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		95.70 1	96.40 1	93.61 2	98.67 1	85.39 29	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 11	85.07 37	99.27 3	99.54 1

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 13	90.26 4	95.70 2	96.46 2	90.58 8	92.86 42	96.29 18	88.16 26	94.17 69	86.07 33	98.48 19	97.22 26

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 50	85.72 28	96.79 1	95.51 4	88.86 13	95.63 10	96.99 8	84.81 55	93.16 123	91.10 1	97.53 59	96.58 39
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
abl_6		93.02 4	93.16 4	92.60 4	94.73 39	88.99 7	93.26 10	94.19 19	89.11 11	94.43 19	95.27 42	91.86 4	95.09 44	87.54 19	98.02 39	93.71 111

HPM-MVS_fast		92.50 5	92.54 5	92.37 5	95.93 14	85.81 27	92.99 11	94.23 16	85.21 24	92.51 51	95.13 46	90.65 11	95.34 35	88.06 10	98.15 34	95.95 51

HPM-MVS		92.13 6	92.20 7	91.91 15	95.58 23	84.67 38	93.51 6	94.85 9	82.88 43	91.77 64	93.94 91	90.55 13	95.73 20	88.50 8	98.23 31	95.33 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 65	85.17 31	92.47 21	95.05 8	87.65 19	93.21 36	94.39 72	90.09 14	95.08 45	86.67 25	97.60 57	94.18 94

COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 51	86.15 18	93.37 8	95.10 7	90.28 9	92.11 56	95.03 48	89.75 15	94.93 49	79.95 104	98.27 29	95.04 76
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 9	91.87 14	92.03 11	95.53 24	85.91 22	93.35 9	94.16 20	82.52 47	92.39 55	94.14 80	89.15 17	95.62 22	87.35 20	98.24 30	94.56 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
mPP-MVS		91.69 10	91.47 20	92.37 5	96.04 11	88.48 8	92.72 14	92.60 72	83.09 40	91.54 67	94.25 76	87.67 33	95.51 30	87.21 24	98.11 35	93.12 126

CP-MVS		91.67 11	91.58 17	91.96 12	95.29 28	87.62 9	93.38 7	93.36 37	83.16 39	91.06 73	94.00 84	88.26 23	95.71 21	87.28 23	98.39 23	92.55 144

XVS		91.54 12	91.36 22	92.08 8	95.64 21	86.25 16	92.64 15	93.33 40	85.07 25	89.99 87	94.03 83	86.57 43	95.80 16	87.35 20	97.62 53	94.20 92

MTAPA		91.52 13	91.60 16	91.29 25	96.59 3	86.29 14	92.02 24	91.81 91	84.07 32	92.00 59	94.40 70	86.63 41	95.28 38	88.59 4	98.31 26	92.30 152

UA-Net		91.49 14	91.53 18	91.39 22	94.98 32	82.95 50	93.52 5	92.79 65	88.22 16	88.53 124	97.64 3	83.45 66	94.55 60	86.02 34	98.60 14	96.67 36

ACMMPR		91.49 14	91.35 24	91.92 14	95.74 18	85.88 24	92.58 18	93.25 47	81.99 53	91.40 70	94.17 79	87.51 34	95.87 13	87.74 12	97.76 48	93.99 99

LPG-MVS_test		91.47 16	91.68 15	90.82 34	94.75 36	81.69 51	90.00 40	94.27 13	82.35 48	93.67 30	94.82 55	91.18 6	95.52 28	85.36 35	98.73 8	95.23 72

region2R		91.44 17	91.30 26	91.87 16	95.75 17	85.90 23	92.63 17	93.30 43	81.91 55	90.88 78	94.21 77	87.75 30	95.87 13	87.60 17	97.71 51	93.83 104

HFP-MVS		91.30 18	91.39 21	91.02 29	95.43 25	84.66 39	92.58 18	93.29 45	81.99 53	91.47 68	93.96 87	88.35 21	95.56 25	87.74 12	97.74 49	92.85 130

zzz-MVS		91.27 19	91.26 27	91.29 25	96.59 3	86.29 14	88.94 65	91.81 91	84.07 32	92.00 59	94.40 70	86.63 41	95.28 38	88.59 4	98.31 26	92.30 152

APDe-MVS		91.22 20	91.92 9	89.14 57	92.97 67	78.04 78	92.84 12	94.14 21	83.33 37	93.90 26	95.73 29	88.77 18	96.41 1	87.60 17	97.98 42	92.98 129

PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 40	93.90 28	80.32 69	91.74 65	94.41 69	88.17 25	95.98 8	86.37 26	97.99 41	93.96 101

SteuartSystems-ACMMP		91.16 22	91.36 22	90.55 37	93.91 47	80.97 58	91.49 29	93.48 36	82.82 44	92.60 50	93.97 85	88.19 24	96.29 3	87.61 16	98.20 33	94.39 90
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 23	90.91 34	91.83 18	96.18 10	86.88 11	92.20 22	93.03 56	82.59 46	88.52 125	94.37 73	86.74 40	95.41 33	86.32 27	98.21 32	93.19 125

MP-MVS-pluss		90.81 24	91.08 29	89.99 46	95.97 12	79.88 63	88.13 76	94.51 11	75.79 131	92.94 38	94.96 50	88.36 20	95.01 47	90.70 2	98.40 21	95.09 75
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 25	91.50 19	88.44 64	93.00 66	76.26 100	89.65 50	95.55 3	87.72 18	93.89 27	94.94 51	91.62 5	93.44 110	78.35 118	98.76 5	95.61 64

ACMMP_Plus		90.65 26	91.07 30	89.42 53	95.93 14	79.54 69	89.95 43	93.68 31	77.65 103	91.97 61	94.89 52	88.38 19	95.45 31	89.27 3	97.87 46	93.27 121

ACMM	79.39 9	90.65 26	90.99 31	89.63 50	95.03 31	83.53 44	89.62 53	93.35 39	79.20 82	93.83 28	93.60 96	90.81 9	92.96 131	85.02 39	98.45 20	92.41 147
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 28	90.34 42	91.38 23	89.03 150	84.23 42	93.58 4	94.68 10	90.65 7	90.33 82	93.95 90	84.50 58	95.37 34	80.87 89	95.50 121	94.53 85

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 41	80.98 57	89.16 62	94.05 23	79.03 86	92.87 41	93.74 94	90.60 12	95.21 42	82.87 66	98.76 5	94.87 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
#test#		90.49 30	90.31 43	91.02 29	95.43 25	84.66 39	90.65 35	93.29 45	77.00 118	91.47 68	93.96 87	88.35 21	95.56 25	84.88 40	97.74 49	92.85 130

SMA-MVS		90.40 31	90.57 40	89.87 47	95.31 27	79.64 68	90.98 34	93.36 37	75.21 140	92.90 39	95.28 41	86.29 48	96.09 6	87.92 11	97.89 44	93.88 103

Anonymous20231211		90.14 32	91.88 12	84.92 119	94.75 36	64.47 180	90.13 39	92.97 58	91.68 3	95.35 12	98.79 2	93.19 3	91.76 160	71.67 173	98.40 21	98.52 7

v7n		90.13 33	90.96 32	87.65 77	91.95 94	71.06 140	89.99 42	93.05 53	86.53 21	94.29 22	96.27 19	82.69 73	94.08 72	86.25 30	97.63 52	97.82 11

PMVS	80.48 6	90.08 34	90.66 36	88.34 66	96.71 2	92.97 2	90.31 37	89.57 162	88.51 15	90.11 83	95.12 47	90.98 8	88.92 222	77.55 126	97.07 68	83.13 288
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		90.06 35	91.92 9	84.47 133	96.56 6	58.83 251	89.04 63	92.74 67	91.40 5	96.12 4	96.06 24	87.23 35	95.57 24	79.42 112	98.74 7	99.00 2

ESAPD		90.05 36	90.56 41	88.50 63	93.86 48	77.77 80	89.63 51	93.93 25	84.39 28	92.84 43	93.43 98	87.19 36	96.26 4	82.18 75	97.61 55	91.48 173

PEN-MVS		90.03 37	91.88 12	84.48 132	96.57 5	58.88 250	88.95 64	93.19 48	91.62 4	96.01 6	96.16 22	87.02 38	95.60 23	78.69 116	98.72 10	98.97 3

OurMVSNet-221017-0		90.01 38	89.74 48	90.83 33	93.16 62	80.37 59	91.91 27	93.11 50	81.10 62	95.32 13	97.24 6	72.94 191	94.85 51	85.07 37	97.78 47	97.26 23

DTE-MVSNet		89.98 39	91.91 11	84.21 141	96.51 7	57.84 254	88.93 66	92.84 64	91.92 2	96.16 3	96.23 20	86.95 39	95.99 7	79.05 113	98.57 16	98.80 6

XVG-ACMP-BASELINE		89.98 39	89.84 46	90.41 39	94.91 34	84.50 41	89.49 58	93.98 24	79.68 75	92.09 57	93.89 92	83.80 62	93.10 126	82.67 70	98.04 36	93.64 113

v52		89.97 41	90.60 37	88.07 70	88.69 156	72.01 128	91.35 30	92.64 70	82.22 50	95.97 8	96.31 16	84.82 54	93.98 76	88.59 4	94.83 143	98.23 8

V4		89.97 41	90.60 37	88.07 70	88.69 156	72.01 128	91.35 30	92.64 70	82.22 50	95.98 7	96.31 16	84.80 56	93.98 76	88.59 4	94.83 143	98.23 8

3Dnovator+	83.92 2	89.97 41	89.66 49	90.92 32	91.27 113	81.66 54	91.25 32	94.13 22	88.89 12	88.83 119	94.26 75	77.55 125	95.86 15	84.88 40	95.87 111	95.24 71

WR-MVS_H		89.91 44	91.31 25	85.71 110	96.32 9	62.39 213	89.54 56	93.31 42	90.21 10	95.57 11	95.66 31	81.42 94	95.90 12	80.94 88	98.80 4	98.84 5

OPM-MVS		89.80 45	89.97 44	89.27 55	94.76 35	79.86 64	86.76 100	92.78 66	78.78 89	92.51 51	93.64 95	88.13 27	93.84 83	84.83 42	97.55 58	94.10 97

mvs_tets		89.78 46	89.27 53	91.30 24	93.51 53	84.79 36	89.89 45	90.63 128	70.00 207	94.55 18	96.67 11	87.94 29	93.59 94	84.27 49	95.97 106	95.52 65

anonymousdsp		89.73 47	88.88 58	92.27 7	89.82 141	86.67 12	90.51 36	90.20 149	69.87 208	95.06 14	96.14 23	84.28 59	93.07 130	87.68 14	96.34 90	97.09 30

test_djsdf		89.62 48	89.01 55	91.45 21	92.36 82	82.98 49	91.98 25	90.08 150	71.54 194	94.28 23	96.54 13	81.57 92	94.27 62	86.26 28	96.49 86	97.09 30

