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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

HSP-MVS		99.36 5	99.79 4	98.85 7	99.61 10	99.96 7	99.71 20	96.94 5	99.97 7	97.11 9	99.60 23	100.00 1	99.70 17	99.96 1	99.12 30	100.00 1	99.96 100

CNVR-MVS		99.39 2	99.75 11	98.98 1	99.69 1	99.95 12	99.76 6	96.91 7	99.98 3	97.59 5	99.64 19	100.00 1	99.93 1	99.94 2	98.75 47	99.97 11	99.97 81

DeepPCF-MVS	97.16 4	97.58 43	99.72 17	95.07 58	98.45 46	99.96 7	93.83 140	95.93 32	100.00 1	90.79 57	98.38 68	99.85 39	95.28 130	99.94 2	99.97 1	96.15 226	99.97 81

DeepC-MVS_fast	98.03 2	99.05 19	99.78 6	98.21 22	99.47 19	99.97 3	99.75 12	96.80 15	99.97 7	93.58 35	98.68 64	99.94 35	99.69 18	99.93 4	99.95 2	99.96 12	99.98 68
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ESAPD		99.37 4	99.74 14	98.94 3	99.60 12	99.94 17	99.87 1	96.95 3	99.94 22	97.42 7	99.62 21	100.00 1	99.80 13	99.91 5	98.78 44	99.98 9	100.00 1

TSAR-MVS + MP.		98.99 20	99.61 26	98.27 20	97.88 50	99.92 36	99.71 20	96.80 15	99.96 16	95.58 22	98.71 63	100.00 1	99.68 20	99.91 5	98.78 44	99.99 6	100.00 1

MCST-MVS		99.08 17	99.72 17	98.33 19	99.59 15	99.97 3	99.78 4	96.96 2	99.95 20	93.72 32	99.67 11	100.00 1	99.90 4	99.91 5	98.55 51	100.00 1	100.00 1

SteuartSystems-ACMMP		98.95 22	99.80 3	97.95 25	99.43 24	99.96 7	99.76 6	96.45 29	99.82 48	93.63 33	99.64 19	100.00 1	98.56 76	99.90 8	99.31 23	99.84 31	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		99.34 6	99.79 4	98.81 9	99.69 1	99.94 17	99.75 12	96.91 7	99.98 3	96.76 11	99.37 38	100.00 1	99.90 4	99.88 9	99.46 16	99.84 31	99.92 121

v1.0		91.56 143	85.41 218	98.74 10	99.62 6	99.94 17	99.79 3	96.87 11	99.93 25	96.33 16	99.59 24	100.00 1	99.84 8	99.88 9	98.50 54	100.00 1	0.00 242

APDe-MVS		99.40 1	99.81 2	98.92 4	99.62 6	99.96 7	99.76 6	96.87 11	99.95 20	97.66 4	99.57 27	100.00 1	99.63 25	99.88 9	99.28 25	100.00 1	100.00 1

train_agg		98.62 26	99.76 8	97.28 30	99.03 38	99.93 30	99.65 24	96.37 30	99.98 3	89.24 82	99.53 28	99.83 40	99.59 27	99.85 12	99.19 28	99.80 52	100.00 1

NCCC		99.24 8	99.75 11	98.65 12	99.63 5	99.96 7	99.76 6	96.91 7	99.97 7	95.86 20	99.67 11	100.00 1	99.75 14	99.85 12	98.80 42	99.98 9	99.97 81

ACMMP_Plus		98.68 25	99.58 29	97.62 28	99.62 6	99.92 36	99.72 19	96.78 17	99.71 63	90.13 71	99.66 15	99.99 27	99.64 24	99.78 14	98.14 61	99.82 41	99.89 139

CP-MVS		99.14 14	99.67 20	98.53 15	99.45 21	99.94 17	99.63 26	96.62 25	99.82 48	95.92 19	99.65 16	100.00 1	99.71 16	99.76 15	98.56 50	99.83 37	100.00 1

zzz-MVS		99.12 15	99.52 34	98.65 12	99.58 16	99.93 30	99.74 15	96.72 21	99.44 85	96.47 14	99.62 21	100.00 1	99.63 25	99.74 16	97.97 65	99.77 68	99.94 113

HPM-MVS++		98.98 21	99.62 25	98.22 21	99.62 6	99.94 17	99.74 15	96.95 3	99.87 38	93.76 31	99.49 32	100.00 1	99.39 36	99.73 17	98.35 56	99.89 25	99.96 100

TSAR-MVS + ACMM		98.30 30	99.64 22	96.74 37	99.08 37	99.94 17	99.67 23	96.73 20	99.97 7	86.30 101	98.30 69	99.99 27	98.78 70	99.73 17	99.57 13	99.88 28	99.98 68

APD-MVS		99.33 7	99.85 1	98.73 11	99.61 10	99.92 36	99.77 5	96.91 7	99.93 25	96.31 17	99.59 24	99.95 34	99.84 8	99.73 17	99.84 8	99.95 14	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVS		98.82 24	99.63 23	97.88 27	99.41 25	99.91 43	99.74 15	96.76 19	99.88 35	91.89 43	99.50 31	99.94 35	99.65 23	99.71 20	98.49 55	99.82 41	99.97 81
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
X-MVS		98.62 26	99.75 11	97.29 29	99.50 18	99.94 17	99.71 20	96.55 26	99.85 43	88.58 87	99.65 16	99.98 29	99.67 21	99.60 21	99.26 26	99.77 68	99.97 81

HFP-MVS		99.19 11	99.77 7	98.51 16	99.55 17	99.94 17	99.76 6	96.84 13	99.88 35	95.27 24	99.67 11	100.00 1	99.85 7	99.56 22	99.36 20	99.79 56	99.97 81

CPTT-MVS		99.08 17	99.53 33	98.57 14	99.44 23	99.93 30	99.60 27	95.92 33	99.77 55	97.01 10	99.67 11	100.00 1	99.72 15	99.56 22	97.76 76	99.70 112	99.98 68

ACMMPR		99.12 15	99.76 8	98.36 18	99.45 21	99.94 17	99.75 12	96.70 22	99.93 25	94.65 28	99.65 16	99.96 32	99.84 8	99.51 24	99.35 21	99.79 56	99.96 100

MVS_111021_LR		98.15 34	99.69 19	96.36 42	99.23 34	99.93 30	97.79 62	91.84 41	99.87 38	90.53 65	100.00 1	99.57 52	98.93 62	99.44 25	99.08 32	99.85 29	99.95 109

PGM-MVS		98.47 29	99.73 15	97.00 34	99.68 3	99.94 17	99.76 6	91.74 42	99.84 46	91.17 54	100.00 1	99.69 47	99.81 11	99.38 26	99.30 24	99.82 41	99.95 109

CDPH-MVS		97.88 40	99.59 27	95.89 47	98.90 39	99.95 12	99.40 31	92.86 40	99.86 42	85.33 104	98.62 65	99.45 56	99.06 58	99.29 27	99.94 3	99.81 49	100.00 1

AdaColmap		99.21 10	99.45 35	98.92 4	99.67 4	99.95 12	99.65 24	96.77 18	99.97 7	97.67 3	100.00 1	99.69 47	99.93 1	99.26 28	97.25 89	99.85 29	100.00 1

GG-mvs-BLEND		69.85 230	99.39 36	35.39 240	3.67 246	99.94 17	99.10 39	1.69 243	99.85 43	3.19 247	98.13 76	99.46 54	4.92 243	99.23 29	99.14 29	99.80 52	100.00 1

SD-MVS		99.16 13	99.73 15	98.49 17	97.93 49	99.95 12	99.74 15	96.94 5	99.96 16	96.60 13	99.47 33	100.00 1	99.88 6	99.15 30	99.59 12	99.84 31	100.00 1

