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.

MCST-MVS		98.20 9	99.18 2	97.06 18	99.27 11	99.87 1	99.37 7	96.11 7	99.37 5	89.29 29	98.76 8	99.50 6	98.37 21	99.23 5	97.64 25	99.95 1	99.87 29

APDe-MVS		98.60 4	98.97 7	98.18 2	99.38 4	99.78 10	99.35 9	96.14 5	99.24 12	95.66 3	98.19 17	99.01 12	98.66 13	98.77 11	97.80 22	99.86 2	99.97 5

HSP-MVS		98.70 2	99.28 1	98.03 3	99.21 12	99.82 4	99.17 17	96.09 9	99.54 2	94.79 6	98.79 6	99.55 5	99.05 4	99.54 1	98.19 13	99.84 3	99.52 66

DeepC-MVS_fast	95.01 1	97.67 18	98.22 25	97.02 19	99.00 19	99.79 6	99.10 20	95.82 13	99.05 17	89.53 28	93.54 44	96.77 36	98.83 8	99.34 4	99.44 1	99.82 4	99.63 55
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	90.31 8	95.67 40	96.16 41	95.11 34	98.59 25	99.37 48	97.50 40	87.98 53	98.02 44	89.09 30	85.36 90	94.62 46	97.66 25	97.10 46	98.90 5	99.82 4	99.73 41

TSAR-MVS + MP.		97.98 13	98.62 15	97.23 16	97.08 46	99.55 30	99.17 17	95.69 16	99.40 4	93.04 15	96.68 28	98.96 13	98.58 16	98.82 9	96.95 44	99.81 6	99.96 6

CNVR-MVS		98.73 1	99.17 4	98.22 1	99.47 1	99.85 2	99.57 2	96.23 1	99.30 9	94.90 5	98.65 10	98.93 14	99.36 1	99.46 3	98.21 10	99.81 6	99.80 36

DELS-MVS		93.82 50	93.82 63	93.81 45	96.34 50	99.47 35	97.26 45	88.53 49	92.13 117	87.80 38	79.67 113	88.01 77	93.14 81	98.28 16	99.22 3	99.80 8	99.98 3
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
ESAPD		98.61 3	99.15 5	97.97 5	99.36 5	99.80 5	99.56 3	96.18 2	99.26 10	93.88 12	98.64 11	99.98 1	99.04 5	98.89 8	97.49 29	99.79 9	99.98 3

MSLP-MVS++		98.12 11	98.23 24	97.99 4	99.28 10	99.72 13	99.59 1	95.27 23	98.61 26	94.79 6	96.11 30	97.79 30	99.27 2	96.62 53	98.96 4	99.77 10	99.80 36

gm-plane-assit		77.20 200	82.26 153	71.30 210	81.10 179	82.00 220	54.33 227	64.41 205	63.80 224	40.93 225	59.04 185	76.57 118	87.30 132	98.26 18	97.36 33	99.74 11	98.76 107

CANet		95.40 41	96.27 40	94.40 37	96.25 51	99.62 24	98.37 28	88.59 48	98.09 40	87.58 41	84.57 96	95.54 43	95.87 58	98.12 21	98.03 20	99.73 12	99.90 21

abl_6				95.40 31	98.18 34	99.45 38	97.39 43	89.27 44	99.48 3	90.52 24	94.52 41	98.63 17	97.32 30			99.73 12	99.82 34

APD-MVS		98.28 7	98.69 12	97.80 6	99.31 9	99.62 24	99.31 13	96.15 4	99.19 14	93.60 13	97.28 25	98.35 21	98.72 12	98.27 17	98.22 9	99.73 12	99.89 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		98.41 5	99.18 2	97.52 11	99.36 5	99.84 3	99.55 4	96.08 11	99.33 8	91.77 20	98.79 6	99.46 7	98.59 15	99.15 6	98.07 18	99.73 12	99.64 51

3Dnovator+	90.72 7	95.99 34	96.42 39	95.50 30	98.18 34	99.33 50	97.44 41	87.73 56	97.93 47	92.36 17	84.67 94	97.33 32	97.55 28	97.32 40	98.47 8	99.72 16	99.88 24

MVS_0304		94.35 47	95.66 45	92.83 53	94.82 59	99.46 37	98.19 30	87.75 55	97.32 57	81.83 79	83.50 103	93.19 56	94.71 69	98.24 19	98.07 18	99.68 17	99.83 32

HPM-MVS++		98.16 10	98.87 11	97.32 14	99.39 3	99.70 16	99.18 16	96.10 8	99.09 16	91.14 23	98.02 20	99.89 3	98.44 19	98.75 12	97.03 42	99.67 18	99.63 55

SteuartSystems-ACMMP		97.86 15	98.91 9	96.64 22	98.89 22	99.79 6	99.34 10	95.20 25	98.48 28	89.91 27	98.58 13	98.69 16	96.84 40	98.92 7	98.16 15	99.66 19	99.74 39
Skip Steuart: Steuart Systems R&D Blog.
gg-mvs-nofinetune		81.27 156	84.65 147	77.32 195	87.96 132	98.48 77	95.64 57	56.36 227	59.35 225	32.80 232	47.96 220	92.11 63	91.49 99	98.12 21	97.00 43	99.65 20	99.56 63

MP-MVS		97.46 21	98.30 20	96.48 24	98.93 21	99.43 40	99.20 15	95.42 20	98.43 30	87.60 40	98.19 17	98.01 29	98.09 23	98.05 23	96.67 50	99.64 21	99.35 77

MVS_111021_HR		95.70 39	98.16 26	92.83 53	97.57 42	99.77 11	94.78 69	88.05 51	98.61 26	82.29 70	98.85 5	94.66 44	94.63 70	97.80 32	97.63 26	99.64 21	99.79 38

SD-MVS		98.33 6	99.01 6	97.54 10	97.17 45	99.77 11	99.14 19	96.09 9	99.34 7	94.06 11	97.91 22	99.89 3	99.18 3	97.99 24	98.21 10	99.63 23	99.95 9

TSAR-MVS + GP.		96.47 29	98.45 18	94.17 40	92.12 77	99.29 52	97.76 35	88.05 51	99.36 6	90.26 26	97.82 24	99.21 9	97.21 32	96.78 52	96.74 48	99.63 23	99.94 12

train_agg		97.42 22	98.88 10	95.71 28	98.46 29	99.60 27	99.05 22	95.16 26	99.10 15	84.38 55	98.47 14	98.85 15	97.61 27	98.54 15	97.66 24	99.62 25	99.93 15

AdaColmap		97.54 19	97.35 34	97.77 8	99.17 14	99.55 30	98.57 26	95.76 14	99.04 18	94.66 8	97.94 21	94.39 49	98.82 9	96.21 59	94.78 72	99.62 25	99.52 66

MVSTER		94.75 43	96.50 38	92.70 56	90.91 104	94.51 136	97.37 44	83.37 91	98.40 33	89.04 31	93.23 47	97.04 35	95.91 56	97.73 33	95.59 66	99.61 27	99.01 101

HFP-MVS		98.02 12	98.55 16	97.40 13	99.11 16	99.69 17	99.41 5	95.41 21	98.79 24	91.86 19	98.61 12	98.16 23	99.02 6	97.87 28	97.40 31	99.60 28	99.35 77

ACMMPR		97.78 17	98.28 21	97.20 17	99.03 18	99.68 18	99.37 7	95.24 24	98.86 23	91.16 22	97.86 23	97.26 33	98.79 10	97.64 37	97.40 31	99.60 28	99.25 83

XVS					93.63 64	99.64 20	94.32 76			83.97 59		98.08 26				99.59 30

X-MVStestdata					93.63 64	99.64 20	94.32 76			83.97 59		98.08 26				99.59 30

MVS_Test		92.42 62	94.43 54	90.08 83	90.69 109	98.26 78	94.78 69	80.81 112	97.27 58	78.76 98	87.06 75	84.25 92	95.84 59	97.67 34	97.56 28	99.59 30	98.93 102