XVG-OURS-SEG-HR		89.59 49	89.37 52	90.28 42	94.47 40	85.95 21	86.84 96	93.91 27	80.07 72	86.75 150	93.26 100	93.64 2	90.93 179	84.60 45	90.75 233	93.97 100

APD-MVS		89.54 50	89.63 50	89.26 56	92.57 75	81.34 56	90.19 38	93.08 52	80.87 64	91.13 72	93.19 101	86.22 49	95.97 9	82.23 74	97.18 66	90.45 199
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 51	88.81 60	91.19 28	93.38 57	84.72 37	89.70 47	90.29 144	69.27 211	94.39 20	96.38 15	86.02 51	93.52 104	83.96 52	95.92 109	95.34 68

CPTT-MVS		89.39 52	88.98 57	90.63 36	95.09 30	86.95 10	92.09 23	92.30 78	79.74 74	87.50 139	92.38 121	81.42 94	93.28 118	83.07 63	97.24 64	91.67 167

ACMH	76.49 14	89.34 53	91.14 28	83.96 147	92.50 78	70.36 144	89.55 54	93.84 29	81.89 56	94.70 16	95.44 39	90.69 10	88.31 233	83.33 59	98.30 28	93.20 124
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		89.27 54	90.91 34	84.37 136	96.34 8	58.61 253	88.66 71	92.06 83	90.78 6	95.67 9	95.17 45	81.80 90	95.54 27	79.00 114	98.69 11	98.95 4

XVG-OURS		89.18 55	88.83 59	90.23 43	94.28 42	86.11 20	85.91 112	93.60 34	80.16 71	89.13 115	93.44 97	83.82 61	90.98 177	83.86 55	95.30 128	93.60 115

DeepC-MVS	82.31 4	89.15 56	89.08 54	89.37 54	93.64 52	79.07 71	88.54 72	94.20 17	73.53 155	89.71 98	94.82 55	85.09 53	95.77 18	84.17 51	98.03 38	93.26 122
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		88.96 57	89.88 45	86.22 97	91.63 100	77.07 90	89.82 46	93.77 30	78.90 87	92.88 40	92.29 125	86.11 50	90.22 199	86.24 31	97.24 64	91.36 176

HPM-MVS++		88.93 58	88.45 64	90.38 40	94.92 33	85.85 25	89.70 47	91.27 116	78.20 97	86.69 151	92.28 126	80.36 103	95.06 46	86.17 32	96.49 86	90.22 203

v748		88.91 59	89.82 47	86.19 101	90.06 137	68.53 156	88.81 68	91.48 100	84.36 30	94.19 24	95.98 25	82.52 76	92.67 140	84.30 48	96.67 79	97.37 20

test_0402		88.65 60	89.58 51	85.88 107	92.55 76	72.22 126	84.01 140	89.44 164	88.63 14	94.38 21	95.77 28	86.38 47	93.59 94	79.84 105	95.21 129	91.82 164

HSP-MVS		88.63 61	87.84 69	91.02 29	95.76 16	86.14 19	92.75 13	91.01 123	78.43 94	89.16 114	92.25 127	72.03 206	96.36 2	88.21 9	90.93 228	90.55 197

DP-MVS		88.60 62	89.01 55	87.36 82	91.30 111	77.50 85	87.55 84	92.97 58	87.95 17	89.62 104	92.87 110	84.56 57	93.89 80	77.65 125	96.62 80	90.70 191

wuykxyi23d		88.46 63	88.80 61	87.44 81	90.96 121	93.03 1	85.85 114	81.96 238	74.58 145	98.58 2	97.29 5	87.73 31	87.31 241	82.84 68	99.41 1	81.99 301

PS-MVSNAJss		88.31 64	87.90 68	89.56 52	93.31 58	77.96 79	87.94 78	91.97 86	70.73 200	94.19 24	96.67 11	76.94 133	94.57 58	83.07 63	96.28 92	96.15 42

OMC-MVS		88.19 65	87.52 74	90.19 44	91.94 96	81.68 53	87.49 86	93.17 49	76.02 127	88.64 122	91.22 148	84.24 60	93.37 113	77.97 124	97.03 69	95.52 65

TSAR-MVS + MP.		88.14 66	87.82 70	89.09 58	95.72 19	76.74 94	92.49 20	91.19 119	67.85 226	86.63 152	94.84 54	79.58 109	95.96 10	87.62 15	94.50 153	94.56 81

RPSCF		88.00 67	86.93 86	91.22 27	90.08 135	89.30 6	89.68 49	91.11 120	79.26 81	89.68 99	94.81 58	82.44 77	87.74 238	76.54 136	88.74 255	96.61 38

AllTest		87.97 68	87.40 78	89.68 48	91.59 101	83.40 45	89.50 57	95.44 5	79.47 77	88.00 132	93.03 104	82.66 74	91.47 165	70.81 175	96.14 99	94.16 95

TranMVSNet+NR-MVSNet		87.86 69	88.76 62	85.18 116	94.02 44	64.13 182	84.38 134	91.29 115	84.88 27	92.06 58	93.84 93	86.45 45	93.73 84	73.22 159	98.66 12	97.69 12

nrg030		87.85 70	88.49 63	85.91 105	90.07 136	69.73 146	87.86 79	94.20 17	74.04 150	92.70 48	94.66 59	85.88 52	91.50 164	79.72 106	97.32 62	96.50 40

CNVR-MVS		87.81 71	87.68 73	88.21 67	92.87 69	77.30 89	85.25 121	91.23 117	77.31 113	87.07 146	91.47 144	82.94 71	94.71 54	84.67 44	96.27 94	92.62 143

HQP_MVS		87.75 72	87.43 77	88.70 61	93.45 54	76.42 98	89.45 59	93.61 32	79.44 79	86.55 153	92.95 108	74.84 154	95.22 40	80.78 91	95.83 113	94.46 86

NCCC		87.36 73	86.87 87	88.83 59	92.32 85	78.84 74	86.58 107	91.09 121	78.77 90	84.85 178	90.89 166	80.85 98	95.29 36	81.14 85	95.32 125	92.34 151

v13		87.31 74	88.10 65	84.94 118	88.84 153	63.75 186	87.85 80	91.47 103	79.12 83	93.72 29	95.82 27	75.20 148	93.58 97	84.76 43	96.16 97	97.48 17

DeepPCF-MVS	81.24 5	87.28 75	86.21 98	90.49 38	91.48 108	84.90 34	83.41 163	92.38 77	70.25 205	89.35 112	90.68 173	82.85 72	94.57 58	79.55 108	95.95 107	92.00 160

SixPastTwentyTwo		87.20 76	87.45 76	86.45 91	92.52 77	69.19 154	87.84 81	88.05 181	81.66 58	94.64 17	96.53 14	65.94 229	94.75 53	83.02 65	96.83 75	95.41 67

v12		87.15 77	87.91 67	84.84 121	88.69 156	63.52 189	87.58 83	91.46 104	78.74 91	93.57 32	95.66 31	74.94 152	93.57 98	84.50 46	96.08 102	97.43 18

v11		86.96 78	87.78 71	84.51 130	88.50 162	62.60 209	87.21 89	91.63 95	78.08 100	93.40 34	95.56 36	75.07 149	93.57 98	84.46 47	96.08 102	97.36 21

V9		86.96 78	87.70 72	84.74 125	88.52 161	63.27 195	87.31 88	91.45 106	78.28 96	93.43 33	95.45 38	74.59 160	93.57 98	84.23 50	96.01 105	97.38 19

UniMVSNet (Re)		86.87 80	86.98 84	86.55 89	93.11 64	68.48 157	83.80 149	92.87 61	80.37 67	89.61 106	91.81 136	77.72 122	94.18 67	75.00 145	98.53 17	96.99 34

Vis-MVSNet		86.86 81	86.58 91	87.72 75	92.09 90	77.43 86	87.35 87	92.09 82	78.87 88	84.27 194	94.05 82	78.35 117	93.65 87	80.54 95	91.58 210	92.08 159
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 82	87.06 81	86.17 102	92.86 71	67.02 165	82.55 186	91.56 96	83.08 41	90.92 75	91.82 135	78.25 118	93.99 74	74.16 148	98.35 24	97.49 16

DU-MVS		86.80 83	86.99 83	86.21 99	93.24 60	67.02 165	83.16 171	92.21 79	81.73 57	90.92 75	91.97 129	77.20 127	93.99 74	74.16 148	98.35 24	97.61 13

V14		86.75 84	87.46 75	84.62 128	88.35 165	63.00 200	87.02 94	91.42 109	77.78 102	93.27 35	95.23 44	74.22 163	93.56 101	83.95 53	95.93 108	97.31 22

Regformer-2		86.74 85	86.08 100	88.73 60	84.18 261	79.20 70	83.52 158	89.33 165	83.33 37	89.92 93	85.07 260	83.23 69	93.16 123	83.39 58	92.72 194	93.83 104

IS-MVSNet		86.66 86	86.82 90	86.17 102	92.05 92	66.87 167	91.21 33	88.64 172	86.30 23	89.60 107	92.59 116	69.22 216	94.91 50	73.89 152	97.89 44	96.72 35

v15		86.56 87	87.25 79	84.51 130	88.15 172	62.72 205	86.72 104	91.40 111	77.38 107	93.11 37	95.00 49	73.93 168	93.55 102	83.67 57	95.86 112	97.26 23

v10		86.54 88	87.10 80	84.84 121	88.16 171	63.28 194	86.64 106	92.20 80	75.42 139	92.81 45	94.50 65	74.05 166	94.06 73	83.88 54	96.28 92	97.17 28

pmmvs6		86.52 89	88.06 66	81.90 187	92.22 88	62.28 219	84.66 129	89.15 167	83.54 36	89.85 94	97.32 4	88.08 28	86.80 254	70.43 182	97.30 63	96.62 37

Regformer-4		86.41 90	85.71 105	88.52 62	84.27 257	77.57 84	84.07 138	88.00 183	82.82 44	89.84 95	85.48 250	82.06 82	92.77 137	83.83 56	91.04 221	95.22 74

PHI-MVS		86.38 91	85.81 103	88.08 69	88.44 164	77.34 87	89.35 61	93.05 53	73.15 166	84.76 179	87.70 224	78.87 113	94.18 67	80.67 93	96.29 91	92.73 134

v17		86.32 92	86.95 85	84.44 134	88.00 174	62.62 208	86.74 102	91.48 100	77.17 115	92.74 46	94.56 61	73.74 172	93.53 103	83.27 60	94.87 142	97.18 27

test_prior3		86.31 93	86.31 95	86.32 93	90.59 128	71.99 130	83.37 164	92.85 62	75.43 137	84.58 185	91.57 140	81.92 88	94.17 69	79.54 109	96.97 70	92.80 132