DeepC-MVS	96.33 6	97.05 47	97.59 81	96.42 41	97.37 53	99.92 36	99.10 39	96.54 27	99.34 96	86.64 99	91.93 137	93.15 109	99.11 56	99.11 31	99.68 11	99.73 104	99.97 81
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		99.39 2	99.76 8	98.95 2	99.60 12	99.99 1	99.83 2	96.82 14	99.92 30	97.58 6	99.58 26	100.00 1	99.93 1	98.98 32	99.86 7	99.96 12	100.00 1

UA-Net		94.95 98	98.66 51	90.63 114	94.60 78	98.94 120	96.03 106	85.28 100	98.01 148	78.92 128	97.42 85	99.96 32	89.09 203	98.95 33	98.80 42	99.82 41	98.57 203

EPNet		98.11 35	99.63 23	96.34 43	98.44 47	99.88 50	98.55 52	90.25 53	99.93 25	92.60 40	100.00 1	99.73 44	98.41 78	98.87 34	99.02 33	99.82 41	99.97 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		98.59 28	99.07 39	98.03 24	99.41 25	99.90 44	99.26 35	94.33 37	99.94 22	96.03 18	96.68 91	99.72 46	99.42 33	98.86 35	98.84 39	99.72 108	99.58 179

gm-plane-assit		84.93 209	91.61 160	77.14 219	84.14 208	91.29 225	66.18 235	69.70 208	85.22 231	47.95 231	78.58 206	89.24 124	94.90 134	98.82 36	98.12 62	99.99 6	100.00 1

thresconf0.02		96.46 58	98.87 45	93.64 87	92.77 102	99.11 107	97.05 86	89.36 62	99.64 68	85.14 105	99.07 45	96.84 82	97.72 97	98.72 37	98.76 46	99.78 63	99.95 109

QAPM		97.90 39	98.89 43	96.74 37	99.35 28	99.80 69	98.84 45	90.20 54	99.94 22	92.85 36	94.17 119	99.78 43	99.42 33	98.71 38	99.87 6	99.79 56	99.98 68

MVS_111021_HR		97.94 38	99.59 27	96.02 46	99.27 33	99.97 3	97.03 87	90.44 50	99.89 32	90.75 58	100.00 1	99.73 44	98.68 75	98.67 39	98.89 37	99.95 14	99.97 81

MVSTER		97.00 51	98.85 46	94.83 68	92.71 103	97.43 151	99.03 41	85.52 98	99.82 48	92.74 38	99.15 43	99.94 35	99.19 50	98.66 40	96.99 104	99.79 56	99.98 68

CNLPA		99.24 8	99.58 29	98.85 7	99.34 29	99.95 12	99.32 32	96.65 23	99.96 16	98.44 2	98.97 52	100.00 1	99.57 28	98.66 40	99.56 14	99.76 75	99.97 81

gg-mvs-nofinetune		86.69 197	91.30 163	81.30 211	90.42 140	99.64 80	98.50 55	61.68 233	79.23 235	40.35 236	66.58 229	97.14 81	96.92 109	98.64 42	97.94 66	99.91 23	99.97 81

IS_MVSNet		96.66 55	98.62 52	94.38 77	92.41 114	99.70 76	97.19 82	87.67 92	99.05 109	91.27 53	95.09 108	98.46 70	97.95 92	98.64 42	99.37 18	99.79 56	100.00 1

MVS_0304		97.04 49	98.72 50	95.08 57	96.32 61	99.90 44	99.15 37	89.61 61	99.89 32	87.22 97	95.47 103	98.22 74	98.22 84	98.63 44	98.90 36	99.93 18	100.00 1

Vis-MVSNet (Re-imp)		95.60 87	98.52 56	92.19 106	92.37 115	99.56 84	96.37 101	87.41 94	98.95 112	84.77 109	94.88 114	98.48 69	92.44 159	98.63 44	99.37 18	99.76 75	99.77 163

MVS_Test		95.74 82	98.18 67	92.90 100	92.16 117	99.49 90	97.36 78	84.30 114	99.79 52	84.94 107	96.65 92	93.63 106	98.85 66	98.61 46	99.10 31	99.81 49	100.00 1

3Dnovator+	95.21 7	98.17 32	99.08 38	97.12 32	99.28 32	99.78 71	98.61 51	89.93 57	99.93 25	95.36 23	95.50 102	100.00 1	99.56 29	98.58 47	99.80 9	99.95 14	99.97 81

DELS-MVS		97.05 47	98.05 70	95.88 49	97.09 55	99.99 1	98.82 46	90.30 52	98.44 135	91.40 49	92.91 130	96.57 84	97.68 100	98.56 48	99.88 5	100.00 1	100.00 1
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
CHOSEN 280x420		97.16 46	99.58 29	94.35 80	96.95 57	99.97 3	97.19 82	81.55 146	99.92 30	91.75 44	100.00 1	100.00 1	98.84 67	98.55 49	98.65 48	99.79 56	99.97 81

3Dnovator	95.01 8	97.98 37	98.89 43	96.92 36	99.36 27	99.76 73	98.72 49	89.98 55	99.98 3	93.99 30	94.60 116	99.43 57	99.50 31	98.55 49	99.91 4	99.99 6	99.98 68

LS3D		96.44 60	97.31 88	95.41 54	97.06 56	99.87 51	99.51 28	97.48 1	99.57 72	79.00 127	95.39 104	89.19 125	99.81 11	98.55 49	98.84 39	99.62 129	99.78 162

PLC	98.06 1	99.17 12	99.38 37	98.92 4	99.47 19	99.90 44	99.48 29	96.47 28	99.96 16	98.73 1	99.52 30	100.00 1	99.55 30	98.54 52	97.73 79	99.84 31	99.99 49
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet		97.62 41	98.94 42	96.08 45	97.19 54	99.93 30	99.29 34	90.38 51	99.87 38	91.00 56	95.79 101	99.51 53	98.72 74	98.53 53	99.00 34	99.90 24	99.99 49

PVSNet_BlendedMVS		96.01 67	96.48 113	95.46 51	96.47 59	99.89 48	95.64 112	91.23 46	99.75 59	91.59 45	96.80 87	82.44 151	98.05 87	98.53 53	97.92 71	99.80 52	100.00 1

PVSNet_Blended		96.01 67	96.48 113	95.46 51	96.47 59	99.89 48	95.64 112	91.23 46	99.75 59	91.59 45	96.80 87	82.44 151	98.05 87	98.53 53	97.92 71	99.80 52	100.00 1

TSAR-MVS + GP.		98.06 36	99.55 32	96.32 44	94.72 75	99.92 36	99.22 36	89.98 55	99.97 7	94.77 27	99.94 9	100.00 1	99.43 32	98.52 56	98.53 52	99.79 56	100.00 1

casdiffmvs1		96.22 63	98.26 62	93.85 84	92.52 111	99.45 93	97.35 79	84.50 111	99.87 38	89.96 74	97.60 83	93.89 103	98.79 69	98.49 57	98.51 53	99.95 14	100.00 1

OpenMVS	94.03 11	96.87 52	98.10 69	95.44 53	99.29 31	99.78 71	98.46 57	89.92 58	99.47 83	85.78 102	91.05 140	98.50 66	99.30 40	98.49 57	99.41 17	99.89 25	99.98 68

PHI-MVS		98.85 23	99.67 20	97.89 26	98.63 45	99.93 30	98.95 43	95.20 35	99.84 46	94.94 25	99.74 10	100.00 1	99.69 18	98.40 59	99.75 10	99.93 18	99.99 49

MDTV_nov1_ep13		94.32 105	98.77 49	89.14 129	91.70 128	99.52 85	95.21 122	72.09 206	99.80 51	78.91 129	96.32 95	99.62 49	97.71 99	98.39 60	97.71 80	99.22 209	100.00 1

FMVSNet3		95.59 88	97.51 84	93.34 91	89.48 146	96.57 163	97.67 64	84.17 116	99.48 80	89.76 75	95.09 108	94.35 97	99.14 54	98.37 61	98.86 38	99.82 41	99.89 139

