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 10	85.07 38	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 7	92.86 42	96.29 18	88.16 25	94.17 69	86.07 34	98.48 19	97.22 25

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 12	95.63 10	96.99 8	84.81 54	93.16 126	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 10	94.43 18	95.27 42	91.86 3	95.09 44	87.54 19	98.02 38	93.71 115

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 25	92.51 52	95.13 47	90.65 10	95.34 35	88.06 10	98.15 33	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 44	91.77 65	93.94 93	90.55 12	95.73 20	88.50 8	98.23 30	95.33 70
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 66	85.17 31	92.47 21	95.05 8	87.65 18	93.21 36	94.39 73	90.09 13	95.08 45	86.67 26	97.60 57	94.18 97

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 8	92.11 57	95.03 49	89.75 14	94.93 49	79.95 106	98.27 28	95.04 78
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 13	92.03 11	95.53 24	85.91 22	93.35 9	94.16 20	82.52 48	92.39 56	94.14 81	89.15 16	95.62 22	87.35 20	98.24 29	94.56 84
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 19	92.37 5	96.04 11	88.48 8	92.72 14	92.60 74	83.09 41	91.54 68	94.25 77	87.67 33	95.51 30	87.21 24	98.11 34	93.12 130

CP-MVS		91.67 11	91.58 16	91.96 12	95.29 29	87.62 9	93.38 7	93.36 39	83.16 40	91.06 75	94.00 86	88.26 22	95.71 21	87.28 23	98.39 22	92.55 150

XVS		91.54 12	91.36 21	92.08 8	95.64 21	86.25 16	92.64 15	93.33 41	85.07 26	89.99 89	94.03 85	86.57 42	95.80 15	87.35 20	97.62 55	94.20 95

MTAPA		91.52 13	91.60 15	91.29 25	96.59 3	86.29 14	92.02 24	91.81 95	84.07 33	92.00 60	94.40 71	86.63 40	95.28 38	88.59 4	98.31 25	92.30 157

UA-Net		91.49 14	91.53 17	91.39 22	94.98 33	82.95 50	93.52 5	92.79 67	88.22 15	88.53 127	97.64 2	83.45 65	94.55 60	86.02 35	98.60 14	96.67 36

ACMMPR		91.49 14	91.35 23	91.92 14	95.74 18	85.88 24	92.58 18	93.25 48	81.99 54	91.40 71	94.17 80	87.51 34	95.87 12	87.74 12	97.76 48	93.99 102

LPG-MVS_test		91.47 16	91.68 14	90.82 34	94.75 37	81.69 52	90.00 41	94.27 13	82.35 49	93.67 30	94.82 56	91.18 5	95.52 28	85.36 36	98.73 8	95.23 74

region2R		91.44 17	91.30 25	91.87 16	95.75 17	85.90 23	92.63 17	93.30 44	81.91 56	90.88 80	94.21 78	87.75 30	95.87 12	87.60 17	97.71 52	93.83 108

HFP-MVS		91.30 18	91.39 20	91.02 29	95.43 26	84.66 39	92.58 18	93.29 46	81.99 54	91.47 69	93.96 89	88.35 20	95.56 25	87.74 12	97.74 50	92.85 135

zzz-MVS		91.27 19	91.26 26	91.29 25	96.59 3	86.29 14	88.94 65	91.81 95	84.07 33	92.00 60	94.40 71	86.63 40	95.28 38	88.59 4	98.31 25	92.30 157

APDe-MVS		91.22 20	91.92 9	89.14 58	92.97 68	78.04 80	92.84 12	94.14 21	83.33 38	93.90 25	95.73 29	88.77 17	96.41 1	87.60 17	97.98 42	92.98 133

PGM-MVS		91.20 21	90.95 34	91.93 13	95.67 20	85.85 25	90.00 41	93.90 29	80.32 72	91.74 66	94.41 70	88.17 24	95.98 7	86.37 27	97.99 40	93.96 104