CSCG		86.26 94	86.47 93	85.60 112	90.87 123	74.26 110	87.98 77	91.85 89	80.35 68	89.54 110	88.01 218	79.09 111	92.13 149	75.51 140	95.06 134	90.41 200

v16		86.24 95	86.85 88	84.43 135	87.96 176	62.59 210	86.73 103	91.48 100	77.17 115	92.67 49	94.55 62	73.63 173	93.52 104	83.26 61	94.16 157	97.17 28

DeepC-MVS_fast	80.27 8	86.23 96	85.65 107	87.96 74	91.30 111	76.92 91	87.19 90	91.99 85	70.56 201	84.96 174	90.69 172	80.01 106	95.14 43	78.37 117	95.78 115	91.82 164
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 97	86.83 89	84.36 137	87.82 182	62.35 214	86.42 109	91.33 114	76.78 120	92.73 47	94.48 66	73.41 180	93.72 85	83.10 62	95.41 122	97.01 33

CDPH-MVS		86.17 98	85.54 108	88.05 73	92.25 86	75.45 103	83.85 146	92.01 84	65.91 238	86.19 160	91.75 138	83.77 63	94.98 48	77.43 129	96.71 78	93.73 110

Regformer-1		86.00 99	85.50 109	87.49 79	84.18 261	76.90 92	83.52 158	87.94 185	82.18 52	89.19 113	85.07 260	82.28 80	91.89 155	82.40 72	92.72 194	93.69 112

NR-MVSNet		86.00 99	86.22 97	85.34 114	93.24 60	64.56 179	82.21 197	90.46 132	80.99 63	88.42 127	91.97 129	77.56 124	93.85 81	72.46 167	98.65 13	97.61 13

v18		85.99 101	86.55 92	84.30 139	87.73 188	62.29 218	86.40 110	91.49 99	76.64 121	92.40 54	94.20 78	73.28 184	93.52 104	82.87 66	93.99 161	97.09 30

train_agg		85.98 102	85.28 112	88.07 70	92.34 83	79.70 66	83.94 142	90.32 137	65.79 239	84.49 187	90.97 162	81.93 86	93.63 89	81.21 83	96.54 83	90.88 186

FC-MVSNet-test		85.93 103	87.05 82	82.58 178	92.25 86	56.44 264	85.75 115	93.09 51	77.33 112	91.94 62	94.65 60	74.78 156	93.41 112	75.11 143	98.58 15	97.88 10

Effi-MVS+-dtu		85.82 104	83.38 153	93.14 3	87.13 207	91.15 3	87.70 82	88.42 174	74.57 146	83.56 201	85.65 247	78.49 115	94.21 66	72.04 170	92.88 191	94.05 98

agg_prior3		85.76 105	84.95 118	88.16 68	92.43 80	79.92 62	83.98 141	90.03 152	65.11 248	83.66 199	90.64 177	81.00 97	93.67 86	81.21 83	96.54 83	90.88 186

agg_prior1		85.72 106	85.20 113	87.28 83	91.58 104	77.69 82	83.69 153	90.30 141	66.29 234	84.32 191	91.07 159	82.13 81	93.18 121	81.02 86	96.36 89	90.98 181

TAPA-MVS	77.73 12	85.71 107	84.83 120	88.37 65	88.78 155	79.72 65	87.15 92	93.50 35	69.17 213	85.80 166	89.56 195	80.76 99	92.13 149	73.21 163	95.51 120	93.25 123
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		85.50 108	86.14 99	83.58 159	87.97 175	67.13 164	87.55 84	94.32 12	73.44 157	88.47 126	87.54 227	86.45 45	91.06 176	75.76 139	93.76 168	92.54 145

EPP-MVSNet		85.47 109	85.04 115	86.77 86	91.52 107	69.37 149	91.63 28	87.98 184	81.51 60	87.05 147	91.83 134	66.18 228	95.29 36	70.75 177	96.89 72	95.64 59

FIs		85.35 110	86.27 96	82.60 177	91.86 97	57.31 258	85.10 123	93.05 53	75.83 130	91.02 74	93.97 85	73.57 177	92.91 135	73.97 151	98.02 39	97.58 15

K. test v3		85.14 111	84.73 121	86.37 92	91.13 118	69.63 148	85.45 119	76.68 265	84.06 34	92.44 53	96.99 8	62.03 241	94.65 55	80.58 94	93.24 184	94.83 80

EI-MVSNet-Vis-set		85.12 112	84.53 133	86.88 84	84.01 263	72.76 118	83.91 145	85.18 219	80.44 66	88.75 120	85.49 249	80.08 105	91.92 153	82.02 77	90.85 231	95.97 49

Regformer-3		85.06 113	84.67 126	86.22 97	84.27 257	73.43 114	84.07 138	85.26 217	80.77 65	88.62 123	85.48 250	80.56 102	90.39 195	81.99 78	91.04 221	94.85 79

EI-MVSNet-UG-set		85.04 114	84.44 135	86.85 85	83.87 266	72.52 121	83.82 147	85.15 220	80.27 70	88.75 120	85.45 253	79.95 107	91.90 154	81.92 79	90.80 232	96.13 43

X-MVStestdata		85.04 114	82.70 161	92.08 8	95.64 21	86.25 16	92.64 15	93.33 40	85.07 25	89.99 87	16.05 353	86.57 43	95.80 16	87.35 20	97.62 53	94.20 92

MSLP-MVS++		85.00 116	86.03 101	81.90 187	91.84 98	71.56 138	86.75 101	93.02 57	75.95 128	87.12 143	89.39 197	77.98 119	89.40 213	77.46 127	94.78 145	84.75 264

F-COLMAP		84.97 117	83.42 152	89.63 50	92.39 81	83.40 45	88.83 67	91.92 88	73.19 165	80.18 250	89.15 200	77.04 131	93.28 118	65.82 218	92.28 200	92.21 157

MVS_0304		84.88 118	83.96 148	87.64 78	87.43 196	74.83 106	84.18 136	93.30 43	77.48 106	77.39 267	88.46 209	74.53 162	95.74 19	78.09 123	94.75 149	92.36 150

v7		84.81 119	85.00 116	84.23 140	88.15 172	63.27 195	83.79 150	91.39 112	71.10 198	90.07 84	91.28 146	74.04 167	93.63 89	81.48 82	93.67 171	95.79 52

3Dnovator	80.37 7	84.80 120	84.71 124	85.06 117	86.36 222	74.71 107	88.77 69	90.00 153	75.65 135	84.96 174	93.17 102	74.06 165	91.19 172	78.28 120	91.09 219	89.29 215

IterMVS-LS		84.73 121	84.98 117	83.96 147	87.35 197	63.66 187	83.25 168	89.88 155	76.06 125	89.62 104	92.37 124	73.40 182	92.52 143	78.16 121	94.77 147	95.69 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 122	84.34 142	85.49 113	90.18 134	75.86 102	79.23 255	87.13 197	73.35 158	85.56 170	89.34 198	83.60 65	90.50 193	76.64 135	94.05 160	90.09 208

HQP-MVS		84.61 123	84.06 145	86.27 95	91.19 114	70.66 142	84.77 125	92.68 68	73.30 161	80.55 245	90.17 186	72.10 202	94.61 56	77.30 130	94.47 154	93.56 117

v1192		84.57 124	84.69 125	84.21 141	87.75 187	62.88 202	83.02 173	91.43 107	69.08 215	89.98 89	90.89 166	72.70 196	93.62 93	82.41 71	94.97 137	96.13 43

mvs-test1		84.55 125	82.12 170	91.84 17	87.13 207	89.54 5	85.05 124	88.42 174	74.57 146	80.60 242	82.98 284	78.49 115	93.98 76	72.04 170	89.77 244	92.00 160

FMVSNet1		84.55 125	85.45 110	81.85 190	90.27 133	61.05 234	86.83 97	88.27 178	78.57 93	89.66 100	95.64 33	75.43 145	90.68 188	69.09 192	95.33 124	93.82 106

v1144		84.54 127	84.72 123	84.00 145	87.67 190	62.55 211	82.97 174	90.93 124	70.32 204	89.80 96	90.99 161	73.50 178	93.48 108	81.69 81	94.65 151	95.97 49

Gipuma		84.44 128	86.33 94	78.78 224	84.20 260	73.57 113	89.55 54	90.44 133	84.24 31	84.38 189	94.89 52	76.35 142	80.40 299	76.14 137	96.80 77	82.36 296
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1neww		84.43 129	84.66 127	83.75 152	87.81 183	62.34 215	83.59 154	90.27 145	72.33 177	89.93 91	91.22 148	73.28 184	93.29 115	80.25 100	93.25 182	95.62 60

v7new		84.43 129	84.66 127	83.75 152	87.81 183	62.34 215	83.59 154	90.27 145	72.33 177	89.93 91	91.22 148	73.28 184	93.29 115	80.25 100	93.25 182	95.62 60

v6		84.43 129	84.66 127	83.75 152	87.81 183	62.34 215	83.59 154	90.26 147	72.33 177	89.94 90	91.19 152	73.30 183	93.29 115	80.26 99	93.26 181	95.62 60

testing_2		84.36 132	84.64 130	83.50 165	86.74 216	63.97 185	84.56 131	90.31 139	66.22 235	91.62 66	94.55 62	75.88 143	91.95 152	77.02 134	94.89 139	94.56 81

MCST-MVS		84.36 132	83.93 149	85.63 111	91.59 101	71.58 137	83.52 158	92.13 81	61.82 269	83.96 195	89.75 192	79.93 108	93.46 109	78.33 119	94.34 156	91.87 163

VDDNet		84.35 134	85.39 111	81.25 199	95.13 29	59.32 247	85.42 120	81.11 244	86.41 22	87.41 140	96.21 21	73.61 176	90.61 191	66.33 212	96.85 73	93.81 109

v1240		84.30 135	84.51 134	83.65 157	87.65 191	61.26 231	82.85 177	91.54 97	67.94 224	90.68 80	90.65 175	71.71 208	93.64 88	82.84 68	94.78 145	96.07 45