CHOSEN 1792x2688		93.69 112	94.89 138	92.28 105	96.17 62	99.84 52	95.69 111	83.17 127	98.54 129	82.04 120	77.58 210	91.15 115	96.90 110	98.36 62	98.82 41	99.73 104	99.98 68

tfpnview11		95.78 76	98.17 68	93.01 97	92.58 105	99.04 113	96.64 96	88.72 79	99.63 70	83.08 114	98.90 54	94.24 100	97.25 105	98.35 63	97.21 91	99.77 68	99.80 161

tfpn_ndepth		96.84 53	98.58 53	94.81 69	93.18 85	99.62 82	96.83 92	88.75 77	99.73 61	92.38 41	98.45 67	96.34 88	97.90 93	98.34 64	97.59 82	99.84 31	99.99 49

casdiffmvs		95.82 73	97.83 77	93.47 89	92.15 118	99.52 85	96.32 102	84.29 115	99.50 76	89.73 78	97.82 80	91.67 113	98.38 79	98.30 65	98.00 63	99.92 22	100.00 1

EPNet_dtu		95.10 96	98.81 48	90.78 112	98.38 48	98.47 130	96.54 97	89.36 62	99.78 54	65.65 194	99.31 39	98.24 73	94.79 135	98.28 66	99.35 21	99.93 18	98.27 206
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		95.37 89	97.44 86	92.95 99	95.20 68	99.80 69	92.68 147	88.41 87	99.12 104	87.64 91	88.31 151	99.10 61	94.07 145	98.27 67	97.51 84	99.73 104	100.00 1

HyFIR lowres test		93.13 121	94.48 142	91.56 109	96.12 64	99.68 77	93.52 142	79.98 154	97.24 155	81.73 123	72.66 221	95.74 93	98.29 82	98.27 67	97.79 74	99.70 112	100.00 1

MSDG		97.29 45	97.55 82	97.00 34	98.66 44	99.71 75	99.03 41	96.15 31	99.59 71	89.67 80	92.77 133	94.86 95	98.75 71	98.22 69	97.94 66	99.72 108	99.76 164

tfpn1000		96.58 56	98.37 59	94.50 76	93.04 93	99.59 83	96.53 98	88.54 82	99.73 61	91.59 45	98.28 71	95.76 92	97.46 102	98.19 70	97.10 98	99.82 41	99.96 100

CR-MVSNet		92.32 137	97.97 73	85.74 167	90.63 139	98.95 118	95.46 118	65.50 222	99.09 106	67.51 173	94.20 118	98.18 75	95.59 128	98.16 71	97.20 95	99.74 92	100.00 1

PatchT		91.06 146	97.66 79	83.36 203	90.32 141	98.96 117	82.30 220	64.72 227	98.45 134	67.51 173	93.28 129	97.60 79	95.59 128	98.16 71	97.20 95	99.70 112	100.00 1

EPMVS		94.08 110	98.54 55	88.87 131	92.51 112	99.47 91	94.18 136	66.53 215	99.68 65	82.40 118	95.24 105	99.40 58	97.86 94	98.12 73	97.99 64	99.75 88	99.88 144

ACMMP		98.16 33	99.01 40	97.18 31	98.86 40	99.92 36	98.77 48	95.73 34	99.31 97	91.15 55	100.00 1	99.81 42	98.82 68	98.11 74	95.91 128	99.77 68	99.97 81
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
tfpn_n400		95.76 80	98.21 64	92.90 100	92.57 109	99.05 111	96.42 99	88.50 83	99.49 78	83.08 114	98.90 54	94.24 100	97.07 107	98.10 75	97.93 68	99.74 92	99.76 164

tfpnconf		95.76 80	98.21 64	92.90 100	92.57 109	99.05 111	96.42 99	88.50 83	99.49 78	83.08 114	98.90 54	94.24 100	97.07 107	98.10 75	97.93 68	99.74 92	99.76 164

RPMNet		92.64 132	97.88 75	86.53 155	90.79 136	98.95 118	95.13 123	64.44 228	99.09 106	72.36 148	93.58 126	99.01 62	96.74 116	98.05 77	96.45 111	99.71 110	100.00 1

PatchMatch-RL		96.84 53	98.03 71	95.47 50	98.84 41	99.81 65	95.61 115	89.20 65	99.65 66	91.28 52	99.39 34	93.46 107	98.18 85	98.05 77	96.28 115	99.69 119	99.55 184

IterMVS		91.65 141	96.62 105	85.85 164	90.27 142	95.80 185	95.32 121	74.15 190	98.91 115	60.95 211	88.79 150	97.76 77	94.69 138	98.04 79	97.07 100	99.73 104	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAPA-MVS	96.62 5	97.60 42	98.46 57	96.60 40	98.73 43	99.90 44	99.30 33	94.96 36	99.46 84	87.57 92	96.05 100	98.53 65	99.26 47	98.04 79	97.33 88	99.77 68	99.88 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MAR-MVS		97.03 50	98.00 72	95.89 47	99.32 30	99.74 74	96.76 94	84.89 105	99.97 7	94.86 26	98.29 70	90.58 118	99.67 21	98.02 81	99.50 15	99.82 41	99.92 121
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
EPP-MVSNet		96.29 61	98.34 61	93.90 82	91.77 126	99.38 99	95.45 120	87.25 95	99.38 92	91.36 51	94.86 115	98.49 68	97.83 95	98.01 82	98.23 58	99.75 88	99.99 49

Fast-Effi-MVS+		92.11 139	94.33 144	89.52 125	89.06 150	99.00 115	95.13 123	76.72 180	98.59 128	78.21 133	89.99 143	77.35 159	98.34 81	97.97 83	97.44 86	99.67 121	99.96 100

diffmvs		95.11 95	96.95 101	92.96 98	92.09 120	99.44 96	97.26 80	83.80 121	99.44 85	86.43 100	96.77 90	87.25 132	98.49 77	97.92 84	97.93 68	99.70 112	99.90 134

FMVSNet2		94.48 103	95.95 125	92.77 103	89.11 147	96.47 165	96.90 88	83.38 124	99.11 105	88.64 86	87.50 159	92.26 111	98.87 63	97.91 85	98.60 49	99.74 92	99.81 157

PatchmatchNet		93.48 118	98.84 47	87.22 148	91.93 123	99.39 98	92.55 149	66.06 219	99.71 63	75.61 139	98.24 73	99.59 50	97.35 103	97.87 86	97.64 81	99.83 37	99.43 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PMMVS		96.45 59	98.24 63	94.36 79	92.58 105	99.01 114	97.08 85	87.42 93	99.88 35	90.06 72	99.39 34	94.63 96	99.33 39	97.85 87	96.99 104	99.70 112	99.96 100

DI_MVS_plusplus_trai		95.29 90	97.02 96	93.28 92	91.76 127	99.52 85	97.84 61	85.67 97	99.08 108	87.29 95	87.76 154	97.46 80	97.31 104	97.83 88	97.48 85	99.83 37	100.00 1

Effi-MVS+		93.06 124	95.94 126	89.70 123	90.82 135	99.45 93	95.71 110	78.94 165	98.72 118	74.71 144	97.92 78	80.73 155	98.35 80	97.72 89	97.05 102	99.70 112	100.00 1

Fast-Effi-MVS+-dtu		92.73 129	97.62 80	87.02 149	88.91 151	98.83 123	95.79 108	73.98 194	99.89 32	68.62 167	97.73 82	93.30 108	95.21 131	97.67 90	95.96 127	99.59 132	100.00 1

Effi-MVS+-dtu		93.13 121	97.13 93	88.47 139	88.86 153	99.19 105	96.79 93	79.08 163	99.64 68	70.01 162	97.51 84	89.38 122	96.53 119	97.60 91	96.55 108	99.57 136	100.00 1