CDPH-MVS		95.90 36	97.77 32	93.72 46	98.28 33	99.43 40	98.40 27	91.30 38	98.34 35	78.62 99	94.80 36	95.74 40	96.11 52	97.86 29	98.67 6	99.59 30	99.56 63

thresconf0.02		92.16 66	95.16 49	88.67 96	91.10 98	97.63 92	92.93 89	86.58 62	96.29 70	73.55 108	94.67 39	88.63 73	88.29 125	96.14 62	95.40 67	99.58 34	97.33 152

tfpn_ndepth		92.26 65	93.84 62	90.42 77	91.45 91	97.91 86	92.73 92	85.80 75	96.69 64	82.22 71	91.92 55	83.42 95	90.76 106	95.51 87	93.28 101	99.58 34	98.14 132

X-MVS		97.20 24	98.42 19	95.77 26	99.04 17	99.64 20	98.95 25	95.10 28	98.16 37	83.97 59	98.27 16	98.08 26	97.95 24	97.89 25	97.46 30	99.58 34	99.47 72

TSAR-MVS + ACMM		96.90 28	98.64 14	94.88 35	98.12 36	99.47 35	99.01 24	95.43 19	99.23 13	81.98 77	95.95 31	99.16 11	95.13 63	98.61 13	98.11 17	99.58 34	99.93 15

PGM-MVS		97.03 26	98.14 27	95.73 27	99.34 7	99.61 26	99.34 10	89.99 40	97.70 49	87.67 39	99.44 2	96.45 39	98.44 19	97.65 36	97.09 39	99.58 34	99.06 100

canonicalmvs		92.54 60	93.28 73	91.68 62	91.44 92	98.24 79	95.45 61	81.84 103	95.98 77	84.85 54	90.69 59	78.53 114	96.96 34	92.97 124	97.06 40	99.57 39	99.47 72

EPNet		96.23 30	97.89 30	94.29 38	97.62 41	99.44 39	97.14 47	88.63 47	98.16 37	88.14 35	99.46 1	94.15 50	94.61 71	97.20 43	97.23 35	99.57 39	99.59 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		97.81 16	98.26 23	97.28 15	99.00 19	99.65 19	99.10 20	95.32 22	98.38 34	92.21 18	98.33 15	97.74 31	98.50 18	97.66 35	96.55 52	99.57 39	99.48 71

QAPM		95.17 42	96.05 42	94.14 41	98.55 26	99.49 33	97.41 42	87.88 54	97.72 48	84.21 57	84.59 95	95.60 42	97.21 32	97.10 46	98.19 13	99.57 39	99.65 49

SMA-MVS		98.26 8	98.97 7	97.44 12	99.42 2	99.79 6	99.33 12	96.12 6	99.25 11	91.26 21	96.72 27	99.96 2	98.95 7	98.81 10	98.52 7	99.56 43	99.72 43

tfpn1000		91.48 74	93.17 75	89.51 89	91.27 94	97.71 89	92.08 101	85.28 82	96.13 74	80.20 89	90.77 58	82.52 98	88.64 121	95.17 96	92.35 122	99.56 43	97.52 150

ACMMP_Plus		97.51 20	98.27 22	96.63 23	99.34 7	99.72 13	99.25 14	95.94 12	98.11 39	87.10 43	96.98 26	98.50 19	98.61 14	98.58 14	96.83 47	99.56 43	99.14 91

PVSNet_BlendedMVS		93.30 52	93.46 71	93.10 49	95.60 55	99.38 46	93.59 85	88.70 45	98.09 40	88.10 36	86.96 77	75.02 122	93.08 82	97.89 25	96.90 45	99.56 43	100.00 1

PVSNet_Blended		93.30 52	93.46 71	93.10 49	95.60 55	99.38 46	93.59 85	88.70 45	98.09 40	88.10 36	86.96 77	75.02 122	93.08 82	97.89 25	96.90 45	99.56 43	100.00 1

PHI-MVS		97.09 25	98.69 12	95.22 33	97.99 38	99.59 29	97.56 39	92.16 34	98.41 32	87.11 42	98.70 9	99.42 8	96.95 36	96.88 50	98.16 15	99.56 43	99.70 45

FMVSNet3		91.25 80	92.13 89	90.21 81	85.64 143	93.14 145	95.29 62	80.09 113	96.40 67	85.74 47	77.13 117	86.81 82	94.98 66	97.19 44	97.11 37	99.55 49	97.13 157

IB-MVS	84.67 14	88.34 113	90.61 111	85.70 114	92.99 72	98.62 71	78.85 202	86.07 73	94.35 93	88.64 34	85.99 87	75.69 120	68.09 212	88.21 160	91.43 132	99.55 49	99.96 6
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
ACMMP		96.05 33	96.70 36	95.29 32	98.01 37	99.43 40	97.60 38	94.33 32	97.62 52	86.17 46	98.92 4	92.81 58	96.10 53	95.67 77	93.33 100	99.55 49	99.12 94
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
zzz-MVS		97.93 14	98.05 28	97.80 6	99.20 13	99.64 20	99.40 6	95.76 14	98.01 45	94.31 10	96.54 29	98.49 20	98.58 16	98.22 20	96.23 53	99.54 52	99.23 84

OpenMVS	88.43 11	93.49 51	93.62 66	93.34 47	98.46 29	99.39 45	97.00 48	87.66 58	95.37 81	81.21 81	75.96 128	91.58 66	96.21 51	96.37 56	97.10 38	99.52 53	99.54 65

tfpnview11		90.36 92	92.74 81	87.59 99	90.93 103	97.30 100	92.28 99	85.63 78	95.88 78	70.44 118	92.30 52	79.50 110	86.76 135	95.26 95	92.83 111	99.51 54	96.09 177

thres100view900		91.69 72	91.52 93	91.88 61	91.61 81	98.89 57	95.49 59	86.96 60	93.24 104	80.82 83	87.90 67	71.15 134	96.88 39	96.00 67	93.51 97	99.51 54	99.95 9

test-LLR		89.31 104	93.60 67	84.30 124	88.08 130	96.98 102	88.10 135	78.00 132	94.83 85	62.43 144	84.29 99	90.96 69	89.70 113	95.63 79	92.86 107	99.51 54	99.64 51

TESTMET0.1,1		88.63 110	93.60 67	82.84 139	84.07 152	96.98 102	88.10 135	73.22 169	94.83 85	62.43 144	84.29 99	90.96 69	89.70 113	95.63 79	92.86 107	99.51 54	99.64 51

IS_MVSNet		92.67 58	94.99 52	89.96 84	91.17 97	98.54 74	92.77 91	84.00 87	92.72 113	81.90 78	85.67 88	92.47 60	90.39 108	97.82 30	97.81 21	99.51 54	99.91 19

conf0.01		92.41 63	92.86 79	91.90 60	91.65 80	98.84 59	95.03 63	86.38 65	93.24 104	82.03 75	91.90 56	77.54 116	96.80 41	95.78 73	92.82 112	99.48 59	99.90 21

conf0.002		92.80 55	93.55 70	91.93 59	91.66 79	98.85 58	95.03 63	86.42 63	93.24 104	82.20 73	92.98 49	79.35 113	96.80 41	95.83 70	94.67 76	99.48 59	99.91 19