MVS_111021_LR		84.28 136	83.76 150	85.83 109	89.23 147	83.07 48	80.99 224	83.56 229	72.71 171	86.07 161	89.07 201	81.75 91	86.19 263	77.11 132	93.36 177	88.24 223

v144192		84.24 137	84.41 136	83.71 156	87.59 193	61.57 227	82.95 175	91.03 122	67.82 227	89.80 96	90.49 178	73.28 184	93.51 107	81.88 80	94.89 139	96.04 47

v1921920		84.23 138	84.37 141	83.79 150	87.64 192	61.71 222	82.91 176	91.20 118	67.94 224	90.06 85	90.34 180	72.04 205	93.59 94	82.32 73	94.91 138	96.07 45

VDD-MVS		84.23 138	84.58 132	83.20 168	91.17 117	65.16 176	83.25 168	84.97 225	79.79 73	87.18 142	94.27 74	74.77 157	90.89 182	69.24 189	96.54 83	93.55 119

v1141		84.16 140	84.38 138	83.52 162	87.32 199	61.70 224	82.79 179	89.74 156	71.90 191	89.64 101	91.12 155	72.68 197	93.10 126	80.39 98	93.80 166	95.75 54

divwei89l23v2f112		84.16 140	84.38 138	83.52 162	87.32 199	61.70 224	82.79 179	89.74 156	71.90 191	89.64 101	91.12 155	72.68 197	93.10 126	80.40 96	93.81 165	95.75 54

v1		84.16 140	84.38 138	83.52 162	87.33 198	61.71 222	82.79 179	89.73 158	71.89 193	89.64 101	91.11 157	72.72 194	93.10 126	80.40 96	93.79 167	95.75 54

v2v482		84.09 143	84.24 143	83.62 158	87.13 207	61.40 228	82.71 183	89.71 159	72.19 181	89.55 108	91.41 145	70.70 213	93.20 120	81.02 86	93.76 168	96.25 41

EG-PatchMatch MVS		84.08 144	84.11 144	83.98 146	92.22 88	72.61 120	82.20 199	87.02 201	72.63 172	88.86 117	91.02 160	78.52 114	91.11 174	73.41 158	91.09 219	88.21 224

DP-MVS Recon		84.05 145	83.22 155	86.52 90	91.73 99	75.27 104	83.23 170	92.40 75	72.04 182	82.04 217	88.33 214	77.91 121	93.95 79	66.17 213	95.12 132	90.34 202

TransMVSNet (Re)		84.02 146	85.74 104	78.85 223	91.00 120	55.20 274	82.29 193	87.26 192	79.65 76	88.38 129	95.52 37	83.00 70	86.88 246	67.97 202	96.60 81	94.45 88

Baseline_NR-MVSNet		84.00 147	85.90 102	78.29 232	91.47 109	53.44 283	82.29 193	87.00 202	79.06 85	89.55 108	95.72 30	77.20 127	86.14 264	72.30 168	98.51 18	95.28 70

TSAR-MVS + GP.		83.95 148	82.69 162	87.72 75	89.27 146	81.45 55	83.72 152	81.58 243	74.73 144	85.66 167	86.06 245	72.56 200	92.69 139	75.44 141	95.21 129	89.01 220

alignmvs		83.94 149	83.98 147	83.80 149	87.80 186	67.88 161	84.54 132	91.42 109	73.27 164	88.41 128	87.96 219	72.33 201	90.83 183	76.02 138	94.11 158	92.69 136

Effi-MVS+		83.90 150	84.01 146	83.57 160	87.22 205	65.61 174	86.55 108	92.40 75	78.64 92	81.34 229	84.18 271	83.65 64	92.93 133	74.22 147	87.87 265	92.17 158

CANet		83.79 151	82.85 160	86.63 87	86.17 229	72.21 127	83.76 151	91.43 107	77.24 114	74.39 290	87.45 228	75.36 146	95.42 32	77.03 133	92.83 192	92.25 156

pm-mvs1		83.69 152	84.95 118	79.91 211	90.04 139	59.66 244	82.43 188	87.44 189	75.52 136	87.85 134	95.26 43	81.25 96	85.65 270	68.74 196	96.04 104	94.42 89

AdaColmap		83.66 153	83.69 151	83.57 160	90.05 138	72.26 125	86.29 111	90.00 153	78.19 98	81.65 224	87.16 230	83.40 67	94.24 65	61.69 238	94.76 148	84.21 271

MIMVSNet1		83.63 154	84.59 131	80.74 204	94.06 43	62.77 204	82.72 182	84.53 227	77.57 105	90.34 81	95.92 26	76.88 139	85.83 268	61.88 236	97.42 60	93.62 114

WR-MVS		83.56 155	84.40 137	81.06 202	93.43 56	54.88 275	78.67 261	85.02 223	81.24 61	90.74 79	91.56 142	72.85 192	91.08 175	68.00 201	98.04 36	97.23 25

CNLPA		83.55 156	83.10 158	84.90 120	89.34 145	83.87 43	84.54 132	88.77 169	79.09 84	83.54 202	88.66 207	74.87 153	81.73 295	66.84 209	92.29 199	89.11 216

LCM-MVSNet-Re		83.48 157	85.06 114	78.75 225	85.94 237	55.75 269	80.05 232	94.27 13	76.47 122	96.09 5	94.54 64	83.31 68	89.75 208	59.95 250	94.89 139	90.75 190

V42		83.47 158	83.37 154	83.75 152	83.16 272	63.33 193	81.31 216	90.23 148	69.51 210	90.91 77	90.81 169	74.16 164	92.29 147	80.06 102	90.22 241	95.62 60

VPA-MVSNet		83.47 158	84.73 121	79.69 215	90.29 132	57.52 257	81.30 218	88.69 171	76.29 123	87.58 137	94.44 67	80.60 101	87.20 242	66.60 211	96.82 76	94.34 91

PAPM_NR		83.23 160	83.19 157	83.33 166	90.90 122	65.98 171	88.19 75	90.78 125	78.13 99	80.87 233	87.92 222	73.49 179	92.42 144	70.07 183	88.40 256	91.60 169

CLD-MVS		83.18 161	82.64 163	84.79 123	89.05 149	67.82 162	77.93 266	92.52 73	68.33 219	85.07 173	81.54 307	82.06 82	92.96 131	69.35 188	97.91 43	93.57 116
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 162	85.68 106	75.65 266	81.24 284	45.26 333	79.94 235	92.91 60	83.83 35	91.33 71	96.88 10	80.25 104	85.92 266	68.89 194	95.89 110	95.76 53

114514_t		83.10 163	82.54 166	84.77 124	92.90 68	69.10 155	86.65 105	90.62 129	54.66 300	81.46 226	90.81 169	76.98 132	94.38 61	72.62 166	96.18 96	90.82 189

UGNet		82.78 164	81.64 176	86.21 99	86.20 228	76.24 101	86.86 95	85.68 213	77.07 117	73.76 293	92.82 111	69.64 214	91.82 158	69.04 193	93.69 170	90.56 196
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		82.75 165	81.93 174	85.19 115	82.08 277	80.15 61	85.53 118	88.76 170	68.01 221	85.58 169	87.75 223	71.80 207	86.85 247	74.02 150	93.87 164	88.58 222

EI-MVSNet		82.61 166	82.42 168	83.20 168	83.25 270	63.66 187	83.50 161	85.07 221	76.06 125	86.55 153	85.10 258	73.41 180	90.25 196	78.15 122	90.67 235	95.68 58

QAPM		82.59 167	82.59 165	82.58 178	86.44 217	66.69 168	89.94 44	90.36 136	67.97 223	84.94 176	92.58 118	72.71 195	92.18 148	70.63 180	87.73 267	88.85 221

Fast-Effi-MVS+-dtu		82.54 168	81.41 179	85.90 106	85.60 238	76.53 97	83.07 172	89.62 161	73.02 168	79.11 257	83.51 277	80.74 100	90.24 198	68.76 195	89.29 247	90.94 183

MVS_Test		82.47 169	83.22 155	80.22 209	82.62 276	57.75 256	82.54 187	91.96 87	71.16 197	82.89 210	92.52 120	77.41 126	90.50 193	80.04 103	87.84 266	92.40 148

v148		82.31 170	82.48 167	81.81 193	85.59 239	59.66 244	81.47 214	86.02 210	72.85 169	88.05 131	90.65 175	70.73 212	90.91 181	75.15 142	91.79 206	94.87 77

API-MVS		82.28 171	82.61 164	81.30 197	86.29 224	69.79 145	88.71 70	87.67 187	78.42 95	82.15 216	84.15 273	77.98 119	91.59 163	65.39 219	92.75 193	82.51 295

MVSFormer		82.23 172	81.57 178	84.19 143	85.54 240	69.26 151	91.98 25	90.08 150	71.54 194	76.23 274	85.07 260	58.69 259	94.27 62	86.26 28	88.77 253	89.03 218

PCF-MVS	74.62 15	82.15 173	80.92 186	85.84 108	89.43 143	72.30 124	80.53 228	91.82 90	57.36 288	87.81 135	89.92 189	77.67 123	93.63 89	58.69 262	95.08 133	91.58 170
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 174	80.31 191	87.45 80	90.86 124	80.29 60	85.88 113	90.65 127	68.17 220	76.32 273	86.33 241	73.12 189	92.61 142	61.40 242	90.02 243	89.44 211
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		82.02 175	82.07 171	81.85 190	86.38 219	61.05 234	86.83 97	88.27 178	72.43 173	86.00 162	95.64 33	63.78 236	90.68 188	65.95 214	93.34 178	93.82 106

test1		82.02 175	82.07 171	81.85 190	86.38 219	61.05 234	86.83 97	88.27 178	72.43 173	86.00 162	95.64 33	63.78 236	90.68 188	65.95 214	93.34 178	93.82 106

OpenMVS	76.72 13	81.98 177	82.00 173	81.93 186	84.42 255	68.22 159	88.50 73	89.48 163	66.92 230	81.80 223	91.86 131	72.59 199	90.16 201	71.19 174	91.25 217	87.40 235

tfpnnormal		81.79 178	82.95 159	78.31 231	88.93 152	55.40 270	80.83 227	82.85 233	76.81 119	85.90 165	94.14 80	74.58 161	86.51 259	66.82 210	95.68 119	93.01 128

PVSNet_Blended_VisFu		81.55 179	80.49 190	84.70 127	91.58 104	73.24 116	84.21 135	91.67 94	62.86 261	80.94 231	87.16 230	67.27 223	92.87 136	69.82 185	88.94 252	87.99 228

DELS-MVS		81.44 180	81.25 181	82.03 185	84.27 257	62.87 203	76.47 280	92.49 74	70.97 199	81.64 225	83.83 274	75.03 150	92.70 138	74.29 146	92.22 204	90.51 198
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
Test4		81.31 181	81.13 183	81.88 189	84.89 247	63.05 199	82.37 190	90.50 131	62.75 262	89.00 116	88.29 215	67.55 222	91.68 161	73.55 156	91.24 218	90.89 185