Vis-MVSNet		93.08 123	96.76 104	88.78 135	91.14 133	99.63 81	94.85 126	83.34 125	97.19 156	74.78 143	91.92 138	93.15 109	88.81 206	97.59 92	98.35 56	99.78 63	99.49 188
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thres100view900		95.86 70	96.62 105	94.97 61	93.10 88	99.83 53	97.76 63	89.15 66	98.62 122	90.69 59	99.00 48	84.86 138	99.30 40	97.57 93	96.48 109	99.81 49	100.00 1

PCF-MVS	97.20 3	97.49 44	98.20 66	96.66 39	97.62 52	99.92 36	98.93 44	96.64 24	98.53 130	88.31 90	94.04 121	99.58 51	98.94 60	97.53 94	97.79 74	99.54 141	99.97 81
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CANet_DTU		94.90 99	98.98 41	90.13 120	94.74 74	99.81 65	98.53 54	82.23 137	99.97 7	66.76 182	100.00 1	98.50 66	98.74 72	97.52 95	97.19 97	99.76 75	99.88 144

conf0.002		96.51 57	97.75 78	95.07 58	93.11 86	99.83 53	97.67 64	89.10 67	98.62 122	91.47 48	99.39 34	91.68 112	99.28 42	97.49 96	97.24 90	99.76 75	100.00 1

conf0.01		96.20 64	97.19 92	95.05 60	93.11 86	99.83 53	97.67 64	89.06 68	98.62 122	91.38 50	99.19 42	89.09 126	99.28 42	97.48 97	96.10 119	99.76 75	100.00 1

thres400		95.72 83	96.48 113	94.84 67	93.00 96	99.83 53	97.55 71	88.93 69	98.49 131	90.61 64	98.86 57	84.63 143	99.20 48	97.45 98	96.10 119	99.77 68	99.99 49

test-LLR		93.71 111	97.23 90	89.60 124	91.69 129	99.10 108	94.68 131	83.60 122	99.36 93	71.94 152	93.82 123	96.51 85	95.96 123	97.42 99	94.37 151	99.74 92	99.99 49

TESTMET0.1,1		92.87 127	97.23 90	87.79 145	86.96 159	99.10 108	94.68 131	77.46 174	99.36 93	71.94 152	93.82 123	96.51 85	95.96 123	97.42 99	94.37 151	99.74 92	99.99 49

tfpn111		95.79 75	96.55 107	94.89 63	93.10 88	99.82 57	97.67 64	88.85 71	98.62 122	90.69 59	99.07 45	84.86 138	99.28 42	97.41 101	96.10 119	99.76 75	99.99 49

conf200view11		95.78 76	96.54 109	94.89 63	93.10 88	99.82 57	97.67 64	88.85 71	98.62 122	90.69 59	99.00 48	84.86 138	99.28 42	97.41 101	96.10 119	99.76 75	99.99 49

tfpn200view9		95.78 76	96.54 109	94.89 63	93.10 88	99.82 57	97.67 64	88.85 71	98.62 122	90.69 59	99.00 48	84.86 138	99.28 42	97.41 101	96.10 119	99.76 75	99.99 49

view600		95.64 84	96.38 116	94.79 70	92.96 97	99.82 57	97.48 75	88.85 71	98.38 136	90.52 66	98.84 59	84.61 144	99.15 52	97.41 101	95.60 136	99.76 75	99.99 49

thres600view7		95.64 84	96.38 116	94.79 70	92.96 97	99.82 57	97.48 75	88.85 71	98.38 136	90.52 66	98.84 59	84.61 144	99.15 52	97.41 101	95.60 136	99.76 75	99.99 49

thres200		95.77 79	96.55 107	94.86 66	93.09 92	99.82 57	97.63 70	88.85 71	98.49 131	90.66 63	98.99 51	84.86 138	99.20 48	97.41 101	96.28 115	99.76 75	100.00 1

view800		95.62 86	96.38 116	94.73 72	92.96 97	99.81 65	97.38 77	88.75 77	98.35 141	90.43 69	98.81 61	84.54 146	99.13 55	97.35 107	95.82 131	99.76 75	99.98 68

test-mter		92.67 131	97.13 93	87.47 147	86.72 161	99.07 110	94.28 135	76.90 178	99.21 99	71.53 156	93.63 125	96.32 89	95.67 125	97.32 108	94.36 153	99.74 92	99.99 49

MVS-HIRNet		88.27 164	94.05 146	81.51 210	88.90 152	98.93 121	83.38 218	60.52 236	98.06 147	63.78 201	80.67 184	90.36 120	92.94 154	97.29 109	96.41 113	99.56 138	96.66 218

TSAR-MVS + COLMAP		95.20 91	95.03 135	95.41 54	96.17 62	98.69 127	99.11 38	93.40 39	99.97 7	84.89 108	98.23 74	75.01 170	99.34 38	97.27 110	96.37 114	99.58 134	99.64 174

tfpn		95.93 69	97.06 95	94.62 73	92.94 101	99.81 65	97.25 81	88.71 80	98.32 142	89.98 73	98.79 62	88.55 128	99.11 56	97.26 111	96.71 107	99.75 88	99.98 68

IterMVS-LS		93.50 115	96.22 121	90.33 119	90.93 134	95.50 195	94.83 127	80.54 150	98.92 114	79.11 126	90.64 141	93.70 105	96.79 113	96.93 112	97.85 73	99.78 63	99.99 49
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HQP-MVS		94.48 103	95.39 133	93.42 90	95.10 69	98.35 133	98.19 58	91.41 44	99.77 55	79.79 124	99.30 40	77.08 160	96.25 120	96.93 112	96.28 115	99.76 75	99.99 49

CDS-MVSNet		94.32 105	97.00 98	91.19 111	89.82 144	98.71 126	95.51 117	85.14 104	96.85 158	82.33 119	92.48 134	96.40 87	94.71 136	96.86 114	97.76 76	99.63 127	99.92 121
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MIMVSNet		91.01 147	96.22 121	84.93 178	85.24 178	98.09 139	90.40 172	64.96 226	97.55 153	72.65 145	96.23 96	90.81 116	96.79 113	96.69 115	97.06 101	99.52 145	97.09 216

GA-MVS		90.38 150	94.59 141	85.46 172	88.30 155	98.44 131	92.18 151	83.30 126	97.89 150	58.05 218	92.86 131	84.25 149	91.27 187	96.65 116	92.61 176	99.66 122	99.43 189

test0.0.03 1		95.15 94	97.87 76	91.99 107	91.69 129	98.82 124	93.04 145	83.60 122	99.65 66	88.80 85	94.15 120	97.67 78	94.97 132	96.62 117	98.16 60	99.83 37	100.00 1

Anonymous20240521		95.85 72	97.53 83	93.89 83	93.20 84	97.01 156	97.14 84	84.77 106	99.16 101	90.38 70	98.96 53	93.73 104	98.23 83	96.57 118	97.37 87	99.64 125	99.93 116

GBi-Net		95.19 92	96.99 99	93.09 95	89.11 147	96.47 165	96.90 88	84.17 116	99.48 80	89.76 75	95.09 108	94.35 97	98.87 63	96.50 119	97.21 91	99.74 92	99.81 157

test1		95.19 92	96.99 99	93.09 95	89.11 147	96.47 165	96.90 88	84.17 116	99.48 80	89.76 75	95.09 108	94.35 97	98.87 63	96.50 119	97.21 91	99.74 92	99.81 157

FMVSNet1		92.55 133	93.66 147	91.26 110	87.91 157	96.12 172	94.75 128	81.69 145	97.67 151	85.63 103	80.56 185	87.88 131	98.15 86	96.50 119	97.21 91	99.41 189	99.71 169