SteuartSystems-ACMMP		91.16 22	91.36 21	90.55 38	93.91 47	80.97 59	91.49 30	93.48 38	82.82 45	92.60 51	93.97 87	88.19 23	96.29 4	87.61 16	98.20 32	94.39 93
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 23	90.91 35	91.83 18	96.18 10	86.88 11	92.20 22	93.03 58	82.59 47	88.52 128	94.37 74	86.74 39	95.41 33	86.32 28	98.21 31	93.19 129
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		90.96 24	91.01 31	90.82 34	95.45 25	82.73 51	91.75 28	93.74 32	80.98 66	91.38 72	93.80 97	87.20 36	95.80 15	87.10 25	97.69 53	93.93 105

MP-MVS-pluss		90.81 25	91.08 28	89.99 47	95.97 12	79.88 64	88.13 78	94.51 11	75.79 136	92.94 39	94.96 51	88.36 19	95.01 47	90.70 2	98.40 21	95.09 77
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 26	91.50 18	88.44 65	93.00 67	76.26 101	89.65 51	95.55 3	87.72 17	93.89 27	94.94 52	91.62 4	93.44 112	78.35 122	98.76 5	95.61 65

ACMMP_Plus		90.65 27	91.07 30	89.42 54	95.93 14	79.54 69	89.95 44	93.68 33	77.65 106	91.97 62	94.89 53	88.38 18	95.45 31	89.27 3	97.87 45	93.27 125

ACMM	79.39 9	90.65 27	90.99 32	89.63 51	95.03 32	83.53 44	89.62 52	93.35 40	79.20 85	93.83 28	93.60 100	90.81 8	92.96 135	85.02 40	98.45 20	92.41 153
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 29	90.34 42	91.38 23	89.03 153	84.23 42	93.58 4	94.68 10	90.65 6	90.33 84	93.95 92	84.50 57	95.37 34	80.87 90	95.50 122	94.53 88

ACMP	79.16 10	90.54 30	90.60 38	90.35 42	94.36 41	80.98 58	89.16 61	94.05 23	79.03 89	92.87 41	93.74 98	90.60 11	95.21 42	82.87 68	98.76 5	94.87 79
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ESAPD		90.53 31	91.08 28	88.88 60	93.38 57	78.65 77	89.15 62	94.05 23	84.68 29	93.90 25	94.11 83	88.13 26	96.30 3	84.51 47	97.81 46	91.70 175

#test#		90.49 32	90.31 43	91.02 29	95.43 26	84.66 39	90.65 37	93.29 46	77.00 121	91.47 69	93.96 89	88.35 20	95.56 25	84.88 41	97.74 50	92.85 135

SMA-MVS		90.31 33	90.48 41	89.83 48	95.31 28	79.52 70	90.98 35	93.24 49	75.37 145	92.84 43	95.28 41	85.58 51	96.09 5	87.92 11	97.76 48	93.88 107

v7n		90.13 34	90.96 33	87.65 78	91.95 95	71.06 141	89.99 43	93.05 55	86.53 21	94.29 21	96.27 19	82.69 72	94.08 73	86.25 31	97.63 54	97.82 10

PMVS	80.48 6	90.08 35	90.66 37	88.34 67	96.71 2	92.97 2	90.31 39	89.57 168	88.51 14	90.11 85	95.12 48	90.98 7	88.92 230	77.55 130	97.07 68	83.13 300
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		90.06 36	91.92 9	84.47 135	96.56 6	58.83 261	89.04 63	92.74 69	91.40 4	96.12 4	96.06 24	87.23 35	95.57 24	79.42 114	98.74 7	99.00 2

PEN-MVS		90.03 37	91.88 12	84.48 134	96.57 5	58.88 258	88.95 64	93.19 50	91.62 3	96.01 6	96.16 22	87.02 37	95.60 23	78.69 120	98.72 10	98.97 3

OurMVSNet-221017-0		90.01 38	89.74 48	90.83 33	93.16 63	80.37 60	91.91 27	93.11 52	81.10 64	95.32 12	97.24 5	72.94 195	94.85 51	85.07 38	97.78 47	97.26 22