FMVSNet2		81.31 181	81.61 177	80.41 207	86.38 219	58.75 252	83.93 144	86.58 205	72.43 173	87.65 136	92.98 106	63.78 236	90.22 199	66.86 207	93.92 163	92.27 154

DI_MVS_plusplus_test		81.27 183	81.26 180	81.29 198	84.98 245	61.65 226	81.98 202	87.25 193	63.56 254	87.56 138	89.60 194	73.62 174	91.83 157	72.20 169	90.59 239	90.38 201

TinyColmap		81.25 184	82.34 169	77.99 237	85.33 243	60.68 238	82.32 192	88.33 176	71.26 196	86.97 148	92.22 128	77.10 130	86.98 245	62.37 233	95.17 131	86.31 245

test_normal		81.23 185	81.16 182	81.43 196	84.77 250	61.99 221	81.46 215	86.95 203	63.16 259	87.22 141	89.63 193	73.62 174	91.65 162	72.92 164	90.70 234	90.65 194

Fast-Effi-MVS+		81.04 186	80.57 187	82.46 182	87.50 194	63.22 197	78.37 262	89.63 160	68.01 221	81.87 219	82.08 302	82.31 79	92.65 141	67.10 205	88.30 261	91.51 172

BH-untuned		80.96 187	80.99 184	80.84 203	88.55 160	68.23 158	80.33 230	88.46 173	72.79 170	86.55 153	86.76 234	74.72 158	91.77 159	61.79 237	88.99 251	82.52 294

1121		80.86 188	79.81 198	84.02 144	93.93 46	78.70 75	81.64 211	80.18 249	55.43 297	83.67 198	91.15 153	71.29 210	91.41 169	67.95 203	93.06 187	81.96 302

xiu_mvs_v1_base_debu		80.84 189	80.14 194	82.93 173	88.31 166	71.73 133	79.53 239	87.17 194	65.43 243	79.59 252	82.73 290	76.94 133	90.14 202	73.22 159	88.33 257	86.90 240

xiu_mvs_v1_base		80.84 189	80.14 194	82.93 173	88.31 166	71.73 133	79.53 239	87.17 194	65.43 243	79.59 252	82.73 290	76.94 133	90.14 202	73.22 159	88.33 257	86.90 240

xiu_mvs_v1_base_debi		80.84 189	80.14 194	82.93 173	88.31 166	71.73 133	79.53 239	87.17 194	65.43 243	79.59 252	82.73 290	76.94 133	90.14 202	73.22 159	88.33 257	86.90 240

BH-RMVSNet		80.53 192	80.22 193	81.49 195	87.19 206	66.21 170	77.79 268	86.23 207	74.21 149	83.69 197	88.50 208	73.25 188	90.75 185	63.18 232	87.90 264	87.52 233

EPNet		80.37 193	78.41 205	86.23 96	76.75 317	73.28 115	87.18 91	77.45 259	76.24 124	68.14 318	88.93 203	65.41 231	93.85 81	69.47 187	96.12 101	91.55 171
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		80.32 194	80.94 185	78.47 230	88.18 169	52.62 290	82.29 193	85.01 224	72.01 183	79.24 256	92.54 119	69.36 215	93.36 114	70.65 179	89.19 250	89.45 210

VPNet		80.25 195	81.68 175	75.94 264	92.46 79	47.98 328	76.70 276	81.67 242	73.45 156	84.87 177	92.82 111	74.66 159	86.51 259	61.66 239	96.85 73	93.33 120

MAR-MVS		80.24 196	78.74 203	84.73 126	86.87 215	78.18 77	85.75 115	87.81 186	65.67 242	77.84 262	78.50 321	73.79 171	90.53 192	61.59 241	90.87 230	85.49 254
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
PM-MVS		80.20 197	79.00 201	83.78 151	88.17 170	86.66 13	81.31 216	66.81 332	69.64 209	88.33 130	90.19 184	64.58 232	83.63 287	71.99 172	90.03 242	81.06 320

LFMVS		80.15 198	80.56 188	78.89 222	89.19 148	55.93 266	85.22 122	73.78 281	82.96 42	84.28 193	92.72 115	57.38 266	90.07 206	63.80 227	95.75 116	90.68 192

MSDG		80.06 199	79.99 197	80.25 208	83.91 265	68.04 160	77.51 271	89.19 166	77.65 103	81.94 218	83.45 279	76.37 141	86.31 262	63.31 231	86.59 277	86.41 243

ab-mvs		79.67 200	80.56 188	76.99 250	88.48 163	56.93 260	84.70 128	86.06 209	68.95 217	80.78 234	93.08 103	75.30 147	84.62 279	56.78 276	90.90 229	89.43 212

VNet		79.31 201	80.27 192	76.44 257	87.92 177	53.95 279	75.58 287	84.35 228	74.39 148	82.23 214	90.72 171	72.84 193	84.39 281	60.38 249	93.98 162	90.97 182

diffmvs		79.20 202	79.04 200	79.69 215	78.64 305	58.90 249	81.79 207	87.61 188	65.07 249	73.65 295	89.80 190	73.10 190	87.79 237	75.02 144	86.63 276	92.38 149

PAPR		78.84 203	78.10 206	81.07 201	85.17 244	60.22 241	82.21 197	90.57 130	62.51 264	75.32 283	84.61 267	74.99 151	92.30 146	59.48 260	88.04 263	90.68 192

PVSNet_BlendedMVS		78.80 204	77.84 207	81.65 194	84.43 253	63.41 190	79.49 242	90.44 133	61.70 272	75.43 281	87.07 232	69.11 217	91.44 167	60.68 247	92.24 202	90.11 207

FMVSNet3		78.80 204	78.55 204	79.57 218	82.89 274	56.89 262	81.76 208	85.77 212	69.04 216	86.00 162	90.44 179	51.75 284	90.09 205	65.95 214	93.34 178	91.72 166

pmmvs-eth3d		78.42 206	77.04 212	82.57 180	87.44 195	74.41 109	80.86 226	79.67 252	55.68 295	84.69 180	90.31 183	60.91 245	85.42 271	62.20 234	91.59 209	87.88 231

mvs_anonymous		78.13 207	78.76 202	76.23 261	79.24 299	50.31 315	78.69 260	84.82 226	61.60 273	83.09 209	92.82 111	73.89 170	87.01 243	68.33 200	86.41 279	91.37 175

TAMVS		78.08 208	76.36 221	83.23 167	90.62 127	72.87 117	79.08 256	80.01 251	61.72 271	81.35 228	86.92 233	63.96 235	88.78 227	50.61 306	93.01 189	88.04 227

Vis-MVSNet (Re-imp)		77.82 209	77.79 208	77.92 238	88.82 154	51.29 303	83.28 166	71.97 297	74.04 150	82.23 214	89.78 191	57.38 266	89.41 212	57.22 274	95.41 122	93.05 127

CANet_DTU		77.81 210	77.05 211	80.09 210	81.37 283	59.90 243	83.26 167	88.29 177	69.16 214	67.83 321	83.72 275	60.93 244	89.47 209	69.22 191	89.70 245	90.88 186

OpenMVS_ROB	70.19 17	77.77 211	77.46 209	78.71 226	84.39 256	61.15 232	81.18 220	82.52 234	62.45 266	83.34 203	87.37 229	66.20 227	88.66 230	64.69 222	85.02 292	86.32 244

MDA-MVSNet-bldmvs		77.47 212	76.90 213	79.16 221	79.03 301	64.59 178	66.58 324	75.67 269	73.15 166	88.86 117	88.99 202	66.94 224	81.23 296	64.71 221	88.22 262	91.64 168

jason		77.42 213	75.75 227	82.43 183	87.10 210	69.27 150	77.99 265	81.94 240	51.47 320	77.84 262	85.07 260	60.32 248	89.00 220	70.74 178	89.27 249	89.03 218
jason: jason.
CDS-MVSNet		77.32 214	75.40 230	83.06 170	89.00 151	72.48 122	77.90 267	82.17 237	60.81 276	78.94 258	83.49 278	59.30 256	88.76 228	54.64 292	92.37 198	87.93 230
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		77.19 215	76.75 214	78.52 229	87.01 212	61.30 230	75.55 288	87.12 199	61.24 275	74.45 289	78.79 320	77.20 127	90.93 179	64.62 224	84.80 296	83.32 284

MVSTER		77.09 216	75.70 228	81.25 199	75.27 331	61.08 233	77.49 272	85.07 221	60.78 277	86.55 153	88.68 206	43.14 326	90.25 196	73.69 154	90.67 235	92.42 146

PS-MVSNAJ		77.04 217	76.53 220	78.56 228	87.09 211	61.40 228	75.26 289	87.13 197	61.25 274	74.38 291	77.22 326	76.94 133	90.94 178	64.63 223	84.83 295	83.35 283

IterMVS		76.91 218	76.34 222	78.64 227	80.91 288	64.03 183	76.30 281	79.03 253	64.88 251	83.11 207	89.16 199	59.90 252	84.46 280	68.61 198	85.15 291	87.42 234
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
view600		76.79 219	76.54 216	77.56 242	87.91 178	50.77 309	81.92 203	71.35 304	77.38 107	84.62 181	88.40 210	45.18 316	89.26 215	58.58 263	93.49 173	92.66 137

view800		76.79 219	76.54 216	77.56 242	87.91 178	50.77 309	81.92 203	71.35 304	77.38 107	84.62 181	88.40 210	45.18 316	89.26 215	58.58 263	93.49 173	92.66 137

conf0.05thres1000		76.79 219	76.54 216	77.56 242	87.91 178	50.77 309	81.92 203	71.35 304	77.38 107	84.62 181	88.40 210	45.18 316	89.26 215	58.58 263	93.49 173	92.66 137

tfpn		76.79 219	76.54 216	77.56 242	87.91 178	50.77 309	81.92 203	71.35 304	77.38 107	84.62 181	88.40 210	45.18 316	89.26 215	58.58 263	93.49 173	92.66 137

TR-MVS		76.77 223	75.79 225	79.72 214	86.10 236	65.79 173	77.14 273	83.02 231	65.20 247	81.40 227	82.10 301	66.30 226	90.73 187	55.57 283	85.27 288	82.65 290

USDC		76.63 224	76.73 215	76.34 259	83.46 268	57.20 259	80.02 233	88.04 182	52.14 315	83.65 200	91.25 147	63.24 239	86.65 258	54.66 291	94.11 158	85.17 255

BH-w/o		76.57 225	76.07 224	78.10 235	86.88 214	65.92 172	77.63 269	86.33 206	65.69 241	80.89 232	79.95 315	68.97 219	90.74 186	53.01 297	85.25 289	77.62 325

