94.07 15	92.46 166	98.13 31	90.69 126	93.75 173	96.25 140	98.03 3	97.02 262	92.08 92	95.55 263	98.45 114

pmmvs6		96.80 14	97.36 9	95.15 85	99.12 7	87.82 111	96.68 23	97.86 57	96.10 24	98.14 25	99.28 3	97.94 4	98.21 198	91.38 112	99.69 15	99.42 27

ACMH	88.36 12	96.59 26	97.43 5	94.07 121	98.56 35	85.33 149	96.33 39	98.30 16	94.66 35	98.72 9	98.30 36	97.51 5	98.00 209	94.87 21	99.59 34	98.86 85
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pcd1.5k->3k		41.03 327	43.65 329	33.18 340	98.74 26	0.00 359	0.00 350	97.57 81	0.00 354	0.00 355	0.00 356	97.01 6	0.00 357	0.00 354	99.52 45	99.53 17

HPM-MVS_fast		97.01 7	96.89 17	97.39 18	99.12 7	93.92 24	97.16 12	98.17 26	93.11 62	96.48 75	97.36 78	96.92 7	99.34 49	94.31 33	99.38 64	98.92 82

ACMH+	88.43 11	96.48 30	96.82 18	95.47 74	98.54 39	89.06 83	95.65 61	98.61 7	96.10 24	98.16 24	97.52 68	96.90 8	98.62 156	90.30 125	99.60 32	98.72 100

HPM-MVS		96.81 13	96.62 24	97.36 20	98.89 18	93.53 34	97.51 9	98.44 8	92.35 81	95.95 102	96.41 125	96.71 9	99.42 28	93.99 42	99.36 65	99.13 50

abl_6		97.31 6	97.12 14	97.86 3	98.54 39	95.32 8	96.61 25	98.35 12	95.81 29	97.55 38	97.44 72	96.51 10	99.40 36	94.06 41	99.23 78	98.85 88

mvs_tets		96.83 10	96.71 21	97.17 25	98.83 21	92.51 43	96.58 27	97.61 77	87.57 195	98.80 8	98.90 9	96.50 11	99.59 12	96.15 9	99.47 48	99.40 31

LPG-MVS_test		96.38 39	96.23 36	96.84 36	98.36 53	92.13 47	95.33 70	98.25 19	91.78 103	97.07 53	97.22 83	96.38 12	99.28 55	92.07 93	99.59 34	99.11 52

LGP-MVS_train				96.84 36	98.36 53	92.13 47		98.25 19	91.78 103	97.07 53	97.22 83	96.38 12	99.28 55	92.07 93	99.59 34	99.11 52

ACMM	88.83 9	96.30 42	96.07 46	96.97 31	98.39 49	92.95 41	94.74 89	98.03 40	90.82 124	97.15 51	96.85 101	96.25 14	99.00 91	93.10 68	99.33 68	98.95 77
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
wuyk23d		87.83 246	90.79 196	78.96 330	90.46 315	88.63 92	92.72 153	90.67 280	91.65 109	98.68 11	97.64 62	96.06 15	77.53 351	59.84 341	99.41 60	70.73 348

wuykxyi23d		96.76 16	96.57 26	97.34 21	97.75 85	96.73 3	94.37 105	96.48 163	91.00 121	99.72 2	98.99 6	96.06 15	98.21 198	94.86 22	99.90 2	97.09 190

ACMP	88.15 13	95.71 53	95.43 72	96.54 42	98.17 64	91.73 55	94.24 109	98.08 32	89.46 148	96.61 72	96.47 120	95.85 17	99.12 73	90.45 117	99.56 41	98.77 95
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TransMVSNet (Re)		95.27 72	96.04 48	92.97 156	98.37 52	81.92 182	95.07 78	96.76 150	93.97 47	97.77 34	98.57 21	95.72 18	97.90 212	88.89 156	99.23 78	99.08 59

ACMMP_Plus		96.21 43	96.12 42	96.49 45	98.90 17	91.42 57	94.57 98	98.03 40	90.42 134	96.37 78	97.35 79	95.68 19	99.25 59	94.44 31	99.34 66	98.80 92

APD-MVS_3200maxsize		96.82 11	96.65 22	97.32 22	97.95 79	93.82 29	96.31 41	98.25 19	95.51 30	96.99 59	97.05 93	95.63 20	99.39 41	93.31 62	98.88 108	98.75 96

MP-MVS-pluss		96.08 47	95.92 53	96.57 41	99.06 9	91.21 59	93.25 141	98.32 13	87.89 189	96.86 61	97.38 75	95.55 21	99.39 41	95.47 13	99.47 48	99.11 52
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
COLMAP_ROB	91.06 5	96.75 17	96.62 24	97.13 26	98.38 50	94.31 12	96.79 21	98.32 13	96.69 15	96.86 61	97.56 65	95.48 22	98.77 137	90.11 131	99.44 54	98.31 119
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SD-MVS		95.19 73	95.73 62	93.55 137	96.62 143	88.88 89	94.67 91	98.05 37	91.26 115	97.25 50	96.40 126	95.42 23	94.36 316	92.72 78	99.19 81	97.40 178

HFP-MVS		96.39 38	96.17 39	97.04 28	98.51 43	93.37 35	96.30 43	97.98 45	92.35 81	95.63 117	96.47 120	95.37 24	99.27 57	93.78 45	99.14 86	98.48 111

#test#		95.89 50	95.51 66	97.04 28	98.51 43	93.37 35	95.14 75	97.98 45	89.34 150	95.63 117	96.47 120	95.37 24	99.27 57	91.99 95	99.14 86	98.48 111

jajsoiax		96.59 26	96.42 29	97.12 27	98.76 25	92.49 44	96.44 35	97.42 95	86.96 205	98.71 10	98.72 17	95.36 26	99.56 16	95.92 10	99.45 52	99.32 37

TranMVSNet+NR-MVSNet		96.07 48	96.26 35	95.50 73	98.26 58	87.69 112	93.75 126	97.86 57	95.96 28	97.48 41	97.14 87	95.33 27	99.44 24	90.79 115	99.76 13	99.38 32

PMVS	87.21 14	94.97 81	95.33 76	93.91 128	98.97 14	97.16 2	95.54 65	95.85 192	96.47 18	93.40 182	97.46 71	95.31 28	95.47 300	86.18 195	98.78 124	89.11 334
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pm-mvs1		95.43 60	95.94 51	93.93 127	98.38 50	85.08 151	95.46 67	97.12 124	91.84 98	97.28 47	98.46 28	95.30 29	97.71 237	90.17 129	99.42 56	98.99 70

PGM-MVS		96.32 40	95.94 51	97.43 15	98.59 34	93.84 28	95.33 70	98.30 16	91.40 113	95.76 113	96.87 100	95.26 30	99.45 23	92.77 74	99.21 80	99.00 68

PS-CasMVS		96.69 20	97.43 5	94.49 108	99.13 5	84.09 162	96.61 25	97.97 48	97.91 5	98.64 13	98.13 40	95.24 31	99.65 3	93.39 59	99.84 5	99.72 2

LTVRE_ROB	93.87 1	97.93 2	98.16 3	97.26 23	98.81 23	93.86 27	99.07 2	98.98 3	97.01 11	98.92 5	98.78 14	95.22 32	98.61 157	96.85 4	99.77 12	99.31 38
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
nrg030		96.32 40	96.55 27	95.62 69	97.83 82	88.55 96	95.77 58	98.29 18	92.68 70	98.03 27	97.91 52	95.13 33	98.95 99	93.85 43	99.49 47	99.36 35

APDe-MVS		96.46 32	96.64 23	95.93 55	97.68 94	89.38 79	96.90 18	98.41 11	92.52 76	97.43 44	97.92 50	95.11 34	99.50 18	94.45 30	99.30 70	98.92 82

ACMMP		96.61 24	96.34 32	97.43 15	98.61 31	93.88 25	96.95 17	98.18 25	92.26 84	96.33 79	96.84 103	95.10 35	99.40 36	93.47 55	99.33 68	99.02 67
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
OPM-MVS		95.61 56	95.45 69	96.08 49	98.49 46	91.00 61	92.65 156	97.33 108	90.05 139	96.77 65	96.85 101	95.04 36	98.56 165	92.77 74	99.06 93	98.70 101

DTE-MVSNet		96.74 18	97.43 5	94.67 96	99.13 5	84.68 154	96.51 30	97.94 54	98.14 3	98.67 12	98.32 35	95.04 36	99.69 2	93.27 63	99.82 10	99.62 11

region2R		96.41 36	96.09 44	97.38 19	98.62 29	93.81 31	96.32 40	97.96 49	92.26 84	95.28 128	96.57 116	95.02 38	99.41 32	93.63 49	99.11 89	98.94 78

PEN-MVS		96.69 20	97.39 8	94.61 98	99.16 3	84.50 155	96.54 29	98.05 37	98.06 4	98.64 13	98.25 38	95.01 39	99.65 3	92.95 72	99.83 8	99.68 5

SteuartSystems-ACMMP		96.40 37	96.30 33	96.71 38	98.63 28	91.96 50	95.70 59	98.01 43	93.34 60	96.64 70	96.57 116	94.99 40	99.36 47	93.48 54	99.34 66	98.82 90
Skip Steuart: Steuart Systems R&D Blog.
canonicalmvs		94.59 100	94.69 94	94.30 116	95.60 220	87.03 121	95.59 62	98.24 22	91.56 111	95.21 133	92.04 273	94.95 41	98.66 153	91.45 110	97.57 210	97.20 188

ACMMPR		96.46 32	96.14 40	97.41 17	98.60 32	93.82 29	96.30 43	97.96 49	92.35 81	95.57 119	96.61 114	94.93 42	99.41 32	93.78 45	99.15 85	99.00 68

CP-MVS		96.44 35	96.08 45	97.54 9	98.29 56	94.62 10	96.80 20	98.08 32	92.67 72	95.08 139	96.39 130	94.77 43	99.42 28	93.17 66	99.44 54	98.58 110

test_part1								98.14 28				94.69 44			99.10 90	98.17 127

ESAPD		95.42 62	95.34 74	95.68 68	98.21 61	89.41 76	93.92 121	98.14 28	91.83 100	96.72 66	96.39 130	94.69 44	99.44 24	89.00 153	99.10 90	98.17 127

TDRefinement		97.68 4	97.60 4	97.93 2	99.02 11	95.95 6	98.61 3	98.81 5	97.41 8	97.28 47	98.46 28	94.62 46	98.84 121	94.64 26	99.53 43	98.99 70

XVS		96.49 29	96.18 38	97.44 13	98.56 35	93.99 22	96.50 31	97.95 51	94.58 36	94.38 157	96.49 118	94.56 47	99.39 41	93.57 50	99.05 95	98.93 79