DTE-MVSNet		89.98 39	91.91 11	84.21 145	96.51 7	57.84 264	88.93 66	92.84 66	91.92 2	96.16 3	96.23 20	86.95 38	95.99 6	79.05 117	98.57 16	98.80 6

XVG-ACMP-BASELINE		89.98 39	89.84 46	90.41 40	94.91 35	84.50 41	89.49 57	93.98 25	79.68 78	92.09 58	93.89 94	83.80 61	93.10 129	82.67 72	98.04 35	93.64 117

v52		89.97 41	90.60 38	88.07 71	88.69 160	72.01 129	91.35 31	92.64 72	82.22 51	95.97 8	96.31 16	84.82 53	93.98 77	88.59 4	94.83 146	98.23 7

V4		89.97 41	90.60 38	88.07 71	88.69 160	72.01 129	91.35 31	92.64 72	82.22 51	95.98 7	96.31 16	84.80 55	93.98 77	88.59 4	94.83 146	98.23 7

3Dnovator+	83.92 2	89.97 41	89.66 49	90.92 32	91.27 114	81.66 55	91.25 33	94.13 22	88.89 11	88.83 122	94.26 76	77.55 127	95.86 14	84.88 41	95.87 112	95.24 73

WR-MVS_H		89.91 44	91.31 24	85.71 111	96.32 9	62.39 219	89.54 55	93.31 43	90.21 9	95.57 11	95.66 31	81.42 95	95.90 11	80.94 89	98.80 4	98.84 5

OPM-MVS		89.80 45	89.97 44	89.27 56	94.76 36	79.86 65	86.76 104	92.78 68	78.78 92	92.51 52	93.64 99	88.13 26	93.84 84	84.83 43	97.55 58	94.10 100

mvs_tets		89.78 46	89.27 54	91.30 24	93.51 53	84.79 36	89.89 46	90.63 133	70.00 215	94.55 17	96.67 11	87.94 29	93.59 95	84.27 51	95.97 107	95.52 66

anonymousdsp		89.73 47	88.88 59	92.27 7	89.82 144	86.67 12	90.51 38	90.20 154	69.87 216	95.06 13	96.14 23	84.28 58	93.07 134	87.68 14	96.34 91	97.09 29

test_djsdf		89.62 48	89.01 56	91.45 21	92.36 83	82.98 49	91.98 25	90.08 155	71.54 202	94.28 22	96.54 13	81.57 93	94.27 62	86.26 29	96.49 86	97.09 29

XVG-OURS-SEG-HR		89.59 49	89.37 53	90.28 43	94.47 40	85.95 21	86.84 100	93.91 28	80.07 75	86.75 154	93.26 103	93.64 2	90.93 184	84.60 46	90.75 243	93.97 103

APD-MVS		89.54 50	89.63 50	89.26 57	92.57 76	81.34 57	90.19 40	93.08 54	80.87 67	91.13 74	93.19 104	86.22 47	95.97 8	82.23 76	97.18 66	90.45 206
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 51	88.81 61	91.19 28	93.38 57	84.72 37	89.70 48	90.29 149	69.27 219	94.39 19	96.38 15	86.02 49	93.52 106	83.96 54	95.92 110	95.34 69

CPTT-MVS		89.39 52	88.98 58	90.63 37	95.09 31	86.95 10	92.09 23	92.30 80	79.74 77	87.50 143	92.38 126	81.42 95	93.28 121	83.07 65	97.24 64	91.67 176

ACMH	76.49 14	89.34 53	91.14 27	83.96 152	92.50 79	70.36 145	89.55 53	93.84 30	81.89 57	94.70 15	95.44 39	90.69 9	88.31 243	83.33 61	98.30 27	93.20 128
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		89.27 54	90.91 35	84.37 139	96.34 8	58.61 263	88.66 72	92.06 86	90.78 5	95.67 9	95.17 46	81.80 91	95.54 27	79.00 118	98.69 11	98.95 4