UGNet		96.05 65	98.55 54	93.13 93	94.64 76	99.65 79	94.70 129	87.78 90	99.40 91	89.69 79	98.25 72	99.25 60	92.12 162	96.50 119	97.08 99	99.84 31	99.72 168
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
Anonymous202405211			95.78 128		93.26 83	99.52 85	96.70 95	88.55 81	97.93 149		88.99 147	90.68 117	98.99 59	96.46 123	97.02 103	99.64 125	99.89 139

COLMAP_ROB	93.56 12	96.03 66	96.83 103	95.11 56	97.87 51	99.52 85	98.81 47	91.40 45	99.42 88	84.97 106	90.46 142	96.82 83	98.05 87	96.46 123	96.19 118	99.54 141	98.92 201
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MS-PatchMatch		93.46 119	95.91 127	90.61 115	95.48 66	99.31 102	95.62 114	77.23 175	99.42 88	81.88 122	88.92 148	96.06 90	93.80 147	96.45 125	93.11 169	99.65 123	98.10 210

conf0.05thres1000		94.50 102	95.70 129	93.11 94	92.68 104	99.67 78	96.04 105	87.81 89	97.52 154	83.71 110	96.20 98	84.52 147	98.73 73	96.39 126	95.66 134	99.71 110	99.92 121

ACMM	94.44 10	94.26 108	94.62 140	93.84 85	94.86 73	97.73 146	93.48 143	90.76 49	99.27 98	87.46 93	99.04 47	76.60 163	96.76 115	96.37 127	93.76 160	99.74 92	99.55 184
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CSCG		98.22 31	98.37 59	98.04 23	99.60 12	99.82 57	99.45 30	93.59 38	99.16 101	96.46 15	98.22 75	95.86 91	99.41 35	96.33 128	99.22 27	99.75 88	99.94 113

LGP-MVS_train		93.60 113	95.05 134	91.90 108	94.90 72	98.29 136	97.93 60	88.06 88	99.14 103	74.83 142	99.26 41	76.50 164	96.07 122	96.31 129	95.90 130	99.59 132	99.97 81

TAMVS		92.43 136	94.21 145	90.35 118	88.68 154	98.85 122	94.15 137	81.53 147	95.58 167	83.61 112	87.05 160	86.45 134	94.71 136	96.27 130	95.91 128	99.42 177	99.38 191

canonicalmvs		95.80 74	97.02 96	94.37 78	92.96 97	99.47 91	97.49 72	84.58 108	99.44 85	92.05 42	98.54 66	86.65 133	99.37 37	96.18 131	98.93 35	99.77 68	99.92 121

CVMVSNet		92.13 138	95.40 132	88.32 142	91.29 132	97.29 153	91.85 154	86.42 96	96.71 160	71.84 154	89.56 144	91.18 114	88.98 205	96.17 132	97.76 76	99.51 149	99.14 197

OPM-MVS		93.50 115	93.00 153	94.07 81	95.82 65	98.26 137	98.49 56	91.62 43	94.69 180	81.93 121	92.82 132	76.18 168	96.82 112	96.12 133	94.57 145	99.74 92	98.39 204

FC-MVSNet-test		92.78 128	96.19 123	88.80 134	88.00 156	97.54 148	93.60 141	82.36 136	98.16 143	79.71 125	91.55 139	95.41 94	89.65 198	96.09 134	95.23 140	99.49 152	99.31 192

ADS-MVSNet		92.91 126	97.97 73	87.01 150	92.07 121	99.27 103	92.70 146	65.39 224	99.85 43	75.40 140	94.93 113	98.26 71	96.86 111	96.09 134	97.52 83	99.65 123	99.84 153

ACMP	94.49 9	94.19 109	94.74 139	93.56 88	94.25 79	98.32 135	96.02 107	89.35 64	98.90 116	87.28 96	99.14 44	76.41 166	94.94 133	96.07 136	94.35 154	99.49 152	99.99 49
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FC-MVSNet-train		94.61 100	96.27 120	92.68 104	92.35 116	97.14 154	93.45 144	87.73 91	98.93 113	87.31 94	96.42 94	89.35 123	95.67 125	96.06 137	96.01 126	99.56 138	99.98 68

anonymousdsp		87.98 167	92.38 154	82.85 204	83.68 213	96.79 158	90.78 163	74.06 193	95.29 172	57.91 219	83.33 167	83.12 150	91.15 191	95.96 138	92.37 178	99.52 145	99.76 164

Anonymous20231211		94.96 97	94.99 136	94.91 62	93.01 95	99.44 96	96.85 91	88.49 85	98.78 117	92.61 39	83.94 165	90.25 121	98.94 60	95.87 139	96.77 106	99.58 134	99.89 139

USDC		90.36 151	91.68 159	88.82 133	92.58 105	98.02 140	96.27 103	79.83 155	98.37 139	70.61 161	89.05 146	67.50 217	94.17 143	95.77 140	94.43 149	99.46 162	98.62 202

testgi		92.47 135	95.68 131	88.73 136	90.68 137	98.35 133	91.67 157	79.50 159	98.96 111	77.12 135	95.17 107	85.84 136	93.95 146	95.75 141	96.47 110	99.45 165	99.21 195

TinyColmap		89.94 152	90.88 165	88.84 132	92.43 113	97.91 144	95.59 116	80.10 153	98.12 145	71.33 158	84.56 161	67.46 218	94.15 144	95.57 142	94.27 155	99.43 171	98.26 207

CLD-MVS		94.53 101	94.45 143	94.61 74	93.85 81	98.36 132	98.12 59	89.68 59	99.35 95	89.62 81	95.19 106	77.08 160	96.66 117	95.51 143	95.67 133	99.74 92	100.00 1
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
pmmvs4		91.41 144	93.05 150	89.49 127	85.85 168	96.52 164	91.70 156	82.49 131	98.14 144	83.17 113	87.57 156	81.76 154	94.39 140	95.47 144	92.62 175	99.33 198	99.29 193

pmmvs5		87.33 183	90.01 171	84.20 191	84.31 206	96.04 180	87.63 205	76.59 181	93.17 212	65.35 198	84.30 164	71.68 187	91.91 165	95.41 145	91.37 201	99.39 191	98.13 208

pm-mvs1		89.68 153	92.00 156	86.96 151	86.23 165	96.62 162	90.36 174	83.05 128	93.97 192	72.15 151	81.77 180	82.10 153	90.69 193	95.38 146	94.50 147	99.29 203	99.65 171

ACMH	92.34 14	91.59 142	93.02 152	89.92 122	93.97 80	97.98 142	90.10 185	84.70 107	98.46 133	76.80 136	93.38 128	71.94 185	94.39 140	95.34 147	94.04 156	99.54 141	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpnnormal		89.09 158	89.71 174	88.38 140	87.37 158	96.78 159	91.46 158	85.20 102	90.33 222	72.35 149	83.45 166	69.30 213	94.45 139	95.29 148	92.86 172	99.44 170	99.93 116

LTVRE_ROB	88.65 16	87.87 171	91.11 164	84.10 193	86.64 163	97.47 149	94.40 133	78.41 169	96.13 164	52.02 225	87.95 152	65.92 223	93.59 150	95.29 148	95.09 142	99.52 145	99.95 109
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
tpmrst		92.52 134	97.45 85	86.77 153	92.15 118	99.36 100	92.53 150	65.95 220	99.53 74	72.50 147	92.22 135	99.83 40	97.81 96	95.18 150	96.05 125	99.69 119	100.00 1

CostFormer		93.50 115	96.50 112	90.00 121	91.69 129	98.65 129	93.88 139	67.64 211	98.97 110	89.16 83	97.79 81	88.92 127	97.97 91	95.14 151	96.06 124	99.63 127	100.00 1

Anonymous20231206		84.28 212	89.53 181	78.17 216	82.31 221	94.16 209	82.57 219	76.51 182	93.38 210	52.98 224	79.47 197	73.74 177	75.45 226	95.07 152	94.41 150	99.18 212	96.46 221