CANet_DTU		91.36 77	95.75 44	86.23 110	92.31 76	98.71 63	95.60 58	78.41 129	98.20 36	56.48 188	94.38 42	87.96 78	95.11 64	96.89 49	96.07 58	99.48 59	98.01 140

thres400		91.24 81	91.01 106	91.50 68	91.56 86	98.77 60	94.66 73	86.41 64	91.87 121	80.56 88	87.05 76	71.01 139	96.35 48	95.67 77	92.82 112	99.48 59	99.88 24

tfpn111		91.99 68	92.28 86	91.65 63	91.61 81	98.69 64	95.03 63	86.17 67	93.24 104	80.82 83	94.67 39	71.15 134	96.80 41	95.53 81	92.82 112	99.47 63	99.88 24

conf200view11		91.47 75	91.31 96	91.65 63	91.61 81	98.69 64	95.03 63	86.17 67	93.24 104	80.82 83	87.90 67	71.15 134	96.80 41	95.53 81	92.82 112	99.47 63	99.88 24

tfpn200view9		91.47 75	91.31 96	91.65 63	91.61 81	98.69 64	95.03 63	86.17 67	93.24 104	80.82 83	87.90 67	71.15 134	96.80 41	95.53 81	92.82 112	99.47 63	99.88 24

view600		90.97 85	90.70 108	91.30 69	91.53 87	98.69 64	94.33 74	86.17 67	91.75 123	80.19 90	86.06 85	70.90 140	96.10 53	95.53 81	92.08 125	99.47 63	99.86 30

view800		90.79 87	90.54 112	91.09 72	91.50 89	98.58 73	94.09 80	85.92 74	91.57 126	79.68 93	85.29 91	70.72 143	95.91 56	95.40 91	92.39 121	99.47 63	99.83 32

tfpn		91.26 79	91.55 92	90.92 73	91.47 90	98.50 76	93.85 84	85.72 76	91.40 127	79.30 97	84.78 93	77.33 117	95.70 61	95.29 93	93.73 87	99.47 63	99.82 34

DI_MVS_plusplus_trai		91.11 83	91.47 94	90.68 75	90.01 115	97.77 87	95.87 55	83.56 90	94.72 88	82.12 74	68.46 150	87.46 79	93.07 84	96.46 55	95.73 64	99.47 63	99.71 44

thres600view7		90.97 85	90.70 108	91.30 69	91.53 87	98.69 64	94.33 74	86.17 67	91.75 123	80.19 90	86.06 85	70.90 140	96.10 53	95.53 81	92.08 125	99.47 63	99.86 30

thres200		91.36 77	91.19 101	91.55 66	91.60 85	98.69 64	94.98 68	86.17 67	92.16 116	80.76 87	87.66 72	71.15 134	96.35 48	95.53 81	93.23 103	99.47 63	99.92 18

EPNet_dtu		89.82 96	94.18 60	84.74 121	96.87 47	95.54 125	92.65 94	86.91 61	96.99 60	54.17 201	92.41 51	88.54 74	78.35 190	96.15 61	96.05 60	99.47 63	93.60 195
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepC-MVS	92.23 5	94.53 45	94.26 59	94.86 36	96.73 48	99.50 32	97.85 34	95.45 18	96.22 73	82.73 66	80.68 111	88.02 76	96.92 37	97.49 39	98.20 12	99.47 63	99.69 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UA-Net		89.56 99	93.03 76	85.52 117	92.46 75	97.55 94	91.92 106	81.91 100	85.24 153	71.39 114	83.57 102	96.56 38	76.01 199	96.81 51	97.04 41	99.46 74	94.41 190

test-mter		88.25 115	93.27 74	82.38 141	83.89 153	96.86 105	87.10 146	72.80 171	94.58 90	61.85 149	83.21 105	90.65 71	89.18 116	95.43 90	92.58 120	99.46 74	99.61 59

Vis-MVSNet		87.60 120	91.31 96	83.27 134	89.14 122	98.04 82	90.35 120	79.42 117	87.23 140	66.92 129	79.10 116	84.63 91	74.34 205	95.81 71	96.06 59	99.46 74	98.32 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAPA-MVS	92.04 6	94.72 44	95.13 50	94.24 39	97.72 40	99.17 55	97.61 37	92.16 34	97.66 51	81.99 76	87.84 71	93.94 52	96.50 46	95.74 74	94.27 79	99.46 74	97.31 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
diffmvs		92.73 56	94.75 53	90.37 78	90.81 108	98.11 80	94.69 72	80.93 110	96.91 62	82.50 69	85.28 92	92.99 57	93.84 77	94.67 103	96.19 56	99.44 78	99.12 94

MVS_111021_LR		96.07 32	97.94 29	93.88 43	97.86 39	99.43 40	95.70 56	89.65 43	98.73 25	84.86 53	99.38 3	94.08 51	95.78 60	97.81 31	96.73 49	99.43 79	99.42 74

Vis-MVSNet (Re-imp)		91.05 84	94.43 54	87.11 103	91.05 100	97.99 84	92.53 97	83.82 89	92.71 114	76.28 102	84.50 97	92.43 61	79.52 184	97.24 41	97.68 23	99.43 79	98.45 117

PLC	94.37 2	97.03 26	96.54 37	97.60 9	98.84 23	98.64 70	98.17 31	94.99 29	99.01 19	96.80 1	93.21 48	95.64 41	97.36 29	96.37 56	94.79 71	99.41 81	98.12 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		94.18 48	95.12 51	93.09 51	98.40 31	99.17 55	94.20 78	81.92 99	98.47 29	86.52 44	90.92 57	84.21 93	98.12 22	95.88 69	97.59 27	99.40 82	99.58 62
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
Effi-MVS+		88.96 106	91.13 103	86.43 108	89.12 123	97.62 93	93.15 87	75.52 148	93.90 97	66.40 130	86.23 82	70.51 145	95.03 65	95.89 68	94.28 78	99.37 83	99.51 68

CHOSEN 1792x2688		88.63 110	89.01 126	88.19 98	94.83 58	99.21 54	92.66 93	79.85 116	92.40 115	72.18 113	56.38 202	80.22 106	90.24 109	97.64 37	97.28 34	99.37 83	99.94 12

PatchT		84.89 138	90.67 110	78.13 192	87.83 133	94.99 129	72.46 214	60.22 221	91.74 125	60.81 155	72.16 142	86.95 81	88.13 127	96.03 64	93.64 91	99.36 85	99.22 86

CHOSEN 280x420		94.51 46	97.78 31	90.70 74	95.54 57	99.49 33	94.14 79	74.91 153	98.43 30	85.32 51	94.78 37	99.19 10	94.95 67	97.02 48	96.18 57	99.35 86	99.36 76

GBi-Net		90.49 90	91.12 104	89.75 87	84.99 146	92.73 148	93.94 81	80.09 113	96.40 67	85.74 47	77.13 117	86.81 82	94.42 72	94.12 108	93.73 87	99.35 86	96.90 166

test1		90.49 90	91.12 104	89.75 87	84.99 146	92.73 148	93.94 81	80.09 113	96.40 67	85.74 47	77.13 117	86.81 82	94.42 72	94.12 108	93.73 87	99.35 86	96.90 166

FMVSNet2		89.51 100	89.63 121	89.38 90	84.99 146	92.73 148	93.94 81	79.28 119	93.73 98	84.28 56	69.36 149	82.32 99	94.42 72	96.16 60	96.22 55	99.35 86	96.90 166

GG-mvs-BLEND		67.99 217	97.35 34	33.72 233	1.22 239	99.72 13	98.30 29	0.57 238	97.61 54	1.18 242	93.26 46	96.63 37	1.74 238	97.15 45	97.14 36	99.34 90	99.96 6

PVSNet_Blended_VisFu		91.20 82	92.89 78	89.23 92	93.41 66	98.61 72	89.80 123	85.39 81	92.84 111	82.80 65	74.21 133	91.38 68	84.64 142	97.22 42	96.04 61	99.34 90	99.93 15