Patchmtry		76.56 226	77.46 209	73.83 278	79.37 298	46.60 330	82.41 189	76.90 262	73.81 153	85.56 170	92.38 121	48.07 293	83.98 284	63.36 230	95.31 127	90.92 184

PVSNet_Blended		76.49 227	75.40 230	79.76 213	84.43 253	63.41 190	75.14 290	90.44 133	57.36 288	75.43 281	78.30 322	69.11 217	91.44 167	60.68 247	87.70 268	84.42 267

lupinMVS		76.37 228	74.46 239	82.09 184	85.54 240	69.26 151	76.79 274	80.77 247	50.68 327	76.23 274	82.82 288	58.69 259	88.94 221	69.85 184	88.77 253	88.07 225

cascas		76.29 229	74.81 235	80.72 206	84.47 252	62.94 201	73.89 299	87.34 190	55.94 294	75.16 285	76.53 329	63.97 234	91.16 173	65.00 220	90.97 227	88.06 226

RPMNet		76.06 230	75.79 225	76.85 253	79.58 294	62.64 206	82.58 184	71.75 301	74.80 143	75.72 279	92.59 116	48.69 291	84.07 282	73.48 157	82.91 306	83.85 275

tfpn111		76.03 231	75.53 229	77.53 246	87.27 201	51.88 295	81.07 221	73.26 286	75.68 132	83.25 204	86.37 238	45.54 306	89.38 214	55.07 288	92.26 201	91.34 177

thres600view7		75.97 232	75.35 232	77.85 240	87.01 212	51.84 299	80.45 229	73.26 286	75.20 141	83.10 208	86.31 243	45.54 306	89.05 219	55.03 289	92.24 202	92.66 137

GA-MVS		75.83 233	74.61 236	79.48 220	81.87 279	59.25 248	73.42 302	82.88 232	68.68 218	79.75 251	81.80 304	50.62 286	89.46 210	66.85 208	85.64 285	89.72 209

MVP-Stereo		75.81 234	73.51 253	82.71 176	89.35 144	73.62 112	80.06 231	85.20 218	60.30 279	73.96 292	87.94 220	57.89 263	89.45 211	52.02 300	74.87 333	85.06 257
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
conf200view11		75.62 235	75.05 233	77.34 248	87.27 201	51.88 295	81.07 221	73.26 286	75.68 132	83.25 204	86.37 238	45.54 306	88.80 223	51.98 301	90.99 223	91.34 177

thres100view900		75.45 236	75.05 233	76.66 256	87.27 201	51.88 295	81.07 221	73.26 286	75.68 132	83.25 204	86.37 238	45.54 306	88.80 223	51.98 301	90.99 223	89.31 213

thres400		75.14 237	74.23 241	77.86 239	86.24 225	52.12 292	79.24 252	73.87 279	73.34 159	81.82 221	84.60 268	46.02 300	88.80 223	51.98 301	90.99 223	92.66 137

wuyk23d		75.13 238	79.30 199	62.63 320	75.56 325	75.18 105	80.89 225	73.10 290	75.06 142	94.76 15	95.32 40	87.73 31	52.85 351	34.16 345	97.11 67	59.85 346

EU-MVSNet		75.12 239	74.43 240	77.18 249	83.11 273	59.48 246	85.71 117	82.43 235	39.76 348	85.64 168	88.76 204	44.71 322	87.88 236	73.86 153	85.88 283	84.16 272

HyFIR lowres test		75.12 239	72.66 260	82.50 181	91.44 110	65.19 175	72.47 304	87.31 191	46.79 336	80.29 248	84.30 270	52.70 282	92.10 151	51.88 305	86.73 275	90.22 203

CMPMVS	59.41 20	75.12 239	73.57 246	79.77 212	75.84 323	67.22 163	81.21 219	82.18 236	50.78 325	76.50 270	87.66 225	55.20 276	82.99 289	62.17 235	90.64 238	89.09 217
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 242	72.98 258	80.73 205	84.95 246	71.71 136	76.23 282	77.59 258	52.83 309	77.73 265	86.38 237	56.35 269	84.97 275	57.72 273	87.05 273	85.51 253

tfpn200view9		74.86 243	74.23 241	76.74 255	86.24 225	52.12 292	79.24 252	73.87 279	73.34 159	81.82 221	84.60 268	46.02 300	88.80 223	51.98 301	90.99 223	89.31 213

1112_ss		74.82 244	73.74 243	78.04 236	89.57 142	60.04 242	76.49 279	87.09 200	54.31 301	73.66 294	79.80 316	60.25 249	86.76 256	58.37 267	84.15 299	87.32 236

Patchmatch-RL test		74.48 245	73.68 244	76.89 252	84.83 248	66.54 169	72.29 305	69.16 316	57.70 286	86.76 149	86.33 241	45.79 305	82.59 291	69.63 186	90.65 237	81.54 310

PatchMatch-RL		74.48 245	73.22 255	78.27 233	87.70 189	85.26 30	75.92 283	70.09 311	64.34 253	76.09 276	81.25 309	65.87 230	78.07 304	53.86 294	83.82 300	71.48 336

XXY-MVS		74.44 247	76.19 223	69.21 300	84.61 251	52.43 291	71.70 307	77.18 260	60.73 278	80.60 242	90.96 164	75.44 144	69.35 325	56.13 279	88.33 257	85.86 250

conf0.01		74.17 248	73.53 247	76.08 262	86.13 230	50.06 318	79.45 243	68.54 317	72.01 183	80.76 235	82.50 293	41.39 330	86.83 248	59.66 253	91.36 211	91.34 177

conf0.002		74.17 248	73.53 247	76.08 262	86.13 230	50.06 318	79.45 243	68.54 317	72.01 183	80.76 235	82.50 293	41.39 330	86.83 248	59.66 253	91.36 211	91.34 177

CR-MVSNet		74.00 250	73.04 257	76.85 253	79.58 294	62.64 206	82.58 184	76.90 262	50.50 328	75.72 279	92.38 121	48.07 293	84.07 282	68.72 197	82.91 306	83.85 275

Test_1112_low_res		73.90 251	73.08 256	76.35 258	90.35 131	55.95 265	73.40 303	86.17 208	50.70 326	73.14 296	85.94 246	58.31 261	85.90 267	56.51 277	83.22 303	87.20 237

test20.03		73.75 252	74.59 238	71.22 294	81.11 286	51.12 305	70.15 312	72.10 296	70.42 202	80.28 249	91.50 143	64.21 233	74.72 315	46.96 324	94.58 152	87.82 232

thresconf0.02		73.65 253	73.53 247	73.98 273	86.13 230	50.06 318	79.45 243	68.54 317	72.01 183	80.76 235	82.50 293	41.39 330	86.83 248	59.66 253	91.36 211	85.06 257

tfpn_n400		73.65 253	73.53 247	73.98 273	86.13 230	50.06 318	79.45 243	68.54 317	72.01 183	80.76 235	82.50 293	41.39 330	86.83 248	59.66 253	91.36 211	85.06 257

tfpnconf		73.65 253	73.53 247	73.98 273	86.13 230	50.06 318	79.45 243	68.54 317	72.01 183	80.76 235	82.50 293	41.39 330	86.83 248	59.66 253	91.36 211	85.06 257

tfpnview11		73.65 253	73.53 247	73.98 273	86.13 230	50.06 318	79.45 243	68.54 317	72.01 183	80.76 235	82.50 293	41.39 330	86.83 248	59.66 253	91.36 211	85.06 257

tfpn1000		73.63 257	73.58 245	73.79 279	85.46 242	50.31 315	79.99 234	68.18 323	72.33 177	80.66 241	83.05 282	39.80 341	86.74 257	60.96 245	91.78 207	84.32 269

1314		73.22 258	72.56 263	75.20 267	80.41 293	57.84 254	81.64 211	85.36 216	51.68 318	73.10 297	76.65 328	61.45 243	85.19 273	63.54 228	79.21 324	82.59 291

MVS		73.21 259	72.59 262	75.06 268	80.97 287	60.81 237	81.64 211	85.92 211	46.03 339	71.68 304	77.54 323	68.47 220	89.77 207	55.70 282	85.39 286	74.60 331

HY-MVS	64.64 18	73.03 260	72.47 264	74.71 270	83.36 269	54.19 277	82.14 200	81.96 238	56.76 293	69.57 314	86.21 244	60.03 250	84.83 278	49.58 312	82.65 308	85.11 256

EPNet_dtu		72.87 261	71.33 273	77.49 247	77.72 311	60.55 239	82.35 191	75.79 267	66.49 233	58.39 348	81.06 310	53.68 280	85.98 265	53.55 295	92.97 190	85.95 248
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test1		72.75 262	72.61 261	73.19 283	81.62 281	55.86 267	78.89 258	71.37 303	61.73 270	74.93 286	82.15 300	60.46 247	81.80 293	59.68 252	82.63 310	81.92 304

CVMVSNet		72.62 263	71.41 272	76.28 260	83.25 270	60.34 240	83.50 161	79.02 254	37.77 349	76.33 272	85.10 258	49.60 289	87.41 240	70.54 181	77.54 329	81.08 318

tfpn_ndepth		72.54 264	72.30 265	73.24 282	84.81 249	51.42 301	79.24 252	70.49 310	69.26 212	78.48 260	79.80 316	40.16 340	86.77 255	58.08 272	90.43 240	81.53 311

CHOSEN 1792x2688		72.45 265	70.56 275	78.13 234	90.02 140	63.08 198	68.72 315	83.16 230	42.99 345	75.92 277	85.46 252	57.22 268	85.18 274	49.87 310	81.67 312	86.14 246

testgi		72.36 266	74.61 236	65.59 313	80.56 292	42.82 341	68.29 316	73.35 285	66.87 231	81.84 220	89.93 188	72.08 204	66.92 333	46.05 326	92.54 196	87.01 239

thres200		72.34 267	71.55 271	74.70 271	83.48 267	51.60 300	75.02 291	73.71 282	70.14 206	78.56 259	80.57 311	46.20 298	88.20 234	46.99 323	89.29 247	84.32 269

FPMVS		72.29 268	72.00 267	73.14 284	88.63 159	85.00 32	74.65 294	67.39 326	71.94 190	77.80 264	87.66 225	50.48 287	75.83 311	49.95 308	79.51 320	58.58 348

FMVSNet5		72.10 269	71.69 269	73.32 280	81.57 282	53.02 286	76.77 275	78.37 255	63.31 257	76.37 271	91.85 132	36.68 344	78.98 303	47.87 319	92.45 197	87.95 229

PAPM		71.77 270	70.06 281	76.92 251	86.39 218	53.97 278	76.62 277	86.62 204	53.44 306	63.97 335	84.73 266	57.79 264	92.34 145	39.65 336	81.33 315	84.45 266