TransMVSNet (Re)		88.33 162	89.55 180	86.91 152	86.65 162	95.56 192	90.48 168	84.44 113	92.02 221	71.07 160	80.13 187	72.48 183	89.41 200	95.05 153	94.44 148	99.39 191	97.14 215

FMVSNet5		93.53 114	96.09 124	90.56 116	86.74 160	92.84 213	92.64 148	77.50 173	99.41 90	88.97 84	98.02 77	97.81 76	98.00 90	94.85 154	95.43 138	99.50 151	94.25 225

EG-PatchMatch MVS		86.96 191	89.56 179	83.93 197	86.29 164	97.61 147	90.75 164	73.31 199	95.43 171	66.08 190	75.88 218	71.31 191	87.55 212	94.79 155	92.74 173	99.61 130	99.13 198

testpf		91.26 145	97.28 89	84.23 190	89.52 145	97.45 150	88.08 204	56.08 237	99.76 57	78.71 130	95.06 112	98.26 71	93.44 151	94.72 156	95.69 132	99.57 136	99.99 49

N_pmnet		87.31 184	91.51 161	82.41 209	85.13 180	95.57 191	80.59 223	81.79 143	96.20 163	58.52 217	78.62 205	85.66 137	89.36 201	94.64 157	92.14 180	99.08 214	97.72 214

ACMH+	92.61 13	91.80 140	93.03 151	90.37 117	93.03 94	98.17 138	94.00 138	84.13 119	98.12 145	77.39 134	91.95 136	74.62 171	94.36 142	94.62 158	93.82 159	99.32 199	99.87 148

tpmp4_e23		92.95 125	96.28 119	89.06 130	91.80 125	98.81 125	94.95 125	67.56 213	99.21 99	82.97 117	96.54 93	88.52 129	97.47 101	94.47 159	96.42 112	99.61 130	100.00 1

DWT-MVSNet_training		96.26 62	98.44 58	93.72 86	92.58 105	99.34 101	96.15 104	83.00 129	99.76 57	93.63 33	97.89 79	99.46 54	97.23 106	94.43 160	98.19 59	99.70 112	100.00 1

NR-MVSNet		89.52 155	90.71 166	88.14 144	86.19 166	96.20 169	92.07 152	84.58 108	95.54 168	75.27 141	87.52 157	67.96 216	91.24 189	94.33 161	93.45 164	99.49 152	99.97 81

tpm cat1		93.29 120	96.53 111	89.50 126	91.84 124	99.18 106	94.70 129	67.70 210	98.38 136	86.67 98	89.16 145	99.38 59	96.66 117	94.33 161	95.30 139	99.43 171	100.00 1

v7n		85.39 208	87.70 213	82.70 205	82.77 219	95.64 188	88.27 203	74.83 186	92.30 217	62.58 206	76.37 216	64.80 226	88.38 209	94.29 163	90.61 212	99.34 196	99.87 148

PM-MVS		82.79 217	84.51 220	80.77 213	77.22 225	92.13 214	83.61 217	73.31 199	93.50 205	61.06 210	77.15 212	46.52 236	90.55 195	94.14 164	89.05 221	98.85 217	99.12 199

SixPastTwentyTwo		88.35 161	91.51 161	84.66 182	85.39 174	96.96 157	86.57 208	79.62 158	96.57 161	63.73 202	87.86 153	75.18 169	93.43 152	94.03 165	90.37 213	99.24 208	99.58 179

MDTV_nov1_ep13_2view		87.75 174	93.32 148	81.26 212	83.74 212	96.64 161	85.66 211	66.20 218	98.36 140	61.61 209	84.34 163	87.95 130	91.12 192	94.01 166	92.66 174	99.22 209	99.27 194

ambc			74.33 231		66.84 238	84.26 233	84.17 212		93.39 209	58.99 216	45.93 238	18.06 247	70.61 231	93.94 167	86.62 228	92.61 235	98.13 208

v11		86.74 195	89.01 188	84.09 195	84.79 194	91.79 222	90.39 173	72.53 205	94.47 186	65.75 193	78.64 204	72.96 181	91.66 169	93.92 168	91.69 189	99.42 177	99.61 175

pmmvs-eth3d		82.92 216	83.31 222	82.47 208	76.97 226	91.76 224	83.79 214	76.10 183	90.33 222	69.95 163	71.04 225	48.09 233	89.02 204	93.85 169	89.14 219	99.02 215	98.96 200

v1192		86.93 192	89.01 188	84.50 183	84.46 203	95.51 194	89.93 192	78.65 167	93.75 197	62.29 207	77.19 211	70.88 199	92.28 161	93.84 170	91.96 182	99.38 193	99.90 134

pmmvs3		80.91 219	85.62 217	75.42 221	75.01 228	89.09 230	75.31 229	68.70 209	86.99 229	46.74 233	81.18 183	62.91 228	87.95 210	93.84 170	89.06 220	98.80 218	96.23 223

v1240		86.24 204	88.56 201	83.54 198	84.05 210	95.21 202	89.27 198	76.76 179	93.42 207	60.68 214	75.99 217	69.80 210	91.21 190	93.83 172	91.76 187	99.29 203	99.91 133

v1144		87.49 177	89.64 175	84.97 177	84.73 196	95.84 184	90.17 184	79.30 160	93.96 193	64.65 199	78.83 203	73.38 180	91.51 177	93.77 173	91.77 186	99.45 165	99.93 116

RPSCF		95.86 70	96.94 102	94.61 74	96.52 58	98.67 128	98.54 53	88.43 86	99.56 73	90.51 68	99.39 34	98.70 63	97.72 97	93.77 173	92.00 181	95.93 227	96.50 220

dps		94.29 107	97.33 87	90.75 113	92.02 122	99.21 104	94.31 134	66.97 214	99.50 76	95.61 21	96.22 97	98.64 64	96.08 121	93.71 175	94.03 157	99.52 145	99.98 68

v1921920		86.81 193	88.93 193	84.33 188	84.23 207	95.41 198	90.09 186	78.10 170	93.74 199	62.17 208	76.98 213	71.14 196	92.05 163	93.69 176	91.69 189	99.32 199	99.88 144

v7		87.72 175	89.75 173	85.35 174	85.01 184	95.79 186	90.43 171	78.98 164	94.50 185	66.39 185	78.87 201	73.65 178	91.85 167	93.69 176	91.86 185	99.45 165	99.92 121

v10		87.40 182	89.62 176	84.80 180	84.93 187	95.07 203	90.44 169	75.63 184	94.51 182	66.52 183	78.87 201	73.47 179	91.86 166	93.69 176	91.87 184	99.45 165	99.86 151

WR-MVS_H		88.47 160	90.55 168	86.04 158	85.13 180	96.07 178	89.86 194	79.80 156	94.37 189	72.32 150	83.12 168	74.44 174	89.60 199	93.52 179	92.40 177	99.51 149	99.96 100

MIMVSNet1		80.64 220	83.97 221	76.76 220	68.91 236	91.15 227	78.32 227	75.47 185	89.58 227	56.64 222	65.10 230	65.17 225	82.14 219	93.51 180	91.64 191	99.10 213	91.66 227

pmmvs6		85.75 207	86.97 215	84.34 187	84.88 189	95.59 190	87.41 206	79.19 162	87.81 228	67.56 172	63.05 232	77.76 158	89.15 202	93.45 181	91.90 183	97.83 223	99.21 195

UniMVSNet (Re)		90.41 149	91.96 157	88.59 138	85.71 169	96.73 160	90.82 162	84.11 120	95.23 173	78.54 131	88.91 149	76.41 166	92.84 156	93.40 182	93.05 170	99.55 140	100.00 1

CP-MVSNet		88.09 166	89.57 178	86.36 156	84.63 200	95.46 197	89.48 196	80.53 151	93.42 207	71.26 159	81.25 182	69.90 208	92.78 157	93.30 183	93.69 161	99.47 159	99.96 100