EPP-MVSNet		92.29 64	94.35 58	89.88 85	90.36 113	97.69 90	90.89 115	83.31 92	93.39 103	83.47 63	85.56 89	93.92 53	91.93 96	95.49 89	94.77 73	99.34 90	99.62 58

OMC-MVS		95.75 38	95.84 43	95.64 29	98.52 28	99.34 49	97.15 46	92.02 36	98.94 22	90.45 25	88.31 64	94.64 45	96.35 48	96.02 66	95.99 62	99.34 90	97.65 145

CLD-MVS		91.67 73	91.30 99	92.10 58	91.25 96	96.59 107	95.93 51	87.25 59	96.86 63	85.55 50	87.08 74	73.01 128	93.26 80	93.07 122	92.84 109	99.34 90	99.68 48
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UGNet		91.71 71	94.43 54	88.53 97	92.72 73	98.00 83	90.22 122	84.81 85	94.45 92	83.05 64	87.65 73	92.74 59	81.04 178	94.51 105	94.45 77	99.32 95	99.21 88
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
OPM-MVS		89.33 103	87.45 135	91.53 67	94.49 62	96.20 113	96.47 50	89.72 42	82.77 164	75.43 103	80.53 112	70.86 142	93.80 78	94.00 112	91.85 129	99.29 96	95.91 180

CNLPA		96.14 31	95.43 46	96.98 21	98.55 26	99.41 44	95.91 53	95.15 27	99.00 20	95.71 2	84.21 101	94.55 47	97.25 31	95.50 88	96.23 53	99.28 97	99.09 99

Fast-Effi-MVS+		86.94 126	87.88 132	85.84 113	86.99 136	95.80 119	91.24 112	73.48 167	92.75 112	69.22 124	72.70 139	65.71 155	94.84 68	94.98 99	94.71 74	99.26 98	98.48 116

TSAR-MVS + COLMAP		92.56 59	92.44 84	92.71 55	94.61 61	97.69 90	97.69 36	91.09 39	98.96 21	76.71 101	94.68 38	69.41 148	96.91 38	95.80 72	94.18 81	99.26 98	96.33 176

test0.0.03 1		88.71 107	92.22 88	84.63 122	88.08 130	94.71 134	85.91 169	78.00 132	95.54 80	72.96 109	86.10 84	85.88 87	83.59 152	92.95 126	93.24 102	99.25 100	97.09 158

HQP-MVS		91.94 69	93.03 76	90.66 76	93.69 63	96.48 110	95.92 52	89.73 41	97.33 56	72.65 110	95.37 32	73.56 127	92.75 88	94.85 100	94.12 82	99.23 101	99.51 68

HyFIR lowres test		87.86 119	88.25 130	87.40 100	94.67 60	98.54 74	90.33 121	76.51 144	89.60 136	70.89 117	51.43 217	85.69 88	92.79 86	96.59 54	95.96 63	99.22 102	99.94 12

IterMVS-LS		87.95 118	89.40 124	86.26 109	88.79 126	90.93 181	91.23 113	76.05 145	90.87 129	71.07 116	75.51 130	81.18 103	91.21 102	94.11 111	95.01 70	99.20 103	98.23 130
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchMatch-RL		92.54 60	92.82 80	92.21 57	96.57 49	98.74 61	91.85 107	86.30 66	96.23 72	85.18 52	95.21 33	73.58 126	94.22 75	95.40 91	93.08 104	99.14 104	97.49 151

MSDG		91.93 70	90.28 115	93.85 44	97.36 44	97.12 101	95.88 54	94.07 33	94.52 91	84.13 58	76.74 123	80.89 104	92.54 90	93.97 114	93.61 95	99.14 104	95.10 187

DWT-MVSNet_training		92.09 67	93.58 69	90.35 79	91.27 94	97.94 85	92.05 102	78.82 125	97.40 55	88.83 33	87.91 66	86.76 85	91.99 95	90.03 143	95.25 68	99.13 106	99.73 41

conf0.05thres1000		88.28 114	87.54 134	89.15 93	91.00 102	97.50 97	92.18 100	84.70 86	85.15 155	73.91 107	73.77 135	70.50 147	94.01 76	93.99 113	92.21 123	99.11 107	99.64 51

EPMVS		89.31 104	93.70 65	84.18 126	91.10 98	98.10 81	89.17 132	62.71 208	96.24 71	70.21 123	86.46 81	92.37 62	92.79 86	91.95 134	93.59 96	99.10 108	97.19 154

PCF-MVS	92.56 4	93.95 49	93.82 63	94.10 42	96.07 53	99.25 53	96.82 49	95.51 17	92.00 119	81.51 80	82.97 106	93.88 54	95.63 62	94.24 106	94.71 74	99.09 109	99.70 45
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GA-MVS		83.83 142	86.63 138	80.58 160	85.40 144	94.73 133	87.27 141	78.76 127	86.49 143	49.57 211	74.21 133	67.67 151	83.38 159	95.28 94	90.92 136	99.08 110	97.09 158

tfpn_n400		90.13 94	92.47 82	87.40 100	90.89 105	97.37 98	92.05 102	85.47 79	93.43 101	70.44 118	92.30 52	79.50 110	86.50 136	94.84 101	93.93 83	99.07 111	95.91 180

tfpnconf		90.13 94	92.47 82	87.40 100	90.89 105	97.37 98	92.05 102	85.47 79	93.43 101	70.44 118	92.30 52	79.50 110	86.50 136	94.84 101	93.93 83	99.07 111	95.91 180

Fast-Effi-MVS+-dtu		86.94 126	91.27 100	81.89 143	86.27 140	95.06 127	90.68 119	68.93 194	91.76 122	57.18 186	89.56 62	75.85 119	89.19 115	94.56 104	92.84 109	99.07 111	99.23 84

LGP-MVS_train		90.34 93	91.63 91	88.83 95	93.31 67	96.14 114	95.49 59	85.24 83	93.91 96	68.71 126	93.96 43	71.63 131	91.12 103	93.82 116	92.79 118	99.07 111	99.16 90

LS3D		92.70 57	92.23 87	93.26 48	96.24 52	98.72 62	97.93 33	96.17 3	96.41 66	72.46 112	81.39 109	80.76 105	97.66 25	95.69 76	95.62 65	99.07 111	97.02 162

UniMVSNet (Re)		83.28 144	83.16 150	83.42 132	81.93 161	93.12 146	86.27 153	80.83 111	85.88 147	68.23 127	64.56 159	60.58 161	84.25 146	89.13 158	89.44 151	99.04 116	99.40 75

FC-MVSNet-train		89.37 102	89.62 122	89.08 94	90.48 111	94.16 138	89.45 127	83.99 88	91.09 128	80.09 92	82.84 107	74.52 125	91.44 100	93.79 117	91.57 131	99.01 117	99.35 77

MS-PatchMatch		87.19 123	88.59 128	85.55 116	93.15 71	96.58 108	92.35 98	74.19 161	91.97 120	70.33 121	71.42 145	85.89 86	84.28 145	93.12 120	89.16 155	99.00 118	91.99 202

ACMM	89.40 10	90.58 89	90.02 118	91.23 71	93.30 68	94.75 132	90.69 118	88.22 50	95.20 82	82.70 67	88.54 63	71.40 133	93.48 79	93.64 119	90.94 135	98.99 119	95.72 184
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		86.78 128	90.29 114	82.69 140	90.55 110	96.95 104	88.49 134	62.58 209	95.09 84	63.52 141	76.67 125	84.00 94	92.05 93	87.93 164	91.89 128	98.98 120	99.50 70

UniMVSNet_NR-MVSNet		83.83 142	83.70 149	83.98 128	81.41 171	92.56 152	86.54 151	82.96 94	85.98 146	66.27 131	66.16 156	63.63 158	87.78 130	87.65 167	90.81 140	98.94 121	99.13 92