X-MVStestdata		90.70 196	88.45 223	97.44 13	98.56 35	93.99 22	96.50 31	97.95 51	94.58 36	94.38 157	26.89 351	94.56 47	99.39 41	93.57 50	99.05 95	98.93 79

mPP-MVS		96.46 32	96.05 47	97.69 5	98.62 29	94.65 9	96.45 33	97.74 67	92.59 75	95.47 121	96.68 112	94.50 49	99.42 28	93.10 68	99.26 74	98.99 70

DeepC-MVS	91.39 4	95.43 60	95.33 76	95.71 66	97.67 95	90.17 67	93.86 124	98.02 42	87.35 197	96.22 89	97.99 47	94.48 50	99.05 81	92.73 77	99.68 18	97.93 143
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS		96.14 45	95.68 63	97.51 10	98.81 23	94.06 16	96.10 47	97.78 66	92.73 69	93.48 179	96.72 110	94.23 51	99.42 28	91.99 95	99.29 72	99.05 63

anonymousdsp		96.74 18	96.42 29	97.68 7	98.00 75	94.03 21	96.97 16	97.61 77	87.68 194	98.45 21	98.77 15	94.20 52	99.50 18	96.70 5	99.40 61	99.53 17

test_0402		95.73 52	96.22 37	94.26 117	98.19 63	85.77 144	93.24 142	97.24 116	96.88 14	97.69 36	97.77 58	94.12 53	99.13 71	91.54 109	99.29 72	97.88 149

Effi-MVS+		92.79 156	92.74 153	92.94 159	95.10 237	83.30 169	94.00 114	97.53 86	91.36 114	89.35 269	90.65 296	94.01 54	98.66 153	87.40 178	95.30 271	96.88 200

OMC-MVS		94.22 113	93.69 130	95.81 60	97.25 108	91.27 58	92.27 174	97.40 97	87.10 203	94.56 153	95.42 179	93.74 55	98.11 206	86.62 188	98.85 112	98.06 134

LCM-MVSNet-Re		94.20 114	94.58 98	93.04 151	95.91 204	83.13 172	93.79 125	99.19 2	92.00 92	98.84 6	98.04 42	93.64 56	99.02 88	81.28 238	98.54 139	96.96 195

MPTG		96.47 31	96.14 40	97.47 11	98.95 15	94.05 18	93.69 128	97.62 74	94.46 40	96.29 83	96.94 94	93.56 57	99.37 45	94.29 35	99.42 56	98.99 70

MTAPA		96.65 22	96.38 31	97.47 11	98.95 15	94.05 18	95.88 55	97.62 74	94.46 40	96.29 83	96.94 94	93.56 57	99.37 45	94.29 35	99.42 56	98.99 70

UA-Net		97.35 5	97.24 13	97.69 5	98.22 60	93.87 26	98.42 4	98.19 24	96.95 12	95.46 123	99.23 4	93.45 59	99.57 13	95.34 17	99.89 4	99.63 10

MVS_111021_HR		93.63 125	93.42 139	94.26 117	96.65 137	86.96 122	89.30 273	96.23 179	88.36 179	93.57 177	94.60 209	93.45 59	97.77 232	90.23 127	98.38 151	98.03 136

cdsmvs_eth3d_5k		23.35 329	31.13 330	0.00 344	0.00 358	0.00 359	0.00 350	95.58 202	0.00 354	0.00 355	91.15 284	93.43 61	0.00 357	0.00 354	0.00 355	0.00 355

APD-MVS		95.00 80	94.69 94	95.93 55	97.38 105	90.88 64	94.59 95	97.81 62	89.22 154	95.46 123	96.17 150	93.42 62	99.34 49	89.30 144	98.87 111	97.56 171
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
v52		96.93 8	97.29 11	95.86 58	98.12 66	88.48 99	97.69 7	97.74 67	94.90 33	98.55 15	98.72 17	93.39 63	99.49 21	96.92 2	99.62 29	99.61 12

V4		96.93 8	97.29 11	95.86 58	98.11 67	88.47 100	97.69 7	97.74 67	94.91 31	98.55 15	98.72 17	93.37 64	99.49 21	96.92 2	99.62 29	99.61 12

ANet_high		94.83 90	96.28 34	90.47 232	96.65 137	73.16 305	94.33 107	98.74 6	96.39 20	98.09 26	98.93 8	93.37 64	98.70 149	90.38 120	99.68 18	99.53 17

test_djsdf		96.62 23	96.49 28	97.01 30	98.55 38	91.77 54	97.15 13	97.37 99	88.98 156	98.26 23	98.86 10	93.35 66	99.60 8	96.41 6	99.45 52	99.66 7

v748		96.51 28	97.05 15	94.89 90	98.35 55	85.82 143	96.58 27	97.47 92	96.25 21	98.46 19	98.35 33	93.27 67	99.33 52	95.13 19	99.59 34	99.52 20

VPA-MVSNet		95.14 76	95.67 64	93.58 136	97.76 84	83.15 171	94.58 97	97.58 80	93.39 59	97.05 57	98.04 42	93.25 68	98.51 174	89.75 138	99.59 34	99.08 59

DeepPCF-MVS	90.46 6	94.20 114	93.56 135	96.14 47	95.96 200	92.96 40	89.48 267	97.46 93	85.14 224	96.23 88	95.42 179	93.19 69	98.08 207	90.37 121	98.76 126	97.38 181

LS3D		96.11 46	95.83 58	96.95 33	94.75 247	94.20 14	97.34 11	97.98 45	97.31 9	95.32 126	96.77 104	93.08 70	99.20 63	91.79 100	98.16 177	97.44 175

DP-MVS		95.62 55	95.84 57	94.97 88	97.16 112	88.62 93	94.54 102	97.64 73	96.94 13	96.58 73	97.32 80	93.07 71	98.72 143	90.45 117	98.84 113	97.57 169

EG-PatchMatch MVS		94.54 103	94.67 96	94.14 119	97.87 81	86.50 126	92.00 183	96.74 151	88.16 185	96.93 60	97.61 63	93.04 72	97.90 212	91.60 105	98.12 182	98.03 136

Fast-Effi-MVS+		91.28 190	90.86 193	92.53 182	95.45 225	82.53 177	89.25 276	96.52 161	85.00 228	89.91 258	88.55 313	92.94 73	98.84 121	84.72 211	95.44 268	96.22 227

v7n		96.82 11	97.31 10	95.33 78	98.54 39	86.81 124	96.83 19	98.07 35	96.59 17	98.46 19	98.43 32	92.91 74	99.52 17	96.25 8	99.76 13	99.65 9

XVG-ACMP-BASELINE		95.68 54	95.34 74	96.69 39	98.40 48	93.04 38	94.54 102	98.05 37	90.45 133	96.31 81	96.76 106	92.91 74	98.72 143	91.19 113	99.42 56	98.32 117

testgi		90.38 203	91.34 184	87.50 291	97.49 103	71.54 314	89.43 268	95.16 210	88.38 178	94.54 154	94.68 208	92.88 76	93.09 326	71.60 315	97.85 199	97.88 149

MVS_111021_LR		93.66 123	93.28 142	94.80 93	96.25 176	90.95 62	90.21 243	95.43 206	87.91 187	93.74 175	94.40 215	92.88 76	96.38 285	90.39 119	98.28 164	97.07 191

CNVR-MVS		94.58 101	94.29 107	95.46 75	96.94 122	89.35 81	91.81 201	96.80 147	89.66 146	93.90 171	95.44 178	92.80 78	98.72 143	92.74 76	98.52 141	98.32 117

XXY-MVS		92.58 164	93.16 145	90.84 228	97.75 85	79.84 218	91.87 192	96.22 181	85.94 215	95.53 120	97.68 60	92.69 79	94.48 312	83.21 222	97.51 211	98.21 125

CDPH-MVS		92.67 161	91.83 169	95.18 84	96.94 122	88.46 101	90.70 229	97.07 125	77.38 288	92.34 210	95.08 190	92.67 80	98.88 109	85.74 197	98.57 136	98.20 126

Fast-Effi-MVS+-dtu		92.77 158	92.16 163	94.58 105	94.66 254	88.25 103	92.05 181	96.65 154	89.62 147	90.08 252	91.23 283	92.56 81	98.60 159	86.30 194	96.27 251	96.90 198

AllTest		94.88 87	94.51 100	96.00 50	98.02 73	92.17 45	95.26 73	98.43 9	90.48 131	95.04 140	96.74 108	92.54 82	97.86 223	85.11 205	98.98 101	97.98 139

TestCases				96.00 50	98.02 73	92.17 45		98.43 9	90.48 131	95.04 140	96.74 108	92.54 82	97.86 223	85.11 205	98.98 101	97.98 139

TinyColmap		92.00 176	92.76 152	89.71 248	95.62 219	77.02 265	90.72 228	96.17 183	87.70 193	95.26 129	96.29 137	92.54 82	96.45 281	81.77 233	98.77 125	95.66 244

Regformer-2		94.86 88	94.55 99	95.77 62	92.83 286	89.98 69	91.87 192	96.40 167	94.38 42	96.19 93	95.04 193	92.47 85	99.04 84	93.49 53	98.31 160	98.28 121

CLD-MVS		91.82 177	91.41 181	93.04 151	96.37 159	83.65 166	86.82 306	97.29 112	84.65 233	92.27 212	89.67 306	92.20 86	97.85 226	83.95 216	99.47 48	97.62 167
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
segment_acmp												92.14 87

Regformer-4		94.90 85	94.67 96	95.59 70	92.78 288	89.02 84	92.39 169	95.91 189	94.50 38	96.41 76	95.56 173	92.10 88	99.01 90	94.23 37	98.14 179	98.74 97

Vis-MVSNet		95.50 59	95.48 67	95.56 72	98.11 67	89.40 78	95.35 69	98.22 23	92.36 79	94.11 165	98.07 41	92.02 89	99.44 24	93.38 60	97.67 206	97.85 152
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Regformer-1		94.55 102	94.33 106	95.19 83	92.83 286	88.54 97	91.87 192	95.84 193	93.99 45	95.95 102	95.04 193	92.00 90	98.79 130	93.14 67	98.31 160	98.23 123

ITE_SJBPF				95.95 52	97.34 107	93.36 37		96.55 160	91.93 93	94.82 146	95.39 182	91.99 91	97.08 260	85.53 199	97.96 193	97.41 176

CP-MVSNet		96.19 44	96.80 19	94.38 114	98.99 13	83.82 164	96.31 41	97.53 86	97.60 6	98.34 22	97.52 68	91.98 92	99.63 6	93.08 70	99.81 11	99.70 4