XVG-OURS		89.18 55	88.83 60	90.23 44	94.28 42	86.11 20	85.91 116	93.60 36	80.16 74	89.13 118	93.44 101	83.82 60	90.98 182	83.86 57	95.30 129	93.60 119

DeepC-MVS	82.31 4	89.15 56	89.08 55	89.37 55	93.64 52	79.07 72	88.54 74	94.20 17	73.53 162	89.71 101	94.82 56	85.09 52	95.77 18	84.17 53	98.03 37	93.26 126
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 98	91.63 101	77.07 91	89.82 47	93.77 31	78.90 90	92.88 40	92.29 130	86.11 48	90.22 205	86.24 32	97.24 64	91.36 183

HPM-MVS++		88.93 58	88.45 65	90.38 41	94.92 34	85.85 25	89.70 48	91.27 120	78.20 100	86.69 155	92.28 131	80.36 104	95.06 46	86.17 33	96.49 86	90.22 210

v748		88.91 59	89.82 47	86.19 102	90.06 140	68.53 157	88.81 69	91.48 104	84.36 31	94.19 23	95.98 25	82.52 75	92.67 144	84.30 50	96.67 79	97.37 19

test_0402		88.65 60	89.58 52	85.88 108	92.55 77	72.22 127	84.01 145	89.44 170	88.63 13	94.38 20	95.77 28	86.38 46	93.59 95	79.84 107	95.21 130	91.82 171

HSP-MVS		88.63 61	87.84 70	91.02 29	95.76 16	86.14 19	92.75 13	91.01 127	78.43 97	89.16 117	92.25 132	72.03 210	96.36 2	88.21 9	90.93 238	90.55 204

DP-MVS		88.60 62	89.01 56	87.36 83	91.30 112	77.50 86	87.55 87	92.97 61	87.95 16	89.62 107	92.87 115	84.56 56	93.89 81	77.65 129	96.62 80	90.70 198

wuykxyi23d		88.46 63	88.80 62	87.44 82	90.96 122	93.03 1	85.85 118	81.96 247	74.58 151	98.58 2	97.29 4	87.73 31	87.31 250	82.84 70	99.41 1	81.99 313

Anonymous20231211		88.40 64	89.62 51	84.73 127	90.46 132	65.27 179	88.86 67	93.02 59	87.15 19	93.05 38	97.10 6	82.28 79	92.02 157	76.70 140	97.99 40	96.88 34

PS-MVSNAJss		88.31 65	87.90 69	89.56 53	93.31 59	77.96 81	87.94 80	91.97 90	70.73 208	94.19 23	96.67 11	76.94 135	94.57 58	83.07 65	96.28 93	96.15 42

OMC-MVS		88.19 66	87.52 75	90.19 45	91.94 97	81.68 54	87.49 89	93.17 51	76.02 130	88.64 125	91.22 155	84.24 59	93.37 116	77.97 128	97.03 69	95.52 66

TSAR-MVS + MP.		88.14 67	87.82 71	89.09 59	95.72 19	76.74 95	92.49 20	91.19 123	67.85 235	86.63 156	94.84 55	79.58 110	95.96 9	87.62 15	94.50 156	94.56 84

RPSCF		88.00 68	86.93 88	91.22 27	90.08 138	89.30 6	89.68 50	91.11 124	79.26 84	89.68 102	94.81 59	82.44 76	87.74 247	76.54 142	88.74 266	96.61 38

AllTest		87.97 69	87.40 79	89.68 49	91.59 102	83.40 45	89.50 56	95.44 5	79.47 80	88.00 136	93.03 108	82.66 73	91.47 170	70.81 180	96.14 100	94.16 98

TranMVSNet+NR-MVSNet		87.86 70	88.76 63	85.18 118	94.02 44	64.13 188	84.38 139	91.29 119	84.88 28	92.06 59	93.84 95	86.45 44	93.73 85	73.22 165	98.66 12	97.69 11

nrg030		87.85 71	88.49 64	85.91 106	90.07 139	69.73 147	87.86 81	94.20 17	74.04 156	92.70 49	94.66 60	85.88 50	91.50 169	79.72 108	97.32 62	96.50 40