DU-MVS		82.87 147	82.16 155	83.70 131	80.77 190	92.24 157	86.54 151	81.91 100	86.41 144	66.27 131	63.95 160	55.66 199	87.78 130	86.83 188	90.86 138	98.94 121	99.13 92

PatchmatchNet		88.67 108	94.10 61	82.34 142	89.38 121	97.72 88	87.24 142	62.18 213	97.00 59	64.79 135	87.97 65	94.43 48	91.55 98	91.21 138	92.77 119	98.90 123	97.60 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		86.73 129	91.47 94	81.20 152	88.56 128	96.06 115	89.43 128	61.37 216	93.57 99	60.81 155	72.89 138	88.85 72	88.13 127	96.03 64	93.64 91	98.89 124	99.22 86

CDS-MVSNet		88.59 112	90.13 117	86.79 107	86.98 137	95.43 126	92.03 105	81.33 108	85.54 150	74.51 106	77.07 120	85.14 89	87.03 133	93.90 115	95.18 69	98.88 125	98.67 110
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
RPMNet		87.35 121	92.41 85	81.45 146	88.85 125	96.06 115	89.42 130	59.59 222	93.57 99	61.81 150	76.48 126	91.48 67	90.18 110	96.32 58	93.37 99	98.87 126	99.59 60

CSCG		95.77 37	95.35 48	96.26 25	99.13 15	99.60 27	98.14 32	91.89 37	96.57 65	92.61 16	89.65 61	91.74 65	96.96 34	93.69 118	96.58 51	98.86 127	99.63 55

NR-MVSNet		82.37 149	81.95 157	82.85 138	82.56 160	92.24 157	87.49 138	81.91 100	86.41 144	65.51 133	63.95 160	52.93 211	80.80 180	89.41 154	89.61 148	98.85 128	99.10 98

TranMVSNet+NR-MVSNet		82.07 151	81.36 160	82.90 137	80.43 196	91.39 175	87.16 144	82.75 95	84.28 159	62.98 142	62.28 164	56.01 196	85.30 141	86.06 198	90.69 142	98.80 129	98.80 106

v18		80.16 172	80.01 180	80.34 164	81.72 163	85.71 205	86.58 150	70.68 181	83.23 160	60.78 159	60.39 167	58.50 172	83.49 154	87.03 177	88.19 171	98.79 130	97.06 160

v8		80.61 167	80.61 169	80.62 158	81.51 166	91.00 180	86.06 159	74.07 163	81.78 171	59.93 165	60.10 172	58.42 173	83.35 162	86.99 181	88.11 182	98.79 130	97.83 143

WR-MVS_H		79.76 177	80.07 177	79.40 177	81.25 172	91.73 171	82.77 190	74.82 155	79.02 197	62.55 143	59.41 178	57.32 188	76.27 198	87.61 168	87.30 197	98.78 132	98.09 137

pmmvs5		80.48 168	81.43 159	79.36 179	81.50 167	92.24 157	82.07 195	74.08 162	78.10 198	55.86 191	67.72 153	54.35 206	83.91 151	92.97 124	88.65 160	98.77 133	96.01 178

ADS-MVSNet		86.68 130	90.79 107	81.88 144	90.38 112	96.81 106	86.90 147	60.50 220	96.01 76	63.93 138	81.67 108	84.72 90	90.78 105	87.03 177	91.67 130	98.77 133	97.63 146

FMVSNet5		87.06 125	89.52 123	84.20 125	79.92 203	86.57 203	87.11 145	72.37 173	96.06 75	75.41 104	84.33 98	91.76 64	91.60 97	91.51 136	91.22 133	98.77 133	85.16 218

v1neww		81.04 159	80.86 163	81.25 149	81.48 168	92.14 162	86.06 159	78.41 129	82.02 168	59.43 169	60.09 173	58.30 176	83.37 160	87.02 179	88.15 175	98.76 136	98.33 124

v7new		81.04 159	80.86 163	81.25 149	81.48 168	92.14 162	86.06 159	78.41 129	82.02 168	59.43 169	60.09 173	58.30 176	83.37 160	87.02 179	88.15 175	98.76 136	98.33 124

v17		79.95 174	79.87 182	80.05 167	81.55 165	85.65 206	86.10 158	70.44 183	82.59 165	60.02 164	60.26 168	58.53 171	83.41 158	86.98 183	88.09 183	98.76 136	97.02 162

v6		81.06 158	80.87 162	81.28 148	81.47 170	92.12 164	86.14 155	78.42 128	81.99 170	59.68 167	60.14 169	58.36 174	83.22 165	86.99 181	88.14 177	98.76 136	98.32 126

ACMP	89.80 9	90.72 88	91.15 102	90.21 81	92.55 74	96.52 109	92.63 95	85.71 77	94.65 89	81.06 82	93.32 45	70.56 144	90.52 107	92.68 128	91.05 134	98.76 136	99.31 81
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v16		80.03 173	79.95 181	80.13 166	81.64 164	85.63 207	86.17 154	70.42 184	83.12 162	60.34 162	60.11 170	58.61 170	83.45 156	86.98 183	88.12 181	98.75 141	97.05 161

divwei89l23v2f112		80.69 165	80.42 172	81.02 155	81.13 177	92.04 165	85.95 166	78.92 123	80.45 185	58.43 177	59.34 179	57.46 181	82.92 169	87.09 174	88.16 173	98.75 141	98.36 123

IterMVS		85.02 137	88.98 127	80.41 162	87.03 135	90.34 191	89.78 124	69.45 190	89.77 135	54.04 202	73.71 136	82.05 101	83.44 157	95.11 97	93.64 91	98.75 141	98.22 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1		80.69 165	80.38 174	81.05 153	81.13 177	92.02 167	86.02 163	78.93 122	80.32 191	58.65 173	59.29 181	57.45 182	82.83 172	87.07 175	88.14 177	98.74 144	98.37 121

v2v482		80.86 162	80.52 171	81.25 149	80.79 189	91.85 169	85.68 174	78.78 126	81.05 176	58.09 181	60.46 166	56.08 194	85.45 139	87.27 171	88.53 161	98.73 145	98.38 120

v1141		80.70 164	80.42 172	81.02 155	81.14 176	92.03 166	85.94 167	78.92 123	80.59 183	58.40 179	59.32 180	57.41 185	82.97 168	87.10 173	88.16 173	98.72 146	98.37 121

v7		80.74 163	80.95 161	80.50 161	81.23 173	91.58 172	86.12 156	74.83 154	82.30 167	57.64 183	58.74 189	57.45 182	84.48 143	89.75 147	88.27 167	98.72 146	98.57 113

V14		79.33 187	79.18 192	79.51 175	81.00 182	85.46 210	85.88 170	69.79 187	80.52 184	58.76 172	59.16 183	57.52 179	82.91 170	86.86 187	87.90 188	98.72 146	96.87 170

MIMVSNet		82.87 147	86.17 139	79.02 186	77.23 214	92.88 147	84.88 181	60.62 219	86.72 142	64.16 136	73.58 137	71.48 132	88.51 123	94.14 107	93.50 98	98.72 146	90.87 207

v10		80.38 169	80.73 166	79.96 169	81.22 174	90.40 190	86.11 157	71.63 175	82.42 166	57.65 182	58.74 189	57.47 180	84.44 144	89.75 147	88.28 166	98.71 150	98.06 139

CP-MVSNet		79.90 175	79.49 186	80.38 163	80.72 192	90.83 182	82.98 189	75.17 151	79.70 193	61.39 152	59.74 176	51.98 214	83.31 163	87.37 169	88.38 164	98.71 150	98.45 117

v15		79.35 185	79.20 191	79.54 174	81.08 180	85.48 208	85.92 168	70.02 186	80.60 182	58.63 174	59.14 184	57.40 186	82.87 171	86.89 186	87.95 187	98.70 152	96.92 165