UniMVSNet_NR-MVSNet		90.50 148	92.31 155	88.38 140	85.04 183	96.34 168	90.94 159	85.32 99	95.87 166	75.69 137	87.68 155	78.49 156	93.78 148	93.21 184	94.60 144	99.53 144	99.97 81

TDRefinement		87.79 173	88.76 199	86.66 154	93.54 82	98.02 140	95.76 109	85.18 103	96.57 161	67.90 168	80.51 186	66.51 222	78.37 224	93.20 185	89.73 217	99.22 209	96.75 217

test2356		83.84 215	91.77 158	74.59 223	78.71 224	89.10 229	78.24 228	72.07 207	96.78 159	45.18 234	96.19 99	76.77 162	74.87 228	93.17 186	94.01 158	98.44 220	96.38 222

PS-CasMVS		87.24 185	88.52 202	85.73 168	84.58 201	95.35 199	89.03 199	80.17 152	93.11 213	68.86 166	77.71 209	66.89 219	92.30 160	93.13 187	93.50 163	99.46 162	99.96 100

new_pmnet		84.12 213	87.89 209	79.72 214	80.43 222	94.14 210	80.26 224	74.14 191	96.01 165	56.30 223	74.94 220	76.45 165	88.59 208	93.11 188	89.31 218	98.59 219	91.27 228

testus		82.22 218	88.82 198	74.52 224	79.14 223	89.37 228	78.38 226	72.99 202	97.57 152	44.54 235	93.44 127	58.13 231	74.20 229	92.96 189	93.67 162	97.89 222	96.58 219

v144192		86.80 194	88.90 195	84.35 185	84.33 205	95.56 192	89.34 197	77.74 172	93.60 203	64.03 200	77.82 208	70.76 200	91.28 186	92.91 190	91.74 188	99.37 195	99.90 134

IB-MVS	90.59 15	92.70 130	95.70 129	89.21 128	94.62 77	99.45 93	83.77 215	88.92 70	99.53 74	92.82 37	98.86 57	86.08 135	75.24 227	92.81 191	93.17 167	99.89 25	100.00 1
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
LP		88.31 163	93.18 149	82.63 206	90.66 138	97.98 142	87.32 207	63.49 231	97.17 157	63.02 205	82.08 172	90.47 119	91.92 164	92.75 192	93.42 165	99.38 193	98.37 205

WR-MVS		88.23 165	90.15 170	86.00 160	84.39 204	95.64 188	89.96 190	81.80 142	94.46 187	71.60 155	82.10 171	74.36 175	88.76 207	92.48 193	92.20 179	99.46 162	99.83 155

test20.03		83.86 214	88.73 200	78.16 217	82.60 220	93.00 212	81.61 222	74.68 187	92.36 216	57.50 220	83.01 170	74.48 173	73.30 230	92.40 194	91.14 210	99.29 203	94.75 224

EU-MVSNet		87.20 186	90.47 169	83.38 202	85.11 182	93.85 211	86.10 210	79.76 157	93.30 211	65.39 197	84.41 162	78.43 157	85.04 217	92.20 195	93.03 171	98.86 216	98.05 211

tpm		89.60 154	94.93 137	83.39 201	89.94 143	97.11 155	90.09 186	65.28 225	98.67 120	60.03 215	96.79 89	84.38 148	95.66 127	91.90 196	95.65 135	99.32 199	99.98 68

v2v482		87.46 179	88.90 195	85.78 166	84.58 201	95.95 183	89.90 193	82.43 135	94.19 191	65.65 194	79.80 191	69.12 214	92.67 158	91.88 197	91.46 192	99.45 165	99.93 116

v1141		87.45 181	88.98 190	85.67 170	84.86 191	96.08 176	90.23 182	82.46 132	93.75 197	65.64 196	79.57 195	70.52 202	91.41 182	91.63 198	91.39 198	99.42 177	99.92 121

divwei89l23v2f112		87.46 179	88.97 191	85.70 169	84.85 192	96.08 176	90.23 182	82.46 132	93.69 201	65.83 192	79.57 195	70.54 201	91.39 183	91.60 199	91.39 198	99.43 171	99.92 121

v1		87.48 178	88.91 194	85.81 165	84.93 187	96.07 178	90.33 176	82.45 134	93.65 202	66.39 185	79.38 198	70.40 204	91.33 184	91.58 200	91.38 200	99.42 177	99.93 116

v6		87.96 168	89.58 177	86.08 157	85.34 175	96.14 171	90.44 169	82.19 138	94.56 181	67.43 177	81.90 175	71.57 190	91.62 174	91.54 201	91.43 194	99.43 171	99.92 121

v18		87.14 189	88.96 192	85.01 176	85.57 170	92.03 215	90.89 161	74.62 188	94.80 179	67.90 168	82.02 173	71.28 192	91.63 173	91.53 202	91.44 193	99.47 159	99.60 176

v1neww		87.88 169	89.51 183	85.97 162	85.32 176	96.12 172	90.33 176	82.17 139	94.51 182	66.96 179	81.84 177	71.21 193	91.64 171	91.52 203	91.43 194	99.42 177	99.92 121

v7new		87.88 169	89.51 183	85.97 162	85.32 176	96.12 172	90.33 176	82.17 139	94.51 182	66.96 179	81.84 177	71.21 193	91.64 171	91.52 203	91.43 194	99.42 177	99.92 121

v16		87.15 188	89.13 187	84.83 179	85.55 171	91.94 217	90.50 166	74.13 192	95.06 175	67.72 170	81.84 177	72.55 182	91.65 170	91.50 205	91.42 197	99.42 177	99.60 176

v17		86.99 190	88.90 195	84.76 181	85.52 172	91.96 216	90.50 166	74.17 189	94.88 177	67.33 178	81.94 174	71.21 193	91.57 176	91.49 206	91.20 206	99.48 156	99.60 176

v8		87.54 176	89.33 186	85.45 173	85.41 173	95.50 195	90.32 179	78.94 165	94.35 190	66.93 181	81.90 175	70.99 198	91.62 174	91.49 206	91.22 205	99.48 156	99.87 148

v15		86.50 200	88.32 204	84.37 184	85.00 185	91.86 218	90.30 180	73.76 195	93.90 195	66.28 188	79.78 192	70.37 205	91.45 180	91.48 208	91.27 202	99.43 171	99.58 179

V14		86.54 199	88.41 203	84.35 185	84.94 186	91.83 219	90.28 181	73.48 197	93.73 200	66.50 184	79.89 190	71.12 197	91.46 179	91.48 208	91.25 203	99.42 177	99.58 179

V9		86.42 201	88.26 205	84.27 189	84.88 189	91.80 220	90.34 175	73.18 201	93.92 194	66.37 187	79.68 194	70.25 206	91.42 181	91.43 210	91.23 204	99.42 177	99.55 184

v12		86.32 202	88.22 206	84.10 193	84.76 195	91.80 220	89.94 191	72.97 203	93.85 196	66.18 189	79.98 189	69.72 212	91.33 184	91.40 211	91.20 206	99.42 177	99.56 183

v13		86.27 203	88.16 208	84.06 196	84.85 192	91.77 223	90.00 188	72.77 204	93.56 204	66.06 191	79.25 199	70.50 203	91.25 188	91.35 212	91.15 209	99.42 177	99.55 184

V42		87.84 172	89.42 185	85.99 161	85.16 179	96.01 181	90.52 165	81.78 144	94.43 188	67.59 171	81.32 181	71.87 186	91.48 178	91.25 213	91.16 208	99.43 171	99.92 121

DU-MVS		89.49 156	90.60 167	88.19 143	84.71 197	96.20 169	90.94 159	84.58 108	95.54 168	75.69 137	87.52 157	68.74 215	93.78 148	91.10 214	95.13 141	99.47 159	99.97 81