CNVR-MVS		87.81 72	87.68 74	88.21 68	92.87 70	77.30 90	85.25 125	91.23 121	77.31 116	87.07 150	91.47 151	82.94 70	94.71 54	84.67 45	96.27 95	92.62 149

HQP_MVS		87.75 73	87.43 78	88.70 63	93.45 54	76.42 99	89.45 58	93.61 34	79.44 82	86.55 157	92.95 112	74.84 159	95.22 40	80.78 92	95.83 114	94.46 89

NCCC		87.36 74	86.87 89	88.83 61	92.32 86	78.84 75	86.58 111	91.09 125	78.77 93	84.85 183	90.89 173	80.85 99	95.29 36	81.14 86	95.32 126	92.34 156

v13		87.31 75	88.10 66	84.94 120	88.84 157	63.75 192	87.85 83	91.47 107	79.12 86	93.72 29	95.82 27	75.20 153	93.58 98	84.76 44	96.16 98	97.48 16

DeepPCF-MVS	81.24 5	87.28 76	86.21 100	90.49 39	91.48 109	84.90 34	83.41 169	92.38 79	70.25 213	89.35 115	90.68 180	82.85 71	94.57 58	79.55 110	95.95 108	92.00 165

SixPastTwentyTwo		87.20 77	87.45 77	86.45 92	92.52 78	69.19 155	87.84 84	88.05 188	81.66 59	94.64 16	96.53 14	65.94 234	94.75 53	83.02 67	96.83 75	95.41 68

v12		87.15 78	87.91 68	84.84 122	88.69 160	63.52 195	87.58 86	91.46 108	78.74 94	93.57 32	95.66 31	74.94 157	93.57 99	84.50 48	96.08 103	97.43 17

v11		86.96 79	87.78 72	84.51 132	88.50 166	62.60 215	87.21 92	91.63 99	78.08 103	93.40 34	95.56 36	75.07 154	93.57 99	84.46 49	96.08 103	97.36 20

V9		86.96 79	87.70 73	84.74 126	88.52 165	63.27 201	87.31 91	91.45 110	78.28 99	93.43 33	95.45 38	74.59 165	93.57 99	84.23 52	96.01 106	97.38 18

UniMVSNet (Re)		86.87 81	86.98 86	86.55 90	93.11 65	68.48 158	83.80 154	92.87 63	80.37 70	89.61 109	91.81 143	77.72 124	94.18 67	75.00 151	98.53 17	96.99 33

Vis-MVSNet		86.86 82	86.58 93	87.72 76	92.09 91	77.43 87	87.35 90	92.09 85	78.87 91	84.27 200	94.05 84	78.35 119	93.65 88	80.54 97	91.58 216	92.08 164
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 83	87.06 83	86.17 103	92.86 72	67.02 167	82.55 194	91.56 100	83.08 42	90.92 77	91.82 142	78.25 120	93.99 75	74.16 154	98.35 23	97.49 15

DU-MVS		86.80 84	86.99 85	86.21 100	93.24 61	67.02 167	83.16 178	92.21 82	81.73 58	90.92 77	91.97 136	77.20 129	93.99 75	74.16 154	98.35 23	97.61 12

V14		86.75 85	87.46 76	84.62 130	88.35 170	63.00 206	87.02 98	91.42 113	77.78 105	93.27 35	95.23 45	74.22 168	93.56 103	83.95 55	95.93 109	97.31 21

Regformer-2		86.74 86	86.08 102	88.73 62	84.18 271	79.20 71	83.52 164	89.33 171	83.33 38	89.92 96	85.07 270	83.23 68	93.16 126	83.39 60	92.72 200	93.83 108