V42		80.88 161	80.74 165	81.05 153	81.21 175	92.01 168	85.96 165	77.75 134	81.62 173	59.73 166	59.93 175	58.35 175	82.98 167	86.90 185	88.06 186	98.69 153	98.32 126

V9		79.23 188	79.09 195	79.39 178	80.95 185	85.40 211	85.85 171	69.63 188	80.42 186	58.45 176	58.94 186	57.42 184	82.77 173	86.79 192	87.85 190	98.69 153	96.83 171

v1144		80.36 170	80.63 168	80.05 167	80.86 188	91.56 173	85.78 173	75.22 150	80.73 180	55.83 192	58.51 192	56.99 192	83.93 150	89.79 146	88.25 168	98.68 155	98.56 114

v12		79.16 189	79.04 196	79.30 182	80.88 186	85.37 213	85.45 178	69.52 189	80.39 187	58.57 175	58.90 187	57.17 190	82.68 175	86.76 193	87.82 191	98.68 155	96.88 169

CostFormer		89.42 101	91.67 90	86.80 106	89.99 116	96.33 112	90.75 116	64.79 204	95.17 83	83.62 62	86.20 83	82.15 100	92.96 85	89.22 157	92.94 105	98.68 155	99.65 49

PMMVS		93.05 54	95.40 47	90.31 80	91.41 93	97.54 95	92.62 96	83.25 93	98.08 43	79.44 96	95.18 34	88.52 75	96.43 47	95.70 75	93.88 85	98.68 155	98.91 103

v13		79.09 190	78.98 197	79.22 184	80.88 186	85.34 215	85.50 177	69.40 191	80.36 189	58.14 180	58.62 191	57.30 189	82.70 174	86.72 194	87.75 193	98.67 159	96.76 172

v11		79.54 183	79.71 185	79.35 180	80.96 183	85.36 214	85.81 172	69.10 193	81.49 174	57.63 184	58.90 187	57.07 191	83.94 149	90.09 142	88.08 185	98.66 160	96.97 164

PS-CasMVS		79.06 191	78.58 198	79.63 171	80.59 194	90.55 187	82.54 193	75.04 152	77.76 199	58.84 171	58.16 195	50.11 219	82.09 177	87.05 176	88.18 172	98.66 160	98.27 129

v1192		79.84 176	80.05 179	79.61 172	80.49 195	91.04 179	85.56 175	74.37 159	80.73 180	54.35 197	57.07 199	54.54 205	84.23 147	89.94 144	88.38 164	98.63 162	98.61 111

v144192		79.61 181	79.77 183	79.41 176	80.28 197	91.06 178	84.87 182	73.86 164	79.65 194	55.38 193	57.76 197	55.20 200	83.46 155	88.42 159	87.89 189	98.61 163	98.42 119

WR-MVS		79.67 178	80.25 175	79.00 187	80.65 193	91.16 177	83.31 187	76.57 143	80.97 177	60.50 161	59.20 182	58.66 169	74.38 204	85.85 200	87.76 192	98.61 163	98.14 132

v1921920		79.55 182	79.77 183	79.30 182	80.24 198	90.77 183	85.37 179	73.75 165	80.38 188	53.78 204	56.89 201	54.18 208	84.05 148	89.55 151	88.13 180	98.59 165	98.52 115

PEN-MVS		78.80 194	78.13 201	79.58 173	80.03 200	89.67 194	83.61 186	75.83 146	77.71 201	58.41 178	60.11 170	50.00 220	81.02 179	84.08 208	88.14 177	98.59 165	97.18 156

v1240		78.97 192	79.27 190	78.63 188	80.04 199	90.61 185	84.25 184	72.95 170	79.22 196	52.70 206	56.22 203	52.88 213	83.28 164	89.60 150	88.20 170	98.56 167	98.14 132

anonymousdsp		81.29 155	84.52 148	77.52 194	79.83 204	92.62 151	82.61 192	70.88 180	80.76 179	50.82 208	68.35 152	68.76 149	82.45 176	93.00 123	89.45 150	98.55 168	98.69 109

FC-MVSNet-test		85.51 132	89.08 125	81.35 147	85.31 145	93.35 141	87.65 137	77.55 135	90.01 134	64.07 137	79.63 114	81.83 102	74.94 202	92.08 131	90.83 139	98.55 168	95.81 183

EG-PatchMatch MVS		78.32 195	79.42 188	77.03 199	83.03 155	93.77 140	84.47 183	69.26 192	75.85 208	53.69 205	55.68 207	60.23 164	73.20 207	89.69 149	88.22 169	98.55 168	92.54 198

dps		88.66 109	90.19 116	86.88 105	89.94 117	96.48 110	89.56 125	64.08 206	94.12 94	89.00 32	83.39 104	82.56 97	90.16 111	86.81 191	89.26 153	98.53 171	98.71 108

USDC		85.11 136	85.35 143	84.83 120	89.45 120	94.93 130	92.98 88	77.30 138	90.53 131	61.80 151	76.69 124	59.62 166	88.90 117	92.78 127	90.79 141	98.53 171	92.12 199

tfpnnormal		81.11 157	79.33 189	83.19 135	84.23 150	92.29 155	86.76 149	82.27 98	72.67 213	62.02 148	56.10 204	53.86 209	85.35 140	92.06 132	89.23 154	98.49 173	99.11 97

pmmvs4		84.88 139	84.67 146	85.13 118	82.80 157	92.37 153	87.29 140	79.08 120	90.51 132	74.94 105	70.37 148	62.49 159	88.17 126	92.01 133	88.51 162	98.49 173	96.44 173

v148		79.66 179	79.13 194	80.27 165	81.02 181	91.76 170	81.90 196	79.32 118	79.24 195	63.79 140	58.07 196	54.34 207	77.17 195	84.42 207	87.52 196	98.40 175	98.59 112

DTE-MVSNet		77.92 196	77.42 205	78.51 190	79.34 205	89.00 197	83.05 188	75.60 147	76.89 203	56.58 187	59.63 177	50.31 217	78.09 193	82.57 215	87.56 195	98.38 176	95.95 179

pm-mvs1		81.68 152	81.70 158	81.65 145	82.61 159	92.26 156	85.54 176	78.95 121	76.29 207	63.81 139	58.43 193	66.33 154	80.63 181	92.30 129	89.93 146	98.37 177	96.39 175

v7n		77.71 197	78.25 200	77.09 198	78.49 208	90.55 187	82.15 194	71.11 179	76.79 204	54.18 200	55.63 208	50.20 218	78.28 191	89.36 156	87.15 198	98.33 178	98.07 138

Baseline_NR-MVSNet		82.08 150	80.64 167	83.77 130	80.77 190	88.50 198	86.88 148	81.71 104	85.58 149	68.80 125	58.20 194	57.75 178	86.16 138	86.83 188	88.68 159	98.33 178	98.90 104

TransMVSNet (Re)		79.51 184	78.36 199	80.84 157	83.17 154	89.72 193	84.22 185	81.45 107	73.98 211	60.79 158	57.20 198	56.05 195	77.11 196	89.88 145	88.86 157	98.30 180	92.83 197

ACMH+	85.62 12	85.27 135	84.96 144	85.64 115	90.84 107	94.78 131	87.46 139	81.30 109	86.94 141	67.35 128	74.56 132	64.09 157	88.70 119	88.14 161	89.00 156	98.22 181	97.19 154

COLMAP_ROB	84.42 15	88.24 116	87.32 136	89.32 91	95.83 54	95.82 118	92.81 90	87.68 57	92.09 118	72.64 111	72.34 141	79.96 108	88.79 118	89.54 152	89.46 149	98.16 182	92.00 201
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet1		85.85 131	84.91 145	86.96 104	82.70 158	91.39 175	91.54 110	77.45 136	85.29 152	79.56 95	60.70 165	72.68 129	92.37 91	94.12 108	93.73 87	98.12 183	96.44 173