CSCG		94.69 96	94.75 92	94.52 106	97.55 100	87.87 109	95.01 81	97.57 81	92.68 70	96.20 91	93.44 244	91.92 93	98.78 133	89.11 152	99.24 76	96.92 197

TSAR-MVS + MP.		94.96 82	94.75 92	95.57 71	98.86 20	88.69 90	96.37 38	96.81 146	85.23 222	94.75 148	97.12 89	91.85 94	99.40 36	93.45 56	98.33 158	98.62 106

Gipuma		95.31 68	95.80 59	93.81 132	97.99 78	90.91 63	96.42 36	97.95 51	96.69 15	91.78 219	98.85 12	91.77 95	95.49 299	91.72 101	99.08 92	95.02 260
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
WR-MVS_H		96.60 25	97.05 15	95.24 81	99.02 11	86.44 130	96.78 22	98.08 32	97.42 7	98.48 18	97.86 55	91.76 96	99.63 6	94.23 37	99.84 5	99.66 7

AdaColmap		91.63 179	91.36 183	92.47 184	95.56 221	86.36 133	92.24 177	96.27 176	88.88 160	89.90 259	92.69 257	91.65 97	98.32 189	77.38 281	97.64 207	92.72 310

PHI-MVS		94.34 109	93.80 122	95.95 52	95.65 216	91.67 56	94.82 88	97.86 57	87.86 190	93.04 193	94.16 224	91.58 98	98.78 133	90.27 126	98.96 104	97.41 176

xiu_mvs_v1_base_debu		91.47 184	91.52 176	91.33 216	95.69 213	81.56 186	89.92 255	96.05 185	83.22 241	91.26 226	90.74 291	91.55 99	98.82 123	89.29 145	95.91 256	93.62 295

xiu_mvs_v1_base		91.47 184	91.52 176	91.33 216	95.69 213	81.56 186	89.92 255	96.05 185	83.22 241	91.26 226	90.74 291	91.55 99	98.82 123	89.29 145	95.91 256	93.62 295

xiu_mvs_v1_base_debi		91.47 184	91.52 176	91.33 216	95.69 213	81.56 186	89.92 255	96.05 185	83.22 241	91.26 226	90.74 291	91.55 99	98.82 123	89.29 145	95.91 256	93.62 295

tfpnnormal		94.27 111	94.87 91	92.48 183	97.71 90	80.88 194	94.55 101	95.41 207	93.70 52	96.67 69	97.72 59	91.40 102	98.18 203	87.45 176	99.18 83	98.36 115

Regformer-3		94.28 110	94.23 112	94.46 110	92.78 288	86.28 134	92.39 169	94.70 221	93.69 55	95.97 100	95.56 173	91.34 103	98.48 178	93.45 56	98.14 179	98.62 106

3Dnovator+	92.74 2	95.86 51	95.77 60	96.13 48	96.81 130	90.79 66	96.30 43	97.82 61	96.13 23	94.74 149	97.23 82	91.33 104	99.16 65	93.25 64	98.30 163	98.46 113

TEST9					96.45 156	89.46 73	90.60 232	96.92 137	79.09 278	90.49 246	94.39 216	91.31 105	98.88 109

agg_prior1		92.60 163	91.76 172	95.10 86	96.20 178	88.89 87	90.37 238	96.88 142	79.67 271	90.21 249	94.41 213	91.30 106	98.78 133	88.46 164	98.37 156	97.64 166

DeepC-MVS_fast	89.96 7	93.73 122	93.44 138	94.60 102	96.14 183	87.90 108	93.36 134	97.14 121	85.53 221	93.90 171	95.45 177	91.30 106	98.59 161	89.51 141	98.62 133	97.31 184
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set		94.36 107	94.28 108	94.61 98	92.55 290	85.98 140	92.44 167	94.69 222	93.70 52	96.12 96	95.81 162	91.24 108	98.86 118	93.76 48	98.22 172	98.98 75

MCST-MVS		92.91 153	92.51 159	94.10 120	97.52 101	85.72 145	91.36 213	97.13 123	80.33 266	92.91 197	94.24 220	91.23 109	98.72 143	89.99 135	97.93 195	97.86 151

RPSCF		95.58 57	94.89 90	97.62 8	97.58 98	96.30 5	95.97 51	97.53 86	92.42 77	93.41 180	97.78 56	91.21 110	97.77 232	91.06 114	97.06 227	98.80 92

train_agg		92.71 160	91.83 169	95.35 76	96.45 156	89.46 73	90.60 232	96.92 137	79.37 274	90.49 246	94.39 216	91.20 111	98.88 109	88.66 161	98.43 147	97.72 159

test_8					96.37 159	89.14 82	90.51 236	96.89 141	79.37 274	90.42 248	94.36 218	91.20 111	98.82 123

EI-MVSNet-UG-set		94.35 108	94.27 110	94.59 103	92.46 291	85.87 141	92.42 168	94.69 222	93.67 56	96.13 95	95.84 161	91.20 111	98.86 118	93.78 45	98.23 170	99.03 66

testing_2		94.03 117	94.38 103	93.00 154	96.79 132	81.41 189	92.87 151	96.96 132	85.88 217	97.06 56	97.92 50	91.18 114	98.71 148	91.72 101	99.04 98	98.87 84

xiu_mvs_v2_base		89.00 224	89.19 211	88.46 281	94.86 242	74.63 289	86.97 303	95.60 198	80.88 262	87.83 291	88.62 312	91.04 115	98.81 128	82.51 229	94.38 287	91.93 321

HPM-MVS++		95.02 79	94.39 102	96.91 34	97.88 80	93.58 33	94.09 112	96.99 130	91.05 120	92.40 206	95.22 185	91.03 116	99.25 59	92.11 90	98.69 131	97.90 147

TAPA-MVS	88.58 10	92.49 167	91.75 173	94.73 95	96.50 149	89.69 72	92.91 149	97.68 71	78.02 285	92.79 198	94.10 226	90.85 117	97.96 211	84.76 210	98.16 177	96.54 207
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pcd_1.5k_mvsjas		7.56 332	10.09 333	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	90.77 118	0.00 357	0.00 354	0.00 355	0.00 355

PS-MVSNAJss		96.01 49	96.04 48	95.89 57	98.82 22	88.51 98	95.57 63	97.88 56	88.72 168	98.81 7	98.86 10	90.77 118	99.60 8	95.43 14	99.53 43	99.57 15

PS-MVSNAJ		88.86 228	88.99 217	88.48 280	94.88 240	74.71 287	86.69 307	95.60 198	80.88 262	87.83 291	87.37 325	90.77 118	98.82 123	82.52 228	94.37 288	91.93 321

MVS_Test		92.57 165	93.29 140	90.40 234	93.53 277	75.85 276	92.52 160	96.96 132	88.73 167	92.35 208	96.70 111	90.77 118	98.37 188	92.53 84	95.49 265	96.99 194

MIMVSNet1		95.52 58	95.45 69	95.72 65	99.14 4	89.02 84	96.23 46	96.87 144	93.73 51	97.87 32	98.49 26	90.73 122	99.05 81	86.43 192	99.60 32	99.10 55

agg_prior3		92.56 166	91.62 174	95.35 76	96.39 158	89.45 75	90.61 231	96.82 145	78.82 281	90.03 254	94.14 225	90.72 123	98.88 109	88.66 161	98.43 147	97.72 159

ab-mvs		92.40 168	92.62 156	91.74 203	97.02 119	81.65 185	95.84 56	95.50 205	86.95 206	92.95 196	97.56 65	90.70 124	97.50 244	79.63 257	97.43 218	96.06 232

Test By Simon												90.61 125

3Dnovator	92.54 3	94.80 91	94.90 89	94.47 109	95.47 224	87.06 120	96.63 24	97.28 114	91.82 102	94.34 160	97.41 73	90.60 126	98.65 155	92.47 85	98.11 183	97.70 161

NCCC		94.08 116	93.54 136	95.70 67	96.49 150	89.90 71	92.39 169	96.91 140	90.64 128	92.33 211	94.60 209	90.58 127	98.96 97	90.21 128	97.70 204	98.23 123

UniMVSNet_NR-MVSNet		95.35 65	95.21 82	95.76 63	97.69 93	88.59 94	92.26 175	97.84 60	94.91 31	96.80 63	95.78 165	90.42 128	99.41 32	91.60 105	99.58 39	99.29 39

test_prior3		93.29 138	92.85 149	94.61 98	95.95 201	87.23 116	90.21 243	97.36 105	89.33 151	90.77 239	94.81 200	90.41 129	98.68 151	88.21 165	98.55 137	97.93 143

test_prior2							90.21 243		89.33 151	90.77 239	94.81 200	90.41 129		88.21 165	98.55 137

MSLP-MVS++		93.25 143	93.88 120	91.37 215	96.34 168	82.81 175	93.11 143	97.74 67	89.37 149	94.08 167	95.29 184	90.40 131	96.35 287	90.35 123	98.25 168	94.96 261

UniMVSNet (Re)		95.32 66	95.15 84	95.80 61	97.79 83	88.91 86	92.91 149	98.07 35	93.46 57	96.31 81	95.97 156	90.14 132	99.34 49	92.11 90	99.64 26	99.16 47

Effi-MVS+-dtu		93.90 120	92.60 157	97.77 4	94.74 248	96.67 4	94.00 114	95.41 207	89.94 141	91.93 218	92.13 271	90.12 133	98.97 96	87.68 173	97.48 216	97.67 164

mvs-test1		93.07 149	91.80 171	96.89 35	94.74 248	95.83 7	92.17 178	95.41 207	89.94 141	89.85 260	90.59 297	90.12 133	98.88 109	87.68 173	95.66 261	95.97 234

FMVSNet1		94.84 89	95.13 85	93.97 124	97.60 97	84.29 156	95.99 48	96.56 157	92.38 78	97.03 58	98.53 23	90.12 133	98.98 92	88.78 158	99.16 84	98.65 102

DU-MVS		95.28 70	95.12 86	95.75 64	97.75 85	88.59 94	92.58 157	97.81 62	93.99 45	96.80 63	95.90 157	90.10 136	99.41 32	91.60 105	99.58 39	99.26 40

NR-MVSNet		95.28 70	95.28 79	95.26 80	97.75 85	87.21 118	95.08 77	97.37 99	93.92 49	97.65 37	95.90 157	90.10 136	99.33 52	90.11 131	99.66 23	99.26 40

Baseline_NR-MVSNet		94.47 105	95.09 87	92.60 177	98.50 45	80.82 195	92.08 180	96.68 153	93.82 50	96.29 83	98.56 22	90.10 136	97.75 235	90.10 133	99.66 23	99.24 42