IS-MVSNet		86.66 87	86.82 92	86.17 103	92.05 93	66.87 169	91.21 34	88.64 178	86.30 23	89.60 110	92.59 121	69.22 220	94.91 50	73.89 158	97.89 44	96.72 35

v15		86.56 88	87.25 80	84.51 132	88.15 177	62.72 211	86.72 108	91.40 115	77.38 110	93.11 37	95.00 50	73.93 173	93.55 104	83.67 59	95.86 113	97.26 22

v10		86.54 89	87.10 82	84.84 122	88.16 176	63.28 200	86.64 110	92.20 83	75.42 144	92.81 46	94.50 66	74.05 171	94.06 74	83.88 56	96.28 93	97.17 27

pmmvs6		86.52 90	88.06 67	81.90 193	92.22 89	62.28 225	84.66 133	89.15 173	83.54 37	89.85 97	97.32 3	88.08 28	86.80 263	70.43 187	97.30 63	96.62 37

Regformer-4		86.41 91	85.71 107	88.52 64	84.27 267	77.57 85	84.07 143	88.00 190	82.82 45	89.84 98	85.48 260	82.06 83	92.77 141	83.83 58	91.04 231	95.22 76

PHI-MVS		86.38 92	85.81 105	88.08 70	88.44 169	77.34 88	89.35 60	93.05 55	73.15 174	84.76 184	87.70 231	78.87 114	94.18 67	80.67 95	96.29 92	92.73 140

v17		86.32 93	86.95 87	84.44 136	88.00 180	62.62 214	86.74 106	91.48 104	77.17 118	92.74 47	94.56 62	73.74 177	93.53 105	83.27 62	94.87 145	97.18 26

test_prior3		86.31 94	86.31 97	86.32 94	90.59 129	71.99 131	83.37 170	92.85 64	75.43 142	84.58 190	91.57 147	81.92 89	94.17 69	79.54 111	96.97 70	92.80 138

CSCG		86.26 95	86.47 95	85.60 113	90.87 124	74.26 111	87.98 79	91.85 93	80.35 71	89.54 113	88.01 225	79.09 112	92.13 153	75.51 146	95.06 137	90.41 207

v16		86.24 96	86.85 90	84.43 137	87.96 182	62.59 216	86.73 107	91.48 104	77.17 118	92.67 50	94.55 63	73.63 178	93.52 106	83.26 63	94.16 161	97.17 27

DeepC-MVS_fast	80.27 8	86.23 97	85.65 109	87.96 75	91.30 112	76.92 92	87.19 93	91.99 89	70.56 209	84.96 179	90.69 179	80.01 107	95.14 43	78.37 121	95.78 116	91.82 171
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 98	86.83 91	84.36 140	87.82 188	62.35 220	86.42 113	91.33 118	76.78 123	92.73 48	94.48 67	73.41 185	93.72 86	83.10 64	95.41 123	97.01 32

Anonymous20240529		86.20 99	87.13 81	83.42 171	90.19 136	64.55 186	84.55 136	90.71 130	85.85 24	89.94 92	95.24 44	82.13 81	90.40 200	69.19 197	96.40 89	95.31 71

CDPH-MVS		86.17 100	85.54 110	88.05 74	92.25 87	75.45 104	83.85 151	92.01 88	65.91 247	86.19 164	91.75 145	83.77 62	94.98 48	77.43 133	96.71 78	93.73 114

Regformer-1		86.00 101	85.50 111	87.49 80	84.18 271	76.90 93	83.52 164	87.94 192	82.18 53	89.19 116	85.07 270	82.28 79	91.89 161	82.40 74	92.72 200	93.69 116

NR-MVSNet		86.00 101	86.22 99	85.34 116	93.24 61	64.56 185	82.21 205	90.46 137	80.99 65	88.42 130	91.97 136	77.56 126	93.85 82	72.46 173	98.65 13	97.61 12

v18		85.99 103	86.55 94	84.30 142	87.73 194	62.29 224	86.40 114	91.49 103	76.64 124	92.40 55	94.20 79	73.28 189	93.52 106	82.87 68	93.99 165	97.09 29

train_agg		85.98 104	85.28 114	88.07 71	92.34 84	79.70 67	83.94 147	90.32 142	65.79 248	84.49 192	90.97 169	81.93 87	93.63 90	81.21 84	96.54 83	90.88 193