IB-MVS	62.13 19	71.64 271	68.97 285	79.66 217	80.80 291	62.26 220	73.94 298	76.90 262	63.27 258	68.63 317	76.79 327	33.83 347	91.84 156	59.28 261	87.26 271	84.88 262
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
UnsupCasMVSNet_eth		71.63 272	72.30 265	69.62 298	76.47 319	52.70 289	70.03 313	80.97 246	59.18 282	79.36 255	88.21 216	60.50 246	69.12 326	58.33 269	77.62 328	87.04 238

no-one		71.52 273	70.43 278	74.81 269	78.45 307	63.41 190	57.73 341	77.03 261	51.46 321	77.17 268	90.33 181	54.96 278	80.35 300	47.41 320	99.29 2	80.68 322

Anonymous20231206		71.38 274	71.88 268	69.88 295	86.31 223	54.37 276	70.39 311	74.62 275	52.57 311	76.73 269	88.76 204	59.94 251	72.06 318	44.35 329	93.23 185	83.23 286

MIMVSNet		71.09 275	71.59 270	69.57 299	87.23 204	50.07 317	78.91 257	71.83 299	60.20 280	71.26 306	91.76 137	55.08 277	76.09 309	41.06 334	87.02 274	82.54 293

MS-PatchMatch		70.93 276	70.22 279	73.06 285	81.85 280	62.50 212	73.82 300	77.90 256	52.44 312	75.92 277	81.27 308	55.67 273	81.75 294	55.37 285	77.70 327	74.94 330

pmmvs5		70.73 277	70.07 280	72.72 287	77.03 316	52.73 288	74.14 296	75.65 270	50.36 329	72.17 302	85.37 256	55.42 275	80.67 298	52.86 298	87.59 269	84.77 263

PatchT		70.52 278	72.76 259	63.79 319	79.38 297	33.53 349	77.63 269	65.37 334	73.61 154	71.77 303	92.79 114	44.38 323	75.65 312	64.53 225	85.37 287	82.18 299

testmv		70.47 279	70.70 274	69.77 297	86.22 227	53.89 280	67.32 321	71.91 298	63.32 256	78.16 261	89.47 196	56.12 271	73.10 316	36.43 342	87.33 270	82.33 297

N_pmnet		70.20 280	68.80 287	74.38 272	80.91 288	84.81 35	59.12 338	76.45 266	55.06 298	75.31 284	82.36 299	55.74 272	54.82 350	47.02 322	87.24 272	83.52 279

tpmvs		70.16 281	69.56 283	71.96 292	74.71 335	48.13 326	79.63 237	75.45 271	65.02 250	70.26 311	81.88 303	45.34 313	85.68 269	58.34 268	75.39 332	82.08 300

new-patchmatchnet		70.10 282	73.37 254	60.29 328	81.23 285	16.95 356	59.54 335	74.62 275	62.93 260	80.97 230	87.93 221	62.83 240	71.90 319	55.24 286	95.01 136	92.00 160

YYNet1		70.06 283	70.44 276	68.90 301	73.76 337	53.42 284	58.99 339	67.20 328	58.42 284	87.10 144	85.39 255	59.82 253	67.32 330	59.79 251	83.50 302	85.96 247

MDA-MVSNet_test_wron		70.05 284	70.44 276	68.88 302	73.84 336	53.47 282	58.93 340	67.28 327	58.43 283	87.09 145	85.40 254	59.80 254	67.25 331	59.66 253	83.54 301	85.92 249

CostFormer		69.98 285	68.68 288	73.87 277	77.14 314	50.72 313	79.26 251	74.51 277	51.94 317	70.97 309	84.75 265	45.16 320	87.49 239	55.16 287	79.23 323	83.40 282

PatchmatchNet		69.71 286	68.83 286	72.33 290	77.66 312	53.60 281	79.29 250	69.99 312	57.66 287	72.53 299	82.93 287	46.45 297	80.08 302	60.91 246	72.09 338	83.31 285
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmp4_e23		69.43 287	67.33 294	75.72 265	78.53 306	52.75 287	82.13 201	74.91 272	49.23 333	66.37 324	84.17 272	41.28 336	88.67 229	49.73 311	79.63 319	85.75 251

LP		69.42 288	68.30 290	72.77 286	71.48 348	56.84 263	73.66 301	74.84 273	63.52 255	70.95 310	83.35 281	49.55 290	77.15 307	57.13 275	70.21 341	84.33 268

JIA-IIPM		69.41 289	66.64 299	77.70 241	73.19 340	71.24 139	75.67 285	65.56 333	70.42 202	65.18 330	92.97 107	33.64 348	83.06 288	53.52 296	69.61 345	78.79 324

UnsupCasMVSNet_bld		69.21 290	69.68 282	67.82 307	79.42 296	51.15 304	67.82 320	75.79 267	54.15 302	77.47 266	85.36 257	59.26 257	70.64 321	48.46 316	79.35 322	81.66 308

gg-mvs-nofinetune		68.96 291	69.11 284	68.52 306	76.12 322	45.32 332	83.59 154	55.88 349	86.68 20	64.62 334	97.01 7	30.36 351	83.97 285	44.78 328	82.94 305	76.26 328

tpm2		68.45 292	66.83 296	73.30 281	78.93 302	48.50 325	79.76 236	71.76 300	47.50 335	69.92 313	83.60 276	42.07 329	88.40 231	48.44 317	79.51 320	83.01 289

tpm		67.95 293	68.08 292	67.55 308	78.74 304	43.53 339	75.60 286	67.10 331	54.92 299	72.23 301	88.10 217	42.87 327	75.97 310	52.21 299	80.95 318	83.15 287

PatchFormer-LS_test		67.91 294	66.49 300	72.17 291	75.29 330	51.85 298	75.68 284	73.62 284	57.23 290	68.64 315	68.13 345	42.19 328	82.76 290	64.06 226	73.51 335	81.89 305

WTY-MVS		67.91 294	68.35 289	66.58 311	80.82 290	48.12 327	65.96 325	72.60 292	53.67 305	71.20 307	81.68 306	58.97 258	69.06 327	48.57 315	81.67 312	82.55 292

test-LLR		67.21 296	66.74 297	68.63 304	76.45 320	55.21 272	67.89 317	67.14 329	62.43 267	65.08 331	72.39 337	43.41 324	69.37 323	61.00 243	84.89 293	81.31 313

sss		66.92 297	67.26 295	65.90 312	77.23 313	51.10 306	64.79 326	71.72 302	52.12 316	70.13 312	80.18 313	57.96 262	65.36 340	50.21 307	81.01 317	81.25 315

tpm cat1		66.76 298	65.21 303	71.42 293	77.09 315	50.62 314	78.01 264	73.68 283	44.89 341	68.64 315	79.00 319	45.51 310	82.42 292	49.91 309	70.15 342	81.23 317

DWT-MVSNet_test		66.43 299	64.37 304	72.63 288	74.86 334	50.86 308	76.52 278	72.74 291	54.06 303	65.50 328	68.30 344	32.13 349	84.84 277	61.63 240	73.59 334	82.19 298

PVSNet	58.17 21	66.41 300	65.63 302	68.75 303	81.96 278	49.88 324	62.19 331	72.51 294	51.03 323	68.04 319	75.34 333	50.84 285	74.77 313	45.82 327	82.96 304	81.60 309

tpmrst		66.28 301	66.69 298	65.05 317	72.82 344	39.33 344	78.20 263	70.69 309	53.16 308	67.88 320	80.36 312	48.18 292	74.75 314	58.13 270	70.79 340	81.08 318

Patchmatch-test		65.91 302	67.38 293	61.48 325	75.51 327	43.21 340	68.84 314	63.79 336	62.48 265	72.80 298	83.42 280	44.89 321	59.52 347	48.27 318	86.45 278	81.70 306

ADS-MVSNet2		65.87 303	63.64 308	72.55 289	73.16 341	56.92 261	67.10 322	74.81 274	49.74 330	66.04 326	82.97 285	46.71 295	77.26 305	42.29 331	69.96 343	83.46 280

test1235678		65.57 304	65.73 301	65.06 316	82.84 275	50.90 307	62.90 329	69.26 314	57.17 291	72.36 300	83.04 283	46.02 300	70.10 322	32.79 347	85.24 290	74.19 332

test-mter		65.00 305	63.79 306	68.63 304	76.45 320	55.21 272	67.89 317	67.14 329	50.98 324	65.08 331	72.39 337	28.27 354	69.37 323	61.00 243	84.89 293	81.31 313

test0.0.03 1		64.66 306	64.36 305	65.57 314	75.03 333	46.89 329	64.69 327	61.58 342	62.43 267	71.18 308	77.54 323	43.41 324	68.47 328	40.75 335	82.65 308	81.35 312

pmmvs3		62.47 307	60.02 320	69.80 296	71.58 347	64.00 184	70.52 310	58.44 346	39.77 347	66.05 325	75.84 330	27.10 356	72.28 317	46.15 325	84.77 297	73.11 334

EPMVS		62.47 307	62.63 311	62.01 321	70.63 349	38.74 345	74.76 292	52.86 351	53.91 304	67.71 322	80.01 314	39.40 342	66.60 335	55.54 284	68.81 347	80.68 322

testus		62.33 309	63.03 309	60.20 329	78.78 303	40.74 342	59.14 336	69.80 313	49.26 332	71.41 305	74.72 335	52.33 283	63.52 342	29.84 349	82.01 311	76.36 327

ADS-MVSNet		61.90 310	62.19 312	61.03 327	73.16 341	36.42 347	67.10 322	61.75 340	49.74 330	66.04 326	82.97 285	46.71 295	63.21 344	42.29 331	69.96 343	83.46 280

1111		61.71 311	63.77 307	55.55 334	78.05 308	25.74 353	60.62 332	67.52 324	66.09 236	74.68 287	86.50 235	16.00 359	59.22 348	38.79 337	85.65 284	81.70 306

PMMVS		61.65 312	60.38 317	65.47 315	65.40 353	69.26 151	63.97 328	61.73 341	36.80 350	60.11 341	68.43 341	59.42 255	66.35 336	48.97 314	78.57 325	60.81 345

E-PMN		61.59 313	61.62 313	61.49 324	66.81 350	55.40 270	53.77 345	60.34 343	66.80 232	58.90 346	65.50 347	40.48 339	66.12 337	55.72 281	86.25 281	62.95 344

TESTMET0.1,1		61.29 314	60.32 318	64.19 318	72.06 345	51.30 302	67.89 317	62.09 338	45.27 340	60.65 340	69.01 340	27.93 355	64.74 341	56.31 278	81.65 314	76.53 326