API-MVS		91.52 182	91.61 175	91.26 219	94.16 265	86.26 135	94.66 92	94.82 216	91.17 118	92.13 214	91.08 286	90.03 139	97.06 261	79.09 262	97.35 222	90.45 331

test12				94.43 112	95.95 201	86.75 125		96.24 178		89.76 263		89.79 140	98.79 130		97.95 194	97.75 158

v13		95.39 63	96.12 42	93.18 148	97.22 109	80.81 196	95.55 64	97.57 81	93.42 58	98.02 29	98.49 26	89.62 141	99.18 64	95.54 12	99.68 18	99.54 16

旧先验1					96.20 178	84.17 160		94.82 216			95.57 172	89.57 142			97.89 197	96.32 223

DELS-MVS		92.05 175	92.16 163	91.72 204	94.44 260	80.13 207	87.62 293	97.25 115	87.34 198	92.22 213	93.18 249	89.54 143	98.73 142	89.67 140	98.20 175	96.30 224
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
v12		95.29 69	96.02 50	93.10 150	97.14 115	80.63 197	95.39 68	97.55 85	93.19 61	97.98 30	98.44 30	89.40 144	99.16 65	95.38 16	99.67 21	99.52 20

VPNet		93.08 147	93.76 125	91.03 223	98.60 32	75.83 278	91.51 208	95.62 197	91.84 98	95.74 114	97.10 90	89.31 145	98.32 189	85.07 207	99.06 93	98.93 79

QAPM		92.88 154	92.77 151	93.22 147	95.82 206	83.31 168	96.45 33	97.35 107	83.91 237	93.75 173	96.77 104	89.25 146	98.88 109	84.56 212	97.02 229	97.49 173

MSDG		90.82 193	90.67 199	91.26 219	94.16 265	83.08 173	86.63 309	96.19 182	90.60 130	91.94 217	91.89 274	89.16 147	95.75 296	80.96 245	94.51 286	94.95 262

V9		95.17 75	95.89 54	93.02 153	97.04 118	80.42 199	95.22 74	97.53 86	92.92 68	97.90 31	98.35 33	89.15 148	99.14 69	95.21 18	99.65 25	99.50 22

v11		95.10 77	95.88 55	92.76 168	96.98 120	79.64 225	95.12 76	97.60 79	92.64 73	98.03 27	98.44 30	89.06 149	99.15 67	95.42 15	99.67 21	99.50 22

CPTT-MVS		94.74 94	94.12 113	96.60 40	98.15 65	93.01 39	95.84 56	97.66 72	89.21 155	93.28 186	95.46 176	88.89 150	98.98 92	89.80 137	98.82 119	97.80 156

V14		95.05 78	95.75 61	92.94 159	96.94 122	80.21 202	95.03 80	97.50 90	92.62 74	97.84 33	98.28 37	88.87 151	99.13 71	95.03 20	99.64 26	99.48 24

v15		94.93 83	95.62 65	92.86 164	96.83 128	80.01 215	94.84 87	97.48 91	92.36 79	97.76 35	98.20 39	88.61 152	99.11 74	94.86 22	99.62 29	99.46 25

DP-MVS Recon		92.31 170	91.88 168	93.60 135	97.18 111	86.87 123	91.10 219	97.37 99	84.92 230	92.08 215	94.08 227	88.59 153	98.20 200	83.50 219	98.14 179	95.73 241

FC-MVSNet-test		95.32 66	95.88 55	93.62 134	98.49 46	81.77 183	95.90 54	98.32 13	93.93 48	97.53 39	97.56 65	88.48 154	99.40 36	92.91 73	99.83 8	99.68 5

OpenMVS	89.45 8	92.27 172	92.13 165	92.68 173	94.53 259	84.10 161	95.70 59	97.03 126	82.44 253	91.14 236	96.42 124	88.47 155	98.38 185	85.95 196	97.47 217	95.55 250

v17		94.80 91	95.46 68	92.83 165	96.76 133	80.02 213	94.85 85	97.40 97	92.23 86	97.45 43	98.04 42	88.46 156	99.06 79	94.56 27	99.40 61	99.41 28

F-COLMAP		92.28 171	91.06 190	95.95 52	97.52 101	91.90 51	93.53 130	97.18 119	83.98 236	88.70 281	94.04 228	88.41 157	98.55 171	80.17 251	95.99 255	97.39 179

v16		94.79 93	95.44 71	92.83 165	96.73 134	80.03 211	94.85 85	97.41 96	92.23 86	97.41 46	98.04 42	88.40 158	99.06 79	94.56 27	99.30 70	99.41 28

ambc				92.98 155	96.88 126	83.01 174	95.92 53	96.38 170		96.41 76	97.48 70	88.26 159	97.80 229	89.96 136	98.93 105	98.12 133

v7		93.66 123	93.97 115	92.73 171	96.55 147	80.15 204	92.54 158	96.99 130	87.36 196	95.99 99	96.48 119	88.18 160	98.94 102	93.35 61	98.31 160	99.09 56

v10		94.68 97	95.27 80	92.90 162	96.57 146	80.15 204	94.65 93	97.57 81	90.68 127	97.43 44	98.00 46	88.18 160	99.15 67	94.84 24	99.55 42	99.41 28

v8		94.65 98	95.29 78	92.74 169	96.65 137	79.77 221	94.59 95	97.17 120	91.86 97	97.47 42	97.93 49	88.16 162	99.08 76	94.32 32	99.47 48	99.38 32

v18		94.63 99	95.26 81	92.74 169	96.60 144	79.81 219	94.64 94	97.37 99	91.87 96	97.26 49	97.91 52	88.13 163	99.04 84	94.30 34	99.24 76	99.38 32

v6		93.59 126	93.93 116	92.56 179	96.65 137	79.77 221	92.50 163	96.40 167	88.55 173	95.94 104	96.23 143	88.13 163	98.87 115	92.46 86	98.50 144	99.06 62

v1neww		93.58 127	93.92 118	92.56 179	96.64 141	79.77 221	92.50 163	96.41 165	88.55 173	95.93 105	96.24 141	88.08 165	98.87 115	92.45 87	98.50 144	99.05 63

v7new		93.58 127	93.92 118	92.56 179	96.64 141	79.77 221	92.50 163	96.41 165	88.55 173	95.93 105	96.24 141	88.08 165	98.87 115	92.45 87	98.50 144	99.05 63

TSAR-MVS + GP.		93.07 149	92.41 161	95.06 87	95.82 206	90.87 65	90.97 221	92.61 260	88.04 186	94.61 152	93.79 236	88.08 165	97.81 228	89.41 143	98.39 150	96.50 216

OurMVSNet-221017-0		96.80 14	96.75 20	96.96 32	99.03 10	91.85 52	97.98 6	98.01 43	94.15 44	98.93 4	99.07 5	88.07 168	99.57 13	95.86 11	99.69 15	99.46 25

原ACMM1				92.87 163	96.91 125	84.22 159		97.01 127	76.84 292	89.64 265	94.46 212	88.00 169	98.70 149	81.53 236	98.01 191	95.70 243

VDD-MVS		94.37 106	94.37 104	94.40 113	97.49 103	86.07 138	93.97 116	93.28 247	94.49 39	96.24 87	97.78 56	87.99 170	98.79 130	88.92 155	99.14 86	98.34 116

XVG-OURS		94.72 95	94.12 113	96.50 44	98.00 75	94.23 13	91.48 209	98.17 26	90.72 125	95.30 127	96.47 120	87.94 171	96.98 263	91.41 111	97.61 209	98.30 120

CANet		92.38 169	91.99 167	93.52 141	93.82 273	83.46 167	91.14 217	97.00 128	89.81 144	86.47 302	94.04 228	87.90 172	99.21 62	89.50 142	98.27 165	97.90 147

BH-untuned		90.68 197	90.90 191	90.05 245	95.98 199	79.57 228	90.04 251	94.94 214	87.91 187	94.07 168	93.00 250	87.76 173	97.78 231	79.19 261	95.17 274	92.80 308

FIs		94.90 85	95.35 73	93.55 137	98.28 57	81.76 184	95.33 70	98.14 28	93.05 63	97.07 53	97.18 85	87.65 174	99.29 54	91.72 101	99.69 15	99.61 12

v1144		93.50 129	93.81 121	92.57 178	96.28 172	79.61 227	91.86 196	96.96 132	86.95 206	95.91 108	96.32 136	87.65 174	98.96 97	93.51 52	98.88 108	99.13 50

mvs_anonymous		90.37 204	91.30 185	87.58 290	92.17 298	68.00 323	89.84 260	94.73 220	83.82 239	93.22 191	97.40 74	87.54 176	97.40 249	87.94 170	95.05 276	97.34 182

PCF-MVS	84.52 17	89.12 222	87.71 240	93.34 143	96.06 186	85.84 142	86.58 310	97.31 109	68.46 330	93.61 176	93.89 233	87.51 177	98.52 173	67.85 328	98.11 183	95.66 244
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1		93.43 132	93.77 124	92.41 185	96.37 159	79.24 233	91.84 197	96.38 170	88.33 180	95.87 109	96.22 146	87.45 178	98.89 105	92.61 81	98.83 116	99.09 56

VNet		92.67 161	92.96 146	91.79 202	96.27 173	80.15 204	91.95 184	94.98 212	92.19 89	94.52 155	96.07 152	87.43 179	97.39 250	84.83 209	98.38 151	97.83 153

v1141		93.42 134	93.76 125	92.40 187	96.37 159	79.24 233	91.84 197	96.38 170	88.33 180	95.86 110	96.23 143	87.41 180	98.89 105	92.61 81	98.82 119	99.08 59

divwei89l23v2f112		93.42 134	93.76 125	92.41 185	96.37 159	79.24 233	91.84 197	96.38 170	88.33 180	95.86 110	96.23 143	87.41 180	98.89 105	92.61 81	98.83 116	99.09 56

v148		92.87 155	93.29 140	91.62 207	96.25 176	77.72 257	91.28 214	95.05 211	89.69 145	95.93 105	96.04 153	87.34 182	98.38 185	90.05 134	97.99 192	98.78 94

V42		93.43 132	93.58 134	92.97 156	95.34 231	81.22 190	92.67 155	96.49 162	87.25 199	96.20 91	96.37 134	87.32 183	98.85 120	92.39 89	98.21 173	98.85 88

v1192		93.49 130	93.78 123	92.62 176	96.16 182	79.62 226	91.83 200	97.22 118	86.07 213	96.10 97	96.38 133	87.22 184	99.02 88	94.14 40	98.88 108	99.22 43

WR-MVS		93.49 130	93.72 128	92.80 167	97.57 99	80.03 211	90.14 247	95.68 196	93.70 52	96.62 71	95.39 182	87.21 185	99.04 84	87.50 175	99.64 26	99.33 36