MVS-HIRNet		79.34 186	82.56 151	75.57 202	84.11 151	95.02 128	75.03 211	57.28 225	85.50 151	55.88 190	53.00 214	70.51 145	83.05 166	92.12 130	91.96 127	98.09 184	89.83 210

CPTT-MVS		97.32 23	97.60 33	96.99 20	98.29 32	99.31 51	99.04 23	94.67 30	97.99 46	93.12 14	98.03 19	98.26 22	98.77 11	96.08 63	94.26 80	98.07 185	99.27 82

ACMH	85.22 13	85.40 133	85.73 142	85.02 119	91.76 78	94.46 137	84.97 180	81.54 106	85.18 154	65.22 134	76.92 122	64.22 156	88.58 122	90.17 141	90.25 145	98.03 186	98.90 104
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testgi		82.88 146	86.14 140	79.08 185	86.05 141	92.20 160	81.23 199	74.77 156	88.70 137	57.63 184	86.73 79	61.53 160	76.83 197	90.33 140	89.43 152	97.99 187	94.05 192

V4		77.67 199	78.01 203	77.28 197	77.82 212	90.56 186	81.70 198	71.63 175	76.33 206	55.38 193	55.74 205	55.83 198	79.20 187	84.02 209	86.01 203	97.97 188	97.55 149

v52		77.69 198	78.04 202	77.29 196	77.79 213	90.54 189	81.76 197	71.62 177	76.52 205	55.34 195	55.70 206	55.91 197	79.27 186	84.02 209	86.03 202	97.96 189	97.56 148

tpm		83.97 141	87.97 131	79.31 181	87.35 134	93.21 144	86.00 164	61.90 214	90.69 130	54.01 203	79.42 115	75.61 121	88.65 120	87.18 172	90.48 143	97.95 190	99.21 88

TAMVS		85.35 134	86.00 141	84.59 123	84.97 149	95.57 124	88.98 133	77.29 139	81.44 175	71.36 115	71.48 144	75.00 124	87.03 133	91.92 135	92.21 123	97.92 191	94.40 191

tpm cat1		87.34 122	88.52 129	85.95 111	89.83 119	95.80 119	90.73 117	64.91 203	92.99 110	82.21 72	71.19 147	82.68 96	90.13 112	86.38 195	90.87 137	97.90 192	99.74 39

tpmp4_e23		88.10 117	90.02 118	85.86 112	89.94 117	95.73 122	91.83 108	64.92 202	94.79 87	78.25 100	81.03 110	78.34 115	92.33 92	88.10 162	92.82 112	97.90 192	99.34 80

TinyColmap		83.03 145	82.24 154	83.95 129	88.88 124	93.22 143	89.48 126	76.89 141	87.53 139	62.12 146	68.46 150	55.03 201	88.43 124	90.87 139	89.65 147	97.89 194	90.91 206

v748		76.68 206	76.82 208	76.51 200	78.70 206	90.06 192	77.12 206	73.40 168	73.32 212	59.57 168	55.00 211	50.71 216	72.48 208	83.71 212	86.78 199	97.81 195	98.13 135

TDRefinement		81.49 154	80.08 176	83.13 136	91.02 101	94.53 135	91.66 109	82.43 96	81.70 172	62.12 146	62.30 163	59.32 167	73.93 206	87.31 170	85.29 205	97.61 196	90.14 208

Anonymous20231206		74.59 209	77.00 207	71.78 209	77.89 211	87.45 202	75.14 210	72.29 174	77.76 199	46.65 216	52.14 215	52.93 211	61.10 218	89.37 155	88.09 183	97.59 197	91.30 204

test20.03		72.81 211	76.24 209	68.80 213	78.31 209	85.40 211	71.04 215	71.20 178	71.85 215	43.40 221	65.31 157	54.71 204	51.27 224	85.92 199	84.18 211	97.58 198	86.35 217

CVMVSNet		84.01 140	86.91 137	80.61 159	88.39 129	93.29 142	86.06 159	82.29 97	83.13 161	54.29 198	72.68 140	79.59 109	75.11 201	91.23 137	92.91 106	97.54 199	95.58 185

SixPastTwentyTwo		80.28 171	82.06 156	78.21 191	81.89 162	92.35 154	77.72 204	74.48 157	83.04 163	54.22 199	76.06 127	56.40 193	83.55 153	86.83 188	84.83 208	97.38 200	94.93 188

pmmvs-eth3d		75.17 208	74.09 213	76.43 201	72.92 218	84.49 216	76.61 207	72.42 172	74.33 209	61.28 154	54.71 212	39.42 228	78.20 192	87.77 165	84.25 209	97.17 201	93.63 194

LP		77.20 200	79.14 193	74.92 205	86.71 138	90.62 184	77.97 203	57.87 224	85.88 147	50.75 209	55.29 209	66.34 153	79.39 185	80.75 216	85.03 206	96.86 202	90.09 209

LTVRE_ROB	79.45 16	79.66 179	80.55 170	78.61 189	83.01 156	92.19 161	87.18 143	73.69 166	71.70 216	43.22 222	71.22 146	50.85 215	87.82 129	89.47 153	90.43 144	96.75 203	98.00 142
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
MIMVSNet1		68.63 216	70.24 217	66.76 219	56.86 232	83.26 218	67.93 219	70.26 185	68.05 220	46.80 215	40.44 225	48.15 221	62.01 216	84.96 206	84.86 207	96.69 204	81.93 219

testpf		81.62 153	87.82 133	74.38 207	85.88 142	89.26 196	74.45 212	48.92 232	95.87 79	60.31 163	76.95 121	80.17 107	80.07 183	85.72 202	88.77 158	96.67 205	98.01 140

MDTV_nov1_ep13_2view		78.83 193	82.35 152	74.73 206	78.65 207	91.51 174	79.18 201	62.52 210	84.51 157	52.51 207	67.49 154	67.29 152	78.90 188	85.52 203	86.34 201	96.62 206	93.76 193

MDTV_nov1_ep13		89.63 98	94.38 57	84.09 127	88.76 127	97.53 96	89.37 131	68.46 197	96.95 61	70.27 122	87.88 70	93.67 55	91.04 104	93.12 120	93.83 86	96.62 206	97.68 144

PM-MVS		75.81 207	76.11 210	75.46 204	73.81 215	85.48 208	76.42 208	70.57 182	80.05 192	54.75 196	62.33 162	39.56 227	80.59 182	87.71 166	82.81 214	96.61 208	94.81 189

EU-MVSNet		76.76 205	79.47 187	73.60 208	79.99 201	87.47 201	77.39 205	75.43 149	77.62 202	47.83 214	64.78 158	60.44 163	64.80 213	86.28 196	86.53 200	96.17 209	93.19 196

N_pmnet		76.83 202	77.97 204	75.50 203	80.96 183	88.23 200	72.81 213	76.83 142	80.87 178	50.55 210	56.94 200	60.09 165	75.70 200	83.28 213	84.23 210	96.14 210	92.12 199

testus		72.50 212	77.19 206	67.04 216	73.69 217	80.06 222	67.65 221	68.24 198	84.46 158	37.48 231	75.90 129	40.07 226	59.40 220	85.45 204	87.69 194	95.76 211	86.70 216

test2356		74.04 210	80.07 177	67.01 218	73.77 216	80.65 221	67.82 220	66.87 200	84.93 156	37.70 229	75.45 131	57.40 186	60.26 219	86.28 196	88.47 163	95.64 212	87.33 215