Baseline_NR-MVSNet		89.13 157	89.53 181	88.66 137	84.71 197	94.43 206	91.79 155	84.49 112	95.54 168	78.28 132	78.52 207	72.46 184	93.29 153	91.10 214	94.82 143	99.42 177	99.86 151

PEN-MVS		87.20 186	88.22 206	86.01 159	84.01 211	94.93 205	90.00 188	81.52 149	93.46 206	69.29 164	79.69 193	65.51 224	91.72 168	91.01 216	93.12 168	99.49 152	99.84 153

TranMVSNet+NR-MVSNet		88.88 159	89.90 172	87.69 146	84.06 209	95.68 187	91.88 153	85.23 101	95.16 174	72.54 146	83.06 169	70.14 207	92.93 155	90.81 217	94.53 146	99.48 156	99.89 139

V4		85.78 206	87.74 211	83.50 200	82.90 216	95.33 200	88.62 201	77.05 176	92.14 220	63.45 204	76.91 214	69.85 209	89.72 197	90.07 218	90.05 216	99.27 206	99.81 157

v52		85.80 205	87.74 211	83.53 199	82.87 217	95.31 201	88.71 200	77.04 177	92.23 218	63.53 203	76.91 214	69.80 210	89.78 196	90.05 219	90.07 215	99.26 207	99.82 156

v748		84.47 211	86.06 216	82.62 207	82.85 218	95.02 204	83.73 216	78.48 168	90.20 224	67.45 176	75.86 219	61.27 229	83.84 218	89.87 220	90.28 214	99.34 196	99.90 134

DTE-MVSNet		86.70 196	87.66 214	85.58 171	83.30 214	94.29 207	89.74 195	81.53 147	92.77 215	68.93 165	80.13 187	64.00 227	90.62 194	89.45 221	93.34 166	99.32 199	99.67 170

v148		86.63 198	87.79 210	85.28 175	84.65 199	95.97 182	86.46 209	82.84 130	92.91 214	71.52 157	78.99 200	66.74 221	86.83 214	89.28 222	90.69 211	99.41 189	99.94 113

new-patchmatchnet		78.17 222	80.82 224	75.07 222	76.93 227	91.20 226	71.90 231	73.32 198	86.59 230	48.91 228	67.11 228	47.85 235	81.19 220	88.18 223	87.02 226	98.19 221	97.79 213

1111		73.79 224	78.62 226	68.16 227	69.34 233	81.48 234	59.42 239	52.46 239	78.55 236	50.42 226	62.43 233	71.67 188	80.43 222	86.79 224	88.22 222	96.87 224	81.17 238

.test1245		70.78 228	79.90 225	60.13 233	69.34 233	81.48 234	59.42 239	52.46 239	78.55 236	50.42 226	62.43 233	71.67 188	80.43 222	86.79 224	78.71 232	48.74 243	99.65 171

CMPMVS	65.66 17	84.62 210	85.02 219	84.15 192	95.40 67	97.79 145	88.35 202	79.22 161	89.66 226	60.71 213	72.20 222	73.94 176	87.32 213	86.73 226	84.55 230	93.90 232	90.31 229
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Gipuma		71.02 227	72.60 233	69.19 226	71.31 231	75.11 240	66.36 234	61.65 234	94.93 176	47.29 232	38.74 239	38.52 240	75.52 225	86.09 227	85.92 229	93.01 233	88.87 233
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		71.50 225	77.62 227	64.36 228	72.64 229	81.28 236	59.32 241	66.24 216	83.91 232	35.02 240	69.74 226	46.18 237	57.12 235	85.60 228	87.48 224	95.84 228	89.16 231

test1235678		71.50 225	77.61 228	64.36 228	72.64 229	81.26 237	59.31 242	66.22 217	83.90 233	35.02 240	69.74 226	46.18 237	57.12 235	85.60 228	87.47 225	95.84 228	89.15 232

test12356		69.94 229	75.85 229	63.04 230	70.04 232	79.32 239	61.62 237	65.84 221	80.56 234	36.30 239	71.45 224	39.38 239	48.79 241	83.64 230	88.02 223	95.64 230	88.56 234

DeepMVS_CX						97.31 152	79.48 225	89.65 60	98.66 121	60.89 212	94.40 117	66.89 219	87.65 211	81.69 231		92.76 234	94.24 226

tmp_tt				78.81 215	98.80 42	85.73 231	70.08 232	77.87 171	98.68 119	83.71 110	99.53 28	74.55 172	54.97 237	78.28 232	72.43 235	87.45 236

testmvs		61.76 233	72.90 232	48.76 237	21.21 244	68.61 242	66.11 236	37.38 241	94.83 178	33.06 242	64.31 231	29.72 242	86.08 216	74.44 233	78.71 232	48.74 243	99.65 171

PMMVS2		65.18 231	68.25 234	61.59 231	61.37 239	79.72 238	59.18 243	61.80 232	64.72 240	37.33 237	53.82 236	35.59 241	54.46 239	73.94 234	80.52 231	95.40 231	89.43 230

FPMVS		73.80 223	74.62 230	72.84 225	83.09 215	84.44 232	83.89 213	73.64 196	92.20 219	48.50 229	72.19 223	59.51 230	63.16 232	69.13 235	66.26 240	84.74 237	78.59 239

MDA-MVSNet-bldmvs		80.30 221	82.83 223	77.34 218	69.16 235	94.29 207	72.16 230	81.97 141	90.14 225	57.32 221	94.01 122	47.97 234	86.81 215	68.74 236	86.82 227	96.63 225	97.86 212

PMVS	60.14 18	62.67 232	64.05 235	61.06 232	68.32 237	53.27 247	52.23 244	67.63 212	75.07 239	48.30 230	58.27 235	57.43 232	49.99 240	67.20 237	62.42 241	79.87 241	74.68 241
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	58.81 19	52.07 237	55.15 239	48.48 238	42.45 243	62.35 243	36.41 247	54.70 238	49.88 243	27.65 243	29.98 243	18.08 246	54.87 238	65.93 238	77.26 234	74.79 242	82.59 235
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		55.33 234	55.79 237	54.81 235	59.81 241	57.23 245	38.83 245	63.59 229	64.06 242	24.66 244	35.33 241	26.40 244	58.69 234	55.41 239	70.54 237	83.26 238	81.56 237

EMVS		55.14 235	55.29 238	54.97 234	60.87 240	57.52 244	38.58 246	63.57 230	64.54 241	23.36 245	36.96 240	27.99 243	60.69 233	51.17 240	66.61 239	82.73 240	82.25 236

no-one		52.34 236	53.36 240	51.14 236	57.63 242	69.39 241	35.07 248	61.58 235	44.14 244	37.06 238	34.80 242	26.36 245	32.65 242	50.68 241	70.83 236	82.88 239	77.30 240

test123		48.14 238	58.11 236	36.51 239	8.71 245	56.81 246	59.55 238	24.08 242	77.50 238	14.41 246	49.20 237	11.94 248	80.98 221	41.62 242	69.81 238	31.32 245	99.90 134

sosnet-low-res		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

sosnet		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

our_test_3					85.89 167	96.09 175	82.15 221

MTAPA										96.61 12		100.00 1

MTMP										97.42 7		100.00 1

Patchmatch-RL test							68.01 233

XVS					95.09 70	99.94 17	97.49 72			88.58 87		99.98 29				99.78 63

X-MVStestdata					95.09 70	99.94 17	97.49 72			88.58 87		99.98 29				99.78 63

abl_6				97.06 33	99.17 36	99.82 57	98.68 50	90.86 48	100.00 1	94.53 29	97.40 86	100.00 1	99.17 51			99.93 18	99.99 49

mPP-MVS					99.23 34							99.87 38

NP-MVS									99.79 52

Patchmtry						99.00 115	95.46 118	65.50 222		67.51 173

High-resolution multi-view benchmark