IterMVS-LS		93.78 121	94.28 108	92.27 189	96.27 173	79.21 238	91.87 192	96.78 148	91.77 105	96.57 74	97.07 91	87.15 186	98.74 141	91.99 95	99.03 99	98.86 85
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		92.99 151	93.26 144	92.19 191	92.12 299	79.21 238	92.32 172	94.67 224	91.77 105	95.24 131	95.85 159	87.14 187	98.49 175	91.99 95	98.26 166	98.86 85

v144192		93.20 146	93.54 136	92.16 193	96.05 187	78.26 251	91.95 184	97.14 121	84.98 229	95.96 101	96.11 151	87.08 188	99.04 84	93.79 44	98.84 113	99.17 46

MVS_0304		92.99 151	92.54 158	94.35 115	94.67 253	86.06 139	91.16 216	97.92 55	90.01 140	88.33 285	94.41 213	87.02 189	99.22 61	90.36 122	99.00 100	97.76 157

diffmvs		90.45 200	90.49 200	90.34 235	92.25 294	77.09 264	91.80 203	95.96 188	82.68 248	85.83 306	95.07 191	87.01 190	97.09 259	89.68 139	94.10 294	96.83 203

114514_t		90.51 198	89.80 208	92.63 175	98.00 75	82.24 179	93.40 133	97.29 112	65.84 338	89.40 268	94.80 203	86.99 191	98.75 138	83.88 217	98.61 134	96.89 199

新几何1				93.17 149	97.16 112	87.29 115		94.43 226	67.95 331	91.29 225	94.94 197	86.97 192	98.23 197	81.06 243	97.75 200	93.98 285

HQP_MVS		94.26 112	93.93 116	95.23 82	97.71 90	88.12 105	94.56 99	97.81 62	91.74 107	93.31 183	95.59 168	86.93 193	98.95 99	89.26 148	98.51 142	98.60 108

plane_prior6					97.21 110	88.23 104						86.93 193

1121		90.26 208	89.23 210	93.34 143	97.15 114	87.40 114	91.94 186	94.39 227	67.88 332	91.02 237	94.91 198	86.91 195	98.59 161	81.17 241	97.71 203	94.02 284

UGNet		93.08 147	92.50 160	94.79 94	93.87 271	87.99 107	95.07 78	94.26 231	90.64 128	87.33 297	97.67 61	86.89 196	98.49 175	88.10 169	98.71 129	97.91 146
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
LF4IMVS		92.72 159	92.02 166	94.84 92	95.65 216	91.99 49	92.92 148	96.60 156	85.08 227	92.44 205	93.62 238	86.80 197	96.35 287	86.81 183	98.25 168	96.18 228

v1921920		93.26 141	93.61 133	92.19 191	96.04 190	78.31 250	91.88 191	97.24 116	85.17 223	96.19 93	96.19 148	86.76 198	99.05 81	94.18 39	98.84 113	99.22 43

v1240		93.29 138	93.71 129	92.06 196	96.01 191	77.89 255	91.81 201	97.37 99	85.12 225	96.69 68	96.40 126	86.67 199	99.07 78	94.51 29	98.76 126	99.22 43

test_normal		91.49 183	91.44 180	91.62 207	95.21 234	79.44 229	90.08 250	93.84 238	82.60 249	94.37 159	94.74 205	86.66 200	98.46 180	88.58 163	96.92 231	96.95 196

MAR-MVS		90.32 207	88.87 220	94.66 97	94.82 243	91.85 52	94.22 110	94.75 219	80.91 261	87.52 296	88.07 317	86.63 201	97.87 222	76.67 285	96.21 253	94.25 277
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
HSP-MVS		95.18 74	94.49 101	97.23 24	98.67 27	94.05 18	96.41 37	97.00 128	91.26 115	95.12 134	95.15 186	86.60 202	99.50 18	93.43 58	96.81 233	98.13 132

DI_MVS_plusplus_test		91.42 187	91.41 181	91.46 212	95.34 231	79.06 240	90.58 234	93.74 240	82.59 250	94.69 151	94.76 204	86.54 203	98.44 182	87.93 171	96.49 249	96.87 201

BH-RMVSNet		90.47 199	90.44 201	90.56 231	95.21 234	78.65 248	89.15 277	93.94 237	88.21 183	92.74 199	94.22 221	86.38 204	97.88 220	78.67 270	95.39 269	95.14 257

CNLPA		91.72 178	91.20 187	93.26 146	96.17 181	91.02 60	91.14 217	95.55 203	90.16 138	90.87 238	93.56 241	86.31 205	94.40 315	79.92 256	97.12 225	94.37 275

PVSNet_BlendedMVS		90.35 205	89.96 206	91.54 211	94.81 244	78.80 246	90.14 247	96.93 135	79.43 272	88.68 282	95.06 192	86.27 206	98.15 204	80.27 248	98.04 189	97.68 163

PVSNet_Blended		88.74 231	88.16 231	90.46 233	94.81 244	78.80 246	86.64 308	96.93 135	74.67 297	88.68 282	89.18 310	86.27 206	98.15 204	80.27 248	96.00 254	94.44 274

PAPR		87.65 251	86.77 256	90.27 238	92.85 285	77.38 260	88.56 287	96.23 179	76.82 293	84.98 311	89.75 305	86.08 208	97.16 257	72.33 309	93.35 301	96.26 226

v2v482		93.29 138	93.63 132	92.29 188	96.35 167	78.82 244	91.77 204	96.28 175	88.45 176	95.70 116	96.26 139	86.02 209	98.90 103	93.02 71	98.81 122	99.14 49

test20.03		90.80 194	90.85 194	90.63 230	95.63 218	79.24 233	89.81 261	92.87 253	89.90 143	94.39 156	96.40 126	85.77 210	95.27 307	73.86 301	99.05 95	97.39 179

PLC	85.34 15	90.40 202	88.92 218	94.85 91	96.53 148	90.02 68	91.58 206	96.48 163	80.16 267	86.14 304	92.18 270	85.73 211	98.25 196	76.87 284	94.61 285	96.30 224
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS		84.98 285	84.30 284	87.01 294	91.03 305	77.69 258	91.94 186	94.16 232	59.36 346	84.23 317	87.50 324	85.66 212	96.80 270	71.79 312	93.05 308	86.54 339

testdata				91.03 223	96.87 127	82.01 180		94.28 230	71.55 315	92.46 204	95.42 179	85.65 213	97.38 252	82.64 227	97.27 223	93.70 293

PM-MVS		93.33 137	92.67 155	95.33 78	96.58 145	94.06 16	92.26 175	92.18 265	85.92 216	96.22 89	96.61 114	85.64 214	95.99 293	90.35 123	98.23 170	95.93 236

MDA-MVSNet-bldmvs		91.04 191	90.88 192	91.55 210	94.68 252	80.16 203	85.49 315	92.14 268	90.41 135	94.93 144	95.79 163	85.10 215	96.93 265	85.15 203	94.19 293	97.57 169

PAPM_NR		91.03 192	90.81 195	91.68 206	96.73 134	81.10 192	93.72 127	96.35 174	88.19 184	88.77 279	92.12 272	85.09 216	97.25 254	82.40 230	93.90 295	96.68 206

HQP2-MVS												84.76 217

HQP-MVS		92.09 174	91.49 179	93.88 129	96.36 164	84.89 152	91.37 210	97.31 109	87.16 200	88.81 275	93.40 245	84.76 217	98.60 159	86.55 190	97.73 201	98.14 131

test222					96.95 121	85.27 150	88.83 283	93.61 241	65.09 340	90.74 241	94.85 199	84.62 219			97.36 221	93.91 286

VDDNet		94.03 117	94.27 110	93.31 145	98.87 19	82.36 178	95.51 66	91.78 273	97.19 10	96.32 80	98.60 20	84.24 220	98.75 138	87.09 181	98.83 116	98.81 91

PVSNet_Blended_VisFu		91.63 179	91.20 187	92.94 159	97.73 89	83.95 163	92.14 179	97.46 93	78.85 280	92.35 208	94.98 196	84.16 221	99.08 76	86.36 193	96.77 235	95.79 239

BH-w/o		87.21 260	87.02 251	87.79 289	94.77 246	77.27 262	87.90 291	93.21 251	81.74 258	89.99 256	88.39 315	83.47 222	96.93 265	71.29 317	92.43 313	89.15 333

PatchMatch-RL		89.18 220	88.02 234	92.64 174	95.90 205	92.87 42	88.67 286	91.06 277	80.34 265	90.03 254	91.67 278	83.34 223	94.42 314	76.35 288	94.84 280	90.64 330

OpenMVS_ROB	85.12 16	89.52 217	89.05 214	90.92 227	94.58 258	81.21 191	91.10 219	93.41 246	77.03 291	93.41 180	93.99 232	83.23 224	97.80 229	79.93 255	94.80 281	93.74 292

new-patchmatchnet		88.97 225	90.79 196	83.50 319	94.28 264	55.83 349	85.34 316	93.56 243	86.18 211	95.47 121	95.73 166	83.10 225	96.51 278	85.40 200	98.06 187	98.16 129

1314		86.46 276	86.33 264	86.87 296	91.65 302	74.54 290	91.94 186	94.10 233	74.28 301	84.78 313	87.33 326	83.03 226	95.00 309	78.72 269	91.16 324	91.06 327

IS-MVSNet		94.49 104	94.35 105	94.92 89	98.25 59	86.46 129	97.13 15	94.31 229	96.24 22	96.28 86	96.36 135	82.88 227	99.35 48	88.19 167	99.52 45	98.96 76

MG-MVS		89.54 216	89.80 208	88.76 271	94.88 240	72.47 311	89.60 264	92.44 263	85.82 218	89.48 267	95.98 155	82.85 228	97.74 236	81.87 232	95.27 272	96.08 231

TR-MVS		87.70 248	87.17 247	89.27 263	94.11 267	79.26 232	88.69 285	91.86 271	81.94 257	90.69 242	89.79 303	82.82 229	97.42 247	72.65 308	91.98 319	91.14 326

YYNet1		88.17 240	88.24 227	87.93 286	92.21 296	73.62 297	80.75 335	88.77 286	82.51 252	94.99 142	95.11 189	82.70 230	93.70 321	83.33 220	93.83 296	96.48 217

MDA-MVSNet_test_wron		88.16 241	88.23 228	87.93 286	92.22 295	73.71 296	80.71 336	88.84 285	82.52 251	94.88 145	95.14 187	82.70 230	93.61 322	83.28 221	93.80 297	96.46 218

pmmvs-eth3d		91.54 181	90.73 198	93.99 122	95.76 210	87.86 110	90.83 225	93.98 236	78.23 284	94.02 169	96.22 146	82.62 232	96.83 269	86.57 189	98.33 158	97.29 185