MVS-HIRNet		61.16 315	62.92 310	55.87 332	79.09 300	35.34 348	71.83 306	57.98 348	46.56 337	59.05 345	91.14 154	49.95 288	76.43 308	38.74 339	71.92 339	55.84 349

EMVS		61.10 316	60.81 316	61.99 322	65.96 352	55.86 267	53.10 346	58.97 345	67.06 228	56.89 350	63.33 348	40.98 337	67.03 332	54.79 290	86.18 282	63.08 343

DSMNet-mixed		60.98 317	61.61 314	59.09 331	72.88 343	45.05 335	74.70 293	46.61 355	26.20 351	65.34 329	90.32 182	55.46 274	63.12 345	41.72 333	81.30 316	69.09 340

dp		60.70 318	60.29 319	61.92 323	72.04 346	38.67 346	70.83 308	64.08 335	51.28 322	60.75 339	77.28 325	36.59 345	71.58 320	47.41 320	62.34 349	75.52 329

CHOSEN 280x420		59.08 319	56.52 324	66.76 310	76.51 318	64.39 181	49.62 348	59.00 344	43.86 343	55.66 351	68.41 343	35.55 346	68.21 329	43.25 330	76.78 331	67.69 341

testpf		58.55 320	61.58 315	49.48 337	66.03 351	40.05 343	74.40 295	58.07 347	64.72 252	59.36 343	72.67 336	22.76 357	66.92 333	67.07 206	69.15 346	41.46 351

test2356		56.69 321	55.15 325	61.32 326	73.20 339	44.11 337	54.95 343	62.52 337	48.75 334	62.45 337	68.42 342	21.10 358	65.67 339	26.86 351	78.08 326	74.19 332

PVSNet_0	51.08 22	56.10 322	54.97 326	59.48 330	75.12 332	53.28 285	55.16 342	61.89 339	44.30 342	59.16 344	62.48 349	54.22 279	65.91 338	35.40 344	47.01 350	59.25 347

new_pmnet		55.69 323	57.66 322	49.76 336	75.47 328	30.59 350	59.56 334	51.45 353	43.62 344	62.49 336	75.48 331	40.96 338	49.15 353	37.39 341	72.52 336	69.55 339

PMMVS2		55.64 324	59.27 321	44.74 339	64.30 354	12.32 357	40.60 349	49.79 354	53.19 307	65.06 333	84.81 264	53.60 281	49.76 352	32.68 348	89.41 246	72.15 335

test12356		54.91 325	57.14 323	48.22 338	75.83 324	17.47 355	52.31 347	69.20 315	51.66 319	60.11 341	75.40 332	29.77 353	62.62 346	27.64 350	72.37 337	64.59 342

PNet_i23d		52.13 326	51.24 328	54.79 335	75.56 325	45.26 333	54.54 344	52.55 352	66.95 229	57.19 349	65.82 346	13.15 361	63.40 343	36.39 343	39.04 352	55.71 350

MVE	40.22 23	51.82 327	50.47 329	55.87 332	62.66 355	51.91 294	31.61 351	39.28 356	40.65 346	50.76 352	74.98 334	56.24 270	44.67 354	33.94 346	64.11 348	71.04 338
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
.test1245		48.02 328	54.41 327	28.84 341	78.05 308	25.74 353	60.62 332	67.52 324	66.09 236	74.68 287	86.50 235	16.00 359	59.22 348	38.79 337	1.47 354	1.55 355

pcd1.5k->3k		38.83 329	41.11 330	32.01 340	93.13 63	0.00 361	0.00 352	91.38 113	0.00 356	0.00 357	0.00 358	89.24 16	0.00 359	0.00 356	96.24 95	96.02 48

cdsmvs_eth3d_5k		20.81 330	27.75 331	0.00 346	0.00 360	0.00 361	0.00 352	85.44 215	0.00 356	0.00 357	82.82 288	81.46 93	0.00 359	0.00 356	0.00 357	0.00 357

tmp_tt		20.25 331	24.50 332	7.49 343	4.47 357	8.70 358	34.17 350	25.16 358	1.00 353	32.43 354	18.49 352	39.37 343	9.21 356	21.64 352	43.75 351	4.57 353

ab-mvs-re		6.65 332	8.87 333	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	79.80 316	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

pcd_1.5k_mvsjas		6.41 333	8.55 334	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	76.94 133	0.00 359	0.00 356	0.00 357	0.00 357

test123		6.27 334	8.08 335	0.84 344	1.11 359	0.57 359	62.90 329	0.82 360	0.54 354	1.07 356	2.75 357	1.26 362	0.30 357	1.04 354	1.26 356	1.66 354

testmvs		5.91 335	7.65 336	0.72 345	1.20 358	0.37 360	59.14 336	0.67 361	0.49 355	1.11 355	2.76 356	0.94 363	0.24 358	1.02 355	1.47 354	1.55 355

sosnet-low-res		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

sosnet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uncertanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

Regformer		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

GSMVS																83.88 273

test_part3							89.63 51		84.39 28		93.43 98		96.26 4	82.18 75

test_part2					93.86 48	77.77 80				92.84 43

test_part1								93.93 25				87.19 36			97.61 55	91.48 173

sam_mvs1												46.11 299				83.88 273

sam_mvs												45.92 304

semantic-postprocess				84.34 138	83.93 264	69.66 147		81.09 245	72.43 173	86.47 159	90.19 184	57.56 265	93.15 125	77.45 128	86.39 280	90.22 203

ambc				82.98 171	90.55 130	64.86 177	88.20 74	89.15 167		89.40 111	93.96 87	71.67 209	91.38 171	78.83 115	96.55 82	92.71 135

MTGPA								91.81 91

test_post1							78.85 259				3.13 354	45.19 315	80.13 301	58.11 271

test_post											3.10 355	45.43 311	77.22 306

patchmatchnet-post											81.71 305	45.93 303	87.01 243

GG-mvs-BLEND				67.16 309	73.36 338	46.54 331	84.15 137	55.04 350		58.64 347	61.95 350	29.93 352	83.87 286	38.71 340	76.92 330	71.07 337

MTMP								33.14 357

gm-plane-assit					75.42 329	44.97 336			52.17 313		72.36 339		87.90 235	54.10 293

test9_res														80.83 90	96.45 88	90.57 195

TEST9					92.34 83	79.70 66	83.94 142	90.32 137	65.41 246	84.49 187	90.97 162	82.03 84	93.63 89

test_8					92.09 90	78.87 73	83.82 147	90.31 139	65.79 239	84.36 190	90.96 164	81.93 86	93.44 110

agg_prior2														79.68 107	96.16 97	90.22 203

agg_prior					91.58 104	77.69 82		90.30 141		84.32 191			93.18 121

TestCases				89.68 48	91.59 101	83.40 45		95.44 5	79.47 77	88.00 132	93.03 104	82.66 74	91.47 165	70.81 175	96.14 99	94.16 95

test_prior4						78.97 72	84.59 130

test_prior2							83.37 164		75.43 137	84.58 185	91.57 140	81.92 88		79.54 109	96.97 70

test_prior				86.32 93	90.59 128	71.99 130		92.85 62					94.17 69			92.80 132

旧先验2							81.73 209		56.88 292	86.54 158			84.90 276	72.81 165

新几何2							81.72 210

新几何1				82.95 172	93.96 45	78.56 76		80.24 248	55.45 296	83.93 196	91.08 158	71.19 211	88.33 232	65.84 217	93.07 186	81.95 303

旧先验1					91.97 93	71.77 132		81.78 241			91.84 133	73.92 169			93.65 172	83.61 278

无先验							82.81 178	85.62 214	58.09 285				91.41 169	67.95 203		84.48 265

原ACMM2							82.26 196

原ACMM1				84.60 129	92.81 73	74.01 111		91.50 98	62.59 263	82.73 212	90.67 174	76.53 140	94.25 64	69.24 189	95.69 118	85.55 252

test222					93.31 58	76.54 95	79.38 249	77.79 257	52.59 310	82.36 213	90.84 168	66.83 225			91.69 208	81.25 315

testdata2													86.43 261	63.52 229

segment_acmp												81.94 85

testdata				79.54 219	92.87 69	72.34 123		80.14 250	59.91 281	85.47 172	91.75 138	67.96 221	85.24 272	68.57 199	92.18 205	81.06 320

testdata1							79.62 238		73.95 152

test12				86.57 88	90.74 125	72.63 119		90.69 126		82.76 211		79.20 110	94.80 52		95.32 125	92.27 154

plane_prior7					93.45 54	77.31 88

plane_prior6					92.61 74	76.54 95						74.84 154

plane_prior5								93.61 32					95.22 40	80.78 91	95.83 113	94.46 86

plane_prior4											92.95 108

plane_prior3						76.85 93			77.79 101	86.55 153

plane_prior2							89.45 59		79.44 79

plane_prior1					92.83 72

plane_prior						76.42 98	87.15 92		75.94 129						95.03 135

n2								0.00 362

nn								0.00 362

door-mid								74.45 278

lessismore_v0				85.95 104	91.10 119	70.99 141		70.91 308		91.79 63	94.42 68	61.76 242	92.93 133	79.52 111	93.03 188	93.93 102

LGP-MVS_train				90.82 34	94.75 36	81.69 51		94.27 13	82.35 48	93.67 30	94.82 55	91.18 6	95.52 28	85.36 35	98.73 8	95.23 72

test11								91.46 104

door								72.57 293

HQP5-MVS						70.66 142

HQP-NCC					91.19 114		84.77 125		73.30 161	80.55 245

ACMP_Plane					91.19 114		84.77 125		73.30 161	80.55 245

BP-MVS														77.30 130

HQP4-MVS										80.56 244			94.61 56			93.56 117

HQP3-MVS								92.68 68							94.47 154

HQP2-MVS												72.10 202

NP-MVS					91.95 94	74.55 108					90.17 186

MDTV_nov1_ep13_2view						27.60 352	70.76 309		46.47 338	61.27 338		45.20 314		49.18 313		83.75 277

MDTV_nov1_ep13			68.29 291		78.03 310	43.87 338	74.12 297	72.22 295	52.17 313	67.02 323	85.54 248	45.36 312	80.85 297	55.73 280	84.42 298

ACMMP++_ref															95.74 117

ACMMP++															97.35 61

Test By Simon												79.09 111

ITE_SJBPF				90.11 45	90.72 126	84.97 33		90.30 141	81.56 59	90.02 86	91.20 151	82.40 78	90.81 184	73.58 155	94.66 150	94.56 81

DeepMVS_CX				24.13 342	32.95 356	29.49 351		21.63 359	12.07 352	37.95 353	45.07 351	30.84 350	19.21 355	17.94 353	33.06 353	23.69 352

High-resolution multi-view benchmark