Effi-MVS+-dtu		87.18 124	90.48 113	83.32 133	86.51 139	95.76 121	91.16 114	74.28 160	90.44 133	61.31 153	86.72 80	72.68 129	91.25 101	95.01 98	93.64 91	95.45 213	99.12 94

DeepPCF-MVS	94.02 3	95.92 35	98.47 17	92.95 52	97.57 42	99.79 6	91.45 111	94.42 31	99.76 1	86.48 45	92.88 50	98.12 25	92.62 89	99.49 2	99.32 2	95.15 214	99.95 9

new-patchmatchnet		67.66 218	68.07 218	67.18 215	72.85 219	82.86 219	63.09 226	68.61 196	66.60 221	42.64 224	49.28 218	38.68 229	61.21 217	75.84 220	75.22 224	94.67 215	88.00 214

pmmvs6		76.79 203	75.69 211	78.09 193	79.95 202	89.57 195	80.92 200	74.46 158	64.79 222	60.74 160	45.71 223	60.55 162	78.37 189	88.04 163	86.00 204	94.07 216	95.15 186

pmmvs3		69.04 215	70.75 215	67.04 216	66.83 224	78.54 223	64.99 225	60.92 218	64.67 223	40.61 226	55.08 210	40.29 225	74.89 203	83.76 211	84.01 212	93.98 217	88.88 212

new_pmnet		71.86 213	73.67 214	69.75 212	72.56 220	84.20 217	70.95 217	66.81 201	80.34 190	43.62 220	51.60 216	53.81 210	71.24 210	82.91 214	80.93 215	93.35 218	81.92 220

1111		61.69 221	63.75 220	59.29 222	64.35 225	70.45 228	48.44 231	48.86 233	55.76 226	39.40 227	39.25 226	54.73 202	62.55 214	77.84 218	80.37 217	92.16 219	67.84 231

RPSCF		89.81 97	89.75 120	89.88 85	93.22 70	93.99 139	94.78 69	85.23 84	94.01 95	82.52 68	95.00 35	87.23 80	92.01 94	85.16 205	83.48 213	91.54 220	89.38 211

testmv		60.16 222	62.42 221	57.53 223	67.85 222	69.87 230	48.47 229	62.44 211	54.75 228	29.08 233	46.99 221	31.77 232	45.97 227	74.85 222	79.08 220	91.39 221	79.62 223

test1235678		60.16 222	62.41 222	57.53 223	67.85 222	69.86 231	48.47 229	62.43 212	54.73 229	29.08 233	46.99 221	31.76 233	45.97 227	74.84 223	79.07 221	91.39 221	79.61 224

CMPMVS	58.73 17	76.78 204	74.27 212	79.70 170	93.26 69	95.58 123	82.74 191	77.44 137	71.46 219	56.29 189	53.58 213	59.13 168	77.33 194	79.20 217	79.71 218	91.14 223	81.24 221
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDA-MVSNet-bldmvs		69.61 214	70.36 216	68.74 214	62.88 229	88.50 198	65.40 224	77.01 140	71.60 218	43.93 217	66.71 155	35.33 230	72.47 209	61.01 229	80.63 216	90.73 224	88.75 213

test12356		57.24 224	59.66 225	54.43 226	64.26 227	66.14 232	49.96 228	61.73 215	54.37 230	28.80 235	44.89 224	25.68 235	32.36 232	70.23 226	79.19 219	89.46 225	77.11 225

Anonymous20231211		63.52 219	62.24 223	65.02 221	68.68 221	78.21 224	65.79 223	68.17 199	49.86 232	42.89 223	29.67 232	34.65 231	55.41 222	75.07 221	76.98 222	89.18 226	91.26 205

ambc			64.61 219		61.80 230	75.31 226	71.00 216		74.16 210	48.83 212	36.02 230	13.22 240	58.66 221	85.80 201	76.26 223	88.01 227	91.53 203

DeepMVS_CX						85.88 204	69.83 218	81.56 105	87.99 138	48.22 213	71.85 143	45.52 222	68.67 211	63.21 227		86.64 228	80.03 222

PMMVS2		50.69 228	52.33 228	48.78 228	51.24 234	64.81 233	47.91 233	53.79 231	44.95 233	21.75 236	29.98 231	25.90 234	31.98 234	59.95 230	65.37 227	86.00 229	75.36 227

tmp_tt				71.24 211	90.29 114	76.39 225	65.81 222	59.43 223	97.62 52	79.65 94	90.60 60	68.71 150	49.71 225	72.71 224	65.70 226	82.54 230

Gipuma		54.59 225	53.98 227	55.30 225	59.03 231	52.63 235	47.17 234	56.08 228	71.68 217	37.54 230	20.90 234	19.00 236	52.33 223	71.69 225	75.20 225	79.64 231	66.79 232
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		63.27 220	61.31 224	65.57 220	78.25 210	74.42 227	75.23 209	68.92 195	72.33 214	43.87 218	49.01 219	43.94 223	48.64 226	61.15 228	58.81 230	78.51 232	69.49 230

no-one		41.64 229	41.19 230	42.16 230	52.35 233	58.34 234	27.46 237	57.21 226	28.41 238	21.09 237	19.65 235	17.04 237	21.39 237	39.74 233	61.20 229	73.45 233	63.95 235

PMVS	49.05 18	51.88 226	50.56 229	53.42 227	64.21 228	43.30 237	42.64 235	62.93 207	50.56 231	43.72 219	37.44 228	42.95 224	35.05 231	58.76 231	54.58 231	71.95 234	66.33 233
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		37.15 230	34.82 232	39.86 231	47.53 236	35.42 239	23.79 238	55.26 229	35.18 236	14.12 239	17.38 238	14.13 239	39.73 230	32.24 234	46.98 232	58.76 235	62.39 236

EMVS		36.45 231	33.63 233	39.74 232	48.47 235	35.73 238	23.59 239	55.11 230	35.61 235	12.88 240	17.49 236	14.62 238	41.04 229	29.33 235	43.00 233	57.32 236	59.62 237

MVE	42.40 19	36.00 232	38.65 231	32.92 234	29.16 237	46.17 236	22.61 240	44.21 235	26.44 239	18.88 238	17.41 237	9.36 241	32.29 233	45.75 232	61.38 228	50.35 237	64.03 234
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
.test1245		51.60 227	57.21 226	45.06 229	64.35 225	70.45 228	48.44 231	48.86 233	55.76 226	39.40 227	39.25 226	54.73 202	62.55 214	77.84 218	27.11 234	6.75 238	75.30 228

testmvs		21.55 233	30.91 234	10.62 235	2.78 238	11.66 240	18.51 241	4.82 236	38.21 234	4.06 241	36.35 229	4.47 242	26.81 235	23.27 236	27.11 234	6.75 238	75.30 228

test123		16.81 234	24.80 235	7.48 236	0.82 240	8.38 241	11.92 242	2.60 237	28.96 237	1.12 243	28.39 233	1.26 243	24.51 236	8.93 237	22.19 236	3.90 240	75.49 226

sosnet-low-res		0.00 235	0.00 236	0.00 237	0.00 241	0.00 242	0.00 243	0.00 239	0.00 240	0.00 244	0.00 239	0.00 244	0.00 239	0.00 238	0.00 237	0.00 241	0.00 238

sosnet		0.00 235	0.00 236	0.00 237	0.00 241	0.00 242	0.00 243	0.00 239	0.00 240	0.00 244	0.00 239	0.00 244	0.00 239	0.00 238	0.00 237	0.00 241	0.00 238

MTAPA										94.58 9		98.56 18

MTMP										95.24 4		98.13 24

Patchmatch-RL test							37.05 236

mPP-MVS					98.66 24							97.11 34

NP-MVS									97.69 50

Patchmtry						95.86 117	89.43 128	61.37 216		60.81 155

High-resolution multi-view benchmark