Anonymous20231206		88.77 230	88.29 225	90.20 243	96.31 170	78.81 245	89.56 266	93.49 245	74.26 302	92.38 207	95.58 171	82.21 233	95.43 302	72.07 310	98.75 128	96.34 222

USDC		89.02 223	89.08 213	88.84 270	95.07 238	74.50 292	88.97 280	96.39 169	73.21 308	93.27 187	96.28 138	82.16 234	96.39 284	77.55 278	98.80 123	95.62 246

EPP-MVSNet		93.91 119	93.68 131	94.59 103	98.08 70	85.55 147	97.44 10	94.03 234	94.22 43	94.94 143	96.19 148	82.07 235	99.57 13	87.28 180	98.89 106	98.65 102

UnsupCasMVSNet_eth		90.33 206	90.34 202	90.28 237	94.64 255	80.24 201	89.69 263	95.88 190	85.77 219	93.94 170	95.69 167	81.99 236	92.98 327	84.21 214	91.30 322	97.62 167

alignmvs		93.26 141	92.85 149	94.50 107	95.70 212	87.45 113	93.45 132	95.76 194	91.58 110	95.25 130	92.42 266	81.96 237	98.72 143	91.61 104	97.87 198	97.33 183

TAMVS		90.16 210	89.05 214	93.49 142	96.49 150	86.37 132	90.34 240	92.55 261	80.84 264	92.99 194	94.57 211	81.94 238	98.20 200	73.51 302	98.21 173	95.90 237

no-one		87.84 245	87.21 246	89.74 247	93.58 276	78.64 249	81.28 334	92.69 259	74.36 300	92.05 216	97.14 87	81.86 239	96.07 291	72.03 311	99.90 2	94.52 271

SixPastTwentyTwo		94.91 84	95.21 82	93.98 123	98.52 42	83.19 170	95.93 52	94.84 215	94.86 34	98.49 17	98.74 16	81.45 240	99.60 8	94.69 25	99.39 63	99.15 48

cascas		87.02 266	86.28 265	89.25 264	91.56 303	76.45 269	84.33 323	96.78 148	71.01 319	86.89 301	85.91 332	81.35 241	96.94 264	83.09 223	95.60 262	94.35 276

Test4		91.41 188	91.25 186	91.89 199	95.35 230	80.32 200	90.97 221	96.92 137	81.96 256	95.11 135	93.81 235	81.34 242	98.48 178	88.71 160	97.08 226	96.87 201

GBi-Net		93.21 144	92.96 146	93.97 124	95.40 226	84.29 156	95.99 48	96.56 157	88.63 169	95.10 136	98.53 23	81.31 243	98.98 92	86.74 184	98.38 151	98.65 102

test1		93.21 144	92.96 146	93.97 124	95.40 226	84.29 156	95.99 48	96.56 157	88.63 169	95.10 136	98.53 23	81.31 243	98.98 92	86.74 184	98.38 151	98.65 102

FMVSNet2		92.78 157	92.73 154	92.95 158	95.40 226	81.98 181	94.18 111	95.53 204	88.63 169	96.05 98	97.37 76	81.31 243	98.81 128	87.38 179	98.67 132	98.06 134

MVE	59.87 23	73.86 324	72.65 325	77.47 332	87.00 343	74.35 293	61.37 349	60.93 354	67.27 334	69.69 350	86.49 329	81.24 246	72.33 352	56.45 345	83.45 339	85.74 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmv		88.46 233	88.11 232	89.48 252	96.00 192	76.14 272	86.20 312	93.75 239	84.48 234	93.57 177	95.52 175	80.91 247	95.09 308	63.97 337	98.61 134	97.22 187

MVP-Stereo		90.07 212	88.92 218	93.54 139	96.31 170	86.49 127	90.93 223	95.59 201	79.80 268	91.48 221	95.59 168	80.79 248	97.39 250	78.57 271	91.19 323	96.76 205
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UnsupCasMVSNet_bld		88.50 232	88.03 233	89.90 246	95.52 223	78.88 243	87.39 298	94.02 235	79.32 277	93.06 192	94.02 230	80.72 249	94.27 317	75.16 298	93.08 307	96.54 207

MS-PatchMatch		88.05 242	87.75 239	88.95 268	93.28 279	77.93 253	87.88 292	92.49 262	75.42 296	92.57 203	93.59 240	80.44 250	94.24 319	81.28 238	92.75 310	94.69 268

CANet_DTU		89.85 213	89.17 212	91.87 200	92.20 297	80.02 213	90.79 226	95.87 191	86.02 214	82.53 327	91.77 276	80.01 251	98.57 164	85.66 198	97.70 204	97.01 193

PMMVS		83.00 294	81.11 303	88.66 274	83.81 352	86.44 130	82.24 331	85.65 312	61.75 345	82.07 330	85.64 333	79.75 252	91.59 333	75.99 290	93.09 306	87.94 338

N_pmnet		88.90 227	87.25 245	93.83 131	94.40 262	93.81 31	84.73 319	87.09 301	79.36 276	93.26 188	92.43 265	79.29 253	91.68 332	77.50 280	97.22 224	96.00 233

EPNet		89.80 214	88.25 226	94.45 111	83.91 351	86.18 136	93.87 123	87.07 302	91.16 119	80.64 338	94.72 206	78.83 254	98.89 105	85.17 201	98.89 106	98.28 121
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		87.23 259	86.82 254	88.46 281	93.96 268	77.94 252	86.84 305	92.78 257	77.59 286	87.61 295	91.83 275	78.75 255	91.92 331	77.84 275	94.20 292	95.52 251

Patchmatch-test1		87.28 257	87.30 244	87.22 293	92.01 301	71.98 313	89.43 268	88.11 294	82.26 255	88.71 280	92.20 269	78.65 256	95.81 295	80.99 244	93.30 302	93.87 289

jason		89.17 221	88.32 224	91.70 205	95.73 211	80.07 208	88.10 290	93.22 249	71.98 314	90.09 251	92.79 253	78.53 257	98.56 165	87.43 177	97.06 227	96.46 218
jason: jason.
IterMVS		90.18 209	90.16 204	90.21 242	93.15 282	75.98 275	87.56 296	92.97 252	86.43 210	94.09 166	96.40 126	78.32 258	97.43 246	87.87 172	94.69 283	97.23 186
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 1792x2688		87.19 262	85.92 275	91.00 226	97.13 116	79.41 230	84.51 322	95.60 198	64.14 341	90.07 253	94.81 200	78.26 259	97.14 258	73.34 303	95.38 270	96.46 218

WTY-MVS		86.93 269	86.50 263	88.24 283	94.96 239	74.64 288	87.19 301	92.07 270	78.29 283	88.32 286	91.59 281	78.06 260	94.27 317	74.88 299	93.15 305	95.80 238

pmmvs4		88.95 226	87.70 241	92.70 172	94.30 263	85.60 146	87.22 300	92.16 267	74.62 298	89.75 264	94.19 222	77.97 261	96.41 283	82.71 226	96.36 250	96.09 230

DSMNet-mixed		82.21 300	81.56 299	84.16 316	89.57 324	70.00 319	90.65 230	77.66 349	54.99 349	83.30 323	97.57 64	77.89 262	90.50 338	66.86 331	95.54 264	91.97 320

lessismore_v0				93.87 130	98.05 71	83.77 165		80.32 346		97.13 52	97.91 52	77.49 263	99.11 74	92.62 80	98.08 186	98.74 97

HY-MVS	82.50 18	86.81 271	85.93 274	89.47 253	93.63 275	77.93 253	94.02 113	91.58 274	75.68 294	83.64 320	93.64 237	77.40 264	97.42 247	71.70 314	92.07 318	93.05 304

1112_ss		88.42 234	87.41 242	91.45 213	96.69 136	80.99 193	89.72 262	96.72 152	73.37 307	87.00 300	90.69 294	77.38 265	98.20 200	81.38 237	93.72 298	95.15 256

semantic-postprocess				91.94 198	93.89 270	79.22 237		93.51 244	91.53 112	95.37 125	96.62 113	77.17 266	98.90 103	91.89 99	94.95 277	97.70 161

CDS-MVSNet		89.55 215	88.22 229	93.53 140	95.37 229	86.49 127	89.26 274	93.59 242	79.76 269	91.15 235	92.31 268	77.12 267	98.38 185	77.51 279	97.92 196	95.71 242
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSFormer		92.18 173	92.23 162	92.04 197	94.74 248	80.06 209	97.15 13	97.37 99	88.98 156	88.83 273	92.79 253	77.02 268	99.60 8	96.41 6	96.75 236	96.46 218

lupinMVS		88.34 235	87.31 243	91.45 213	94.74 248	80.06 209	87.23 299	92.27 264	71.10 318	88.83 273	91.15 284	77.02 268	98.53 172	86.67 187	96.75 236	95.76 240

PMMVS2		81.31 306	83.44 289	74.92 334	90.52 313	46.49 351	69.19 347	85.23 321	84.30 235	87.95 290	94.71 207	76.95 270	84.36 349	64.07 336	98.09 185	93.89 287

pmmvs5		87.87 244	87.14 248	90.07 244	93.26 281	76.97 267	88.89 282	92.18 265	73.71 306	88.36 284	93.89 233	76.86 271	96.73 272	80.32 247	96.81 233	96.51 209

testus		82.09 302	81.78 297	83.03 321	92.35 292	64.37 340	79.44 337	93.27 248	73.08 309	87.06 299	85.21 335	76.80 272	89.27 342	53.30 346	95.48 266	95.46 252

K. test v3		93.37 136	93.27 143	93.66 133	98.05 71	82.62 176	94.35 106	86.62 304	96.05 26	97.51 40	98.85 12	76.59 273	99.65 3	93.21 65	98.20 175	98.73 99

Test_1112_low_res		87.50 253	86.58 258	90.25 239	96.80 131	77.75 256	87.53 297	96.25 177	69.73 326	86.47 302	93.61 239	75.67 274	97.88 220	79.95 253	93.20 303	95.11 258

Vis-MVSNet (Re-imp)		90.42 201	90.16 204	91.20 221	97.66 96	77.32 261	94.33 107	87.66 297	91.20 117	92.99 194	95.13 188	75.40 275	98.28 191	77.86 274	99.19 81	97.99 138

PVSNet	76.22 20	82.89 295	82.37 294	84.48 314	93.96 268	64.38 339	78.60 339	88.61 287	71.50 316	84.43 316	86.36 331	74.27 276	94.60 311	69.87 325	93.69 299	94.46 273

CMPMVS	68.83 22	87.28 257	85.67 276	92.09 195	88.77 332	85.42 148	90.31 241	94.38 228	70.02 325	88.00 289	93.30 247	73.78 277	94.03 320	75.96 291	96.54 244	96.83 203
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	70.34 21	74.58 322	72.96 324	79.47 329	90.63 311	66.24 332	73.26 342	83.40 331	63.67 343	78.02 343	78.35 347	72.53 278	89.59 341	56.68 344	60.05 350	82.57 345

MIMVSNet		87.13 264	86.54 260	88.89 269	96.05 187	76.11 273	94.39 104	88.51 288	81.37 260	88.27 287	96.75 107	72.38 279	95.52 298	65.71 335	95.47 267	95.03 259

PAPM		81.91 303	80.11 313	87.31 292	93.87 271	72.32 312	84.02 325	93.22 249	69.47 327	76.13 346	89.84 300	72.15 280	97.23 255	53.27 347	89.02 328	92.37 313

LFMVS		91.33 189	91.16 189	91.82 201	96.27 173	79.36 231	95.01 81	85.61 314	96.04 27	94.82 146	97.06 92	72.03 281	98.46 180	84.96 208	98.70 130	97.65 165

MVS-HIRNet		78.83 319	80.60 308	73.51 335	93.07 283	47.37 350	87.10 302	78.00 348	68.94 328	77.53 344	97.26 81	71.45 282	94.62 310	63.28 339	88.74 329	78.55 347

EPNet_dtu		85.63 281	84.37 283	89.40 260	86.30 344	74.33 294	91.64 205	88.26 290	84.84 232	72.96 349	89.85 299	71.27 283	97.69 238	76.60 286	97.62 208	96.18 228
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test1235678		84.54 286	83.85 288	86.59 297	93.81 274	73.41 299	82.38 329	91.79 272	79.43 272	89.50 266	91.61 280	70.59 284	92.94 328	58.14 343	97.40 220	93.44 299

LP		86.29 277	85.35 278	89.10 266	87.80 334	76.21 271	89.92 255	90.99 278	84.86 231	87.66 293	92.32 267	70.40 285	96.48 279	81.94 231	82.24 343	94.63 269

HyFIR lowres test		87.19 262	85.51 277	92.24 190	97.12 117	80.51 198	85.03 317	96.06 184	66.11 337	91.66 220	92.98 251	70.12 286	99.14 69	75.29 297	95.23 273	97.07 191

FMVSNet3		90.78 195	90.32 203	92.16 193	93.03 284	79.92 217	92.54 158	94.95 213	86.17 212	95.10 136	96.01 154	69.97 287	98.75 138	86.74 184	98.38 151	97.82 155

RPMNet		89.30 219	89.00 216	90.22 240	91.01 306	78.93 241	92.52 160	87.85 296	91.91 94	89.10 270	96.89 99	68.84 288	97.64 240	90.17 129	92.70 311	94.08 279

ADS-MVSNet2		84.01 290	82.20 296	89.41 259	89.04 329	76.37 270	87.57 294	90.98 279	72.71 312	84.46 314	92.45 262	68.08 289	96.48 279	70.58 323	83.97 336	95.38 253

ADS-MVSNet		82.25 299	81.55 300	84.34 315	89.04 329	65.30 333	87.57 294	85.13 322	72.71 312	84.46 314	92.45 262	68.08 289	92.33 330	70.58 323	83.97 336	95.38 253

CVMVSNet		85.16 283	84.72 281	86.48 298	92.12 299	70.19 317	92.32 172	88.17 293	56.15 348	90.64 243	95.85 159	67.97 291	96.69 273	88.78 158	90.52 326	92.56 311

new_pmnet		81.22 307	81.01 306	81.86 325	90.92 308	70.15 318	84.03 324	80.25 347	70.83 321	85.97 305	89.78 304	67.93 292	84.65 348	67.44 329	91.90 320	90.78 328

CR-MVSNet		87.89 243	87.12 249	90.22 240	91.01 306	78.93 241	92.52 160	92.81 254	73.08 309	89.10 270	96.93 96	67.11 293	97.64 240	88.80 157	92.70 311	94.08 279

Patchmtry		90.11 211	89.92 207	90.66 229	90.35 317	77.00 266	92.96 147	92.81 254	90.25 137	94.74 149	96.93 96	67.11 293	97.52 243	85.17 201	98.98 101	97.46 174

PatchmatchNet		85.22 282	84.64 282	86.98 295	89.51 325	69.83 320	90.52 235	87.34 300	78.87 279	87.22 298	92.74 255	66.91 295	96.53 276	81.77 233	86.88 334	94.58 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
GA-MVS		87.70 248	86.82 254	90.31 236	93.27 280	77.22 263	84.72 321	92.79 256	85.11 226	89.82 261	90.07 298	66.80 296	97.76 234	84.56 212	94.27 291	95.96 235

MDTV_nov1_ep13_2view						42.48 354	88.45 288		67.22 335	83.56 321		66.80 296		72.86 307		94.06 281

tpmrst		82.85 296	82.93 293	82.64 323	87.65 335	58.99 346	90.14 247	87.90 295	75.54 295	83.93 318	91.63 279	66.79 298	95.36 303	81.21 240	81.54 344	93.57 298

sam_mvs1												66.64 299				94.75 266

sam_mvs												66.41 300

Patchmatch-RL test		88.81 229	88.52 222	89.69 251	95.33 233	79.94 216	86.22 311	92.71 258	78.46 282	95.80 112	94.18 223	66.25 301	95.33 305	89.22 150	98.53 140	93.78 290

patchmatchnet-post											91.71 277	66.22 302	97.59 242

test_post											6.07 354	65.74 303	95.84 294

test_post1							90.21 243				5.85 355	65.36 304	96.00 292	79.61 258

MDTV_nov1_ep13			83.88 287		89.42 326	61.52 343	88.74 284	87.41 299	73.99 304	84.96 312	94.01 231	65.25 305	95.53 297	78.02 273	93.16 304

Patchmatch-test		86.10 278	86.01 273	86.38 300	90.63 311	74.22 295	89.57 265	86.69 303	85.73 220	89.81 262	92.83 252	65.24 306	91.04 334	77.82 277	95.78 260	93.88 288

tpmvs		84.22 289	83.97 286	84.94 310	87.09 341	65.18 334	91.21 215	88.35 289	82.87 247	85.21 308	90.96 288	65.24 306	96.75 271	79.60 259	85.25 335	92.90 306

EU-MVSNet		87.39 255	86.71 257	89.44 258	93.40 278	76.11 273	94.93 84	90.00 283	57.17 347	95.71 115	97.37 76	64.77 308	97.68 239	92.67 79	94.37 288	94.52 271

thres200		85.85 279	85.18 279	87.88 288	94.44 260	72.52 310	89.08 278	86.21 306	88.57 172	91.44 223	88.40 314	64.22 309	98.00 209	68.35 327	95.88 259	93.12 303

PatchT		87.51 252	88.17 230	85.55 304	90.64 310	66.91 327	92.02 182	86.09 307	92.20 88	89.05 272	97.16 86	64.15 310	96.37 286	89.21 151	92.98 309	93.37 301

tfpn200view9		87.05 265	86.52 261	88.67 273	95.77 208	72.94 308	91.89 189	86.00 309	90.84 122	92.61 201	89.80 301	63.93 311	98.28 191	71.27 318	96.54 244	94.79 264

thres400		87.20 261	86.52 261	89.24 265	95.77 208	72.94 308	91.89 189	86.00 309	90.84 122	92.61 201	89.80 301	63.93 311	98.28 191	71.27 318	96.54 244	96.51 209

FPMVS		84.50 287	83.28 290	88.16 284	96.32 169	94.49 11	85.76 313	85.47 315	83.09 244	85.20 309	94.26 219	63.79 313	86.58 347	63.72 338	91.88 321	83.40 342

conf200view11		87.41 254	86.89 252	88.97 267	96.14 183	73.09 306	93.00 145	85.31 317	92.13 90	93.26 188	90.96 288	63.42 314	98.28 191	71.27 318	96.54 244	95.56 247

thres100view900		87.35 256	86.89 252	88.72 272	96.14 183	73.09 306	93.00 145	85.31 317	92.13 90	93.26 188	90.96 288	63.42 314	98.28 191	71.27 318	96.54 244	94.79 264

thres600view7		87.66 250	87.10 250	89.36 261	96.05 187	73.17 304	92.72 153	85.31 317	91.89 95	93.29 185	90.97 287	63.42 314	98.39 183	73.23 304	96.99 230	96.51 209

view600		88.32 236	87.94 235	89.46 254	96.49 150	73.31 300	93.95 117	84.46 326	93.02 64	94.18 161	92.68 258	63.33 317	98.56 165	75.87 292	97.50 212	96.51 209

view800		88.32 236	87.94 235	89.46 254	96.49 150	73.31 300	93.95 117	84.46 326	93.02 64	94.18 161	92.68 258	63.33 317	98.56 165	75.87 292	97.50 212	96.51 209

conf0.05thres1000		88.32 236	87.94 235	89.46 254	96.49 150	73.31 300	93.95 117	84.46 326	93.02 64	94.18 161	92.68 258	63.33 317	98.56 165	75.87 292	97.50 212	96.51 209

tfpn		88.32 236	87.94 235	89.46 254	96.49 150	73.31 300	93.95 117	84.46 326	93.02 64	94.18 161	92.68 258	63.33 317	98.56 165	75.87 292	97.50 212	96.51 209

EMVS		80.35 315	80.28 312	80.54 327	84.73 350	69.07 321	72.54 345	80.73 344	87.80 191	81.66 334	81.73 343	62.89 321	89.84 340	75.79 296	94.65 284	82.71 344

test-LLR		83.58 291	83.17 291	84.79 312	89.68 322	66.86 329	83.08 326	84.52 324	83.07 245	82.85 325	84.78 336	62.86 322	93.49 323	82.85 224	94.86 278	94.03 282

test0.0.03 1		82.48 298	81.47 301	85.48 305	89.70 321	73.57 298	84.73 319	81.64 342	83.07 245	88.13 288	86.61 327	62.86 322	89.10 344	66.24 334	90.29 327	93.77 291

tpm cat1		80.61 313	79.46 315	84.07 317	88.78 331	65.06 337	89.26 274	88.23 291	62.27 344	81.90 333	89.66 307	62.70 324	95.29 306	71.72 313	80.60 345	91.86 323

E-PMN		80.72 312	80.86 307	80.29 328	85.11 348	68.77 322	72.96 343	81.97 341	87.76 192	83.25 324	83.01 342	62.22 325	89.17 343	77.15 283	94.31 290	82.93 343

CostFormer		83.09 293	82.21 295	85.73 303	89.27 328	67.01 326	90.35 239	86.47 305	70.42 323	83.52 322	93.23 248	61.18 326	96.85 268	77.21 282	88.26 332	93.34 302

MVSTER		89.32 218	88.75 221	91.03 223	90.10 319	76.62 268	90.85 224	94.67 224	82.27 254	95.24 131	95.79 163	61.09 327	98.49 175	90.49 116	98.26 166	97.97 142

tpm		84.38 288	84.08 285	85.30 309	90.47 314	63.43 342	89.34 271	85.63 313	77.24 290	87.62 294	95.03 195	61.00 328	97.30 253	79.26 260	91.09 325	95.16 255

PatchFormer-LS_test		82.62 297	81.71 298	85.32 308	87.92 333	67.31 325	89.03 279	88.20 292	77.58 287	83.79 319	80.50 346	60.96 329	96.42 282	83.86 218	83.59 338	92.23 318

EPMVS		81.17 309	80.37 310	83.58 318	85.58 347	65.08 336	90.31 241	71.34 351	77.31 289	85.80 307	91.30 282	59.38 330	92.70 329	79.99 252	82.34 342	92.96 305

tmp_tt		37.97 328	44.33 328	18.88 341	11.80 355	21.54 356	63.51 348	45.66 357	4.23 351	51.34 352	50.48 350	59.08 331	22.11 354	44.50 350	68.35 349	13.00 351

conf0.01		86.95 267	86.04 267	89.70 249	95.99 193	75.66 279	93.28 135	82.70 333	88.81 161	91.26 226	88.01 318	58.77 332	97.89 214	78.93 263	96.60 238	95.56 247

conf0.002		86.95 267	86.04 267	89.70 249	95.99 193	75.66 279	93.28 135	82.70 333	88.81 161	91.26 226	88.01 318	58.77 332	97.89 214	78.93 263	96.60 238	95.56 247

thresconf0.02		86.69 272	86.04 267	88.64 275	95.99 193	75.66 279	93.28 135	82.70 333	88.81 161	91.26 226	88.01 318	58.77 332	97.89 214	78.93 263	96.60 238	92.36 314

tfpn_n400		86.69 272	86.04 267	88.64 275	95.99 193	75.66 279	93.28 135	82.70 333	88.81 161	91.26 226	88.01 318	58.77 332	97.89 214	78.93 263	96.60 238	92.36 314

tfpnconf		86.69 272	86.04 267	88.64 275	95.99 193	75.66 279	93.28 135	82.70 333	88.81 161	91.26 226	88.01 318	58.77 332	97.89 214	78.93 263	96.60 238	92.36 314

tfpnview11		86.69 272	86.04 267	88.64 275	95.99 193	75.66 279	93.28 135	82.70 333	88.81 161	91.26 226	88.01 318	58.77 332	97.89 214	78.93 263	96.60 238	92.36 314

tpm2		81.46 305	80.35 311	84.80 311	89.90 320	65.14 335	90.44 237	85.36 316	65.82 339	82.05 331	92.44 264	57.94 338	96.69 273	70.71 322	88.49 331	92.56 311

tfpn_ndepth		85.85 279	85.15 280	87.98 285	95.19 236	75.36 285	92.79 152	83.18 332	86.97 204	89.92 257	86.43 330	57.44 339	97.85 226	78.18 272	96.22 252	90.72 329

tfpn1000		86.83 270	86.23 266	88.64 275	95.53 222	75.25 286	93.57 129	82.28 340	89.27 153	91.46 222	89.24 309	57.22 340	97.86 223	80.63 246	96.88 232	92.81 307

CHOSEN 280x420		80.04 316	77.97 320	86.23 302	90.13 318	74.53 291	72.87 344	89.59 284	66.38 336	76.29 345	85.32 334	56.96 341	95.36 303	69.49 326	94.72 282	88.79 336

JIA-IIPM		85.08 284	83.04 292	91.19 222	87.56 336	86.14 137	89.40 270	84.44 330	88.98 156	82.20 329	97.95 48	56.82 342	96.15 289	76.55 287	83.45 339	91.30 325

tpmp4_e23		81.87 304	80.41 309	86.27 301	89.29 327	67.84 324	91.58 206	87.61 298	67.42 333	78.60 342	92.71 256	56.42 343	96.87 267	71.44 316	88.63 330	94.10 278

DeepMVS_CX				53.83 338	70.38 354	64.56 338		48.52 356	33.01 350	65.50 351	74.21 349	56.19 344	46.64 353	38.45 351	70.07 348	50.30 350

dp		79.28 317	78.62 318	81.24 326	85.97 346	56.45 348	86.91 304	85.26 320	72.97 311	81.45 335	89.17 311	56.01 345	95.45 301	73.19 305	76.68 347	91.82 324

FMVSNet5		87.82 247	86.56 259	91.62 207	92.31 293	79.81 219	93.49 131	94.81 218	83.26 240	91.36 224	96.93 96	52.77 346	97.49 245	76.07 289	98.03 190	97.55 172

test12356		76.35 320	77.41 321	73.19 336	90.70 309	38.86 355	74.56 341	91.14 276	74.55 299	80.54 339	88.18 316	52.36 347	90.49 339	52.38 348	92.26 315	90.21 332

pmmvs3		80.83 310	78.96 317	86.45 299	87.23 340	77.48 259	84.87 318	82.31 339	63.83 342	85.03 310	89.50 308	49.66 348	93.10 325	73.12 306	95.10 275	88.78 337

DWT-MVSNet_test		80.74 311	79.18 316	85.43 306	87.51 338	66.87 328	89.87 259	86.01 308	74.20 303	80.86 336	80.62 345	48.84 349	96.68 275	81.54 235	83.14 341	92.75 309

IB-MVS	77.21 19	83.11 292	81.05 304	89.29 262	91.15 304	75.85 276	85.66 314	86.00 309	79.70 270	82.02 332	86.61 327	48.26 350	98.39 183	77.84 275	92.22 316	93.63 294
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
gg-mvs-nofinetune		82.10 301	81.02 305	85.34 307	87.46 339	71.04 315	94.74 89	67.56 352	96.44 19	79.43 341	98.99 6	45.24 351	96.15 289	67.18 330	92.17 317	88.85 335

GG-mvs-BLEND				83.24 320	85.06 349	71.03 316	94.99 83	65.55 353		74.09 348	75.51 348	44.57 352	94.46 313	59.57 342	87.54 333	84.24 341

TESTMET0.1,1		79.09 318	78.04 319	82.25 324	87.52 337	64.03 341	83.08 326	80.62 345	70.28 324	80.16 340	83.22 341	44.13 353	90.56 337	79.95 253	93.36 300	92.15 319

test-mter		81.21 308	80.01 314	84.79 312	89.68 322	66.86 329	83.08 326	84.52 324	73.85 305	82.85 325	84.78 336	43.66 354	93.49 323	82.85 224	94.86 278	94.03 282

testpf		74.01 323	76.37 322	66.95 337	80.56 353	60.00 344	88.43 289	75.07 350	81.54 259	75.75 347	83.73 338	38.93 355	83.09 350	84.01 215	79.32 346	57.75 349

test2356		75.58 321	73.13 323	82.95 322	86.10 345	66.42 331	75.07 340	84.87 323	70.91 320	80.85 337	80.66 344	38.02 356	88.98 345	49.32 349	92.35 314	93.44 299

1111		80.36 314	81.32 302	77.48 331	94.61 256	44.56 352	81.59 332	90.66 281	86.78 208	90.60 244	93.52 242	30.37 357	90.67 335	66.36 332	97.42 219	97.20 188

.test1245		64.72 326	70.88 327	46.22 339	94.61 256	44.56 352	81.59 332	90.66 281	86.78 208	90.60 244	93.52 242	30.37 357	90.67 335	66.36 332	3.45 353	3.44 353

PNet_i23d		72.03 325	70.91 326	75.38 333	90.46 315	57.84 347	71.73 346	81.53 343	83.86 238	82.21 328	83.49 340	29.97 359	87.80 346	60.78 340	54.12 351	80.51 346

test123		9.49 330	12.01 331	1.91 342	2.87 356	1.30 357	82.38 329	1.34 359	1.36 352	2.84 353	6.56 353	2.45 360	0.97 355	2.73 352	5.56 352	3.47 352

testmvs		9.02 331	11.42 332	1.81 343	2.77 357	1.13 358	79.44 337	1.90 358	1.18 353	2.65 354	6.80 352	1.95 361	0.87 356	2.62 353	3.45 353	3.44 353

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

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

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

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

ab-mvs-re		7.56 332	10.08 334	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	90.69 294	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

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

GSMVS																94.75 266

test_part3							93.92 121		91.83 100		96.39 130		99.44 24	89.00 153

test_part2					98.21 61	89.41 76				96.72 66

MTGPA								97.62 74

MTMP								54.62 355

gm-plane-assit					87.08 342	59.33 345			71.22 317		83.58 339		97.20 256	73.95 300

test9_res														88.16 168	98.40 149	97.83 153

agg_prior2														87.06 182	98.36 157	97.98 139

agg_prior					96.20 178	88.89 87		96.88 142		90.21 249			98.78 133

test_prior4						89.91 70	90.74 227

test_prior				94.61 98	95.95 201	87.23 116		97.36 105					98.68 151			97.93 143

旧先验2							90.00 253		68.65 329	92.71 200			96.52 277	85.15 203

新几何2							90.02 252

无先验							89.94 254	95.75 195	70.81 322				98.59 161	81.17 241		94.81 263

原ACMM2							89.34 271

testdata2													98.03 208	80.24 250

testdata1							88.96 281		88.44 177

plane_prior7					97.71 90	88.68 91

plane_prior5								97.81 62					98.95 99	89.26 148	98.51 142	98.60 108

plane_prior4											95.59 168

plane_prior3						88.43 102			90.35 136	93.31 183

plane_prior2							94.56 99		91.74 107

plane_prior1					97.38 105

plane_prior						88.12 105	93.01 144		88.98 156						98.06 187

n2								0.00 360

nn								0.00 360

door-mid								92.13 269

test11								96.65 154

door								91.26 275

HQP5-MVS						84.89 152

HQP-NCC					96.36 164		91.37 210		87.16 200	88.81 275

ACMP_Plane					96.36 164		91.37 210		87.16 200	88.81 275

BP-MVS														86.55 190

HQP4-MVS										88.81 275			98.61 157			98.15 130

HQP3-MVS								97.31 109							97.73 201

NP-MVS					96.82 129	87.10 119					93.40 245

ACMMP++_ref															98.82 119

ACMMP++															99.25 75

High-resolution multi-view benchmark