FC-MVSNet-test		85.93 105	87.05 84	82.58 184	92.25 87	56.44 276	85.75 119	93.09 53	77.33 115	91.94 63	94.65 61	74.78 161	93.41 115	75.11 150	98.58 15	97.88 9

Effi-MVS+-dtu		85.82 106	83.38 157	93.14 3	87.13 213	91.15 3	87.70 85	88.42 180	74.57 152	83.56 207	85.65 257	78.49 117	94.21 66	72.04 176	92.88 195	94.05 101

agg_prior3		85.76 107	84.95 120	88.16 69	92.43 81	79.92 63	83.98 146	90.03 157	65.11 257	83.66 205	90.64 184	81.00 98	93.67 87	81.21 84	96.54 83	90.88 193

agg_prior1		85.72 108	85.20 115	87.28 84	91.58 105	77.69 83	83.69 158	90.30 146	66.29 243	84.32 196	91.07 166	82.13 81	93.18 124	81.02 87	96.36 90	90.98 188

TAPA-MVS	77.73 12	85.71 109	84.83 122	88.37 66	88.78 159	79.72 66	87.15 95	93.50 37	69.17 221	85.80 170	89.56 202	80.76 100	92.13 153	73.21 169	95.51 121	93.25 127
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		85.50 110	86.14 101	83.58 164	87.97 181	67.13 166	87.55 87	94.32 12	73.44 165	88.47 129	87.54 234	86.45 44	91.06 181	75.76 145	93.76 172	92.54 151

EPP-MVSNet		85.47 111	85.04 117	86.77 87	91.52 108	69.37 150	91.63 29	87.98 191	81.51 61	87.05 151	91.83 141	66.18 233	95.29 36	70.75 182	96.89 72	95.64 60

FIs		85.35 112	86.27 98	82.60 183	91.86 98	57.31 268	85.10 127	93.05 55	75.83 135	91.02 76	93.97 87	73.57 182	92.91 139	73.97 157	98.02 38	97.58 14

K. test v3		85.14 113	84.73 123	86.37 93	91.13 119	69.63 149	85.45 123	76.68 275	84.06 35	92.44 54	96.99 8	62.03 246	94.65 55	80.58 96	93.24 188	94.83 82

EI-MVSNet-Vis-set		85.12 114	84.53 135	86.88 85	84.01 273	72.76 119	83.91 150	85.18 227	80.44 69	88.75 123	85.49 259	80.08 106	91.92 159	82.02 77	90.85 241	95.97 49

Regformer-3		85.06 115	84.67 128	86.22 98	84.27 267	73.43 115	84.07 143	85.26 225	80.77 68	88.62 126	85.48 260	80.56 103	90.39 201	81.99 78	91.04 231	94.85 81

EI-MVSNet-UG-set		85.04 116	84.44 137	86.85 86	83.87 276	72.52 122	83.82 152	85.15 228	80.27 73	88.75 123	85.45 263	79.95 108	91.90 160	81.92 79	90.80 242	96.13 43

X-MVStestdata		85.04 116	82.70 165	92.08 8	95.64 21	86.25 16	92.64 15	93.33 41	85.07 26	89.99 89	16.05 366	86.57 42	95.80 15	87.35 20	97.62 55	94.20 95

MSLP-MVS++		85.00 118	86.03 103	81.90 193	91.84 99	71.56 139	86.75 105	93.02 59	75.95 133	87.12 147	89.39 204	77.98 121	89.40 221	77.46 131	94.78 148	84.75 276

F-COLMAP		84.97 119	83.42 156	89.63 51	92.39 82	83.40 45	88.83 68	91.92 92	73.19 173	80.18 261	89.15 207	77.04 133	93.28 121	65.82 224	92.28 206	92.21 162

MVS_0304		84.88 120	83.96 150	87.64 79	87.43 202	74.83 107	84.18 141	93.30 44	77.48 109	77.39 281	88.46 216	74.53 167	95.74 19	78.09 127	94.75 152	92.36 155

v7		84.81 121	85.00 118	84.23 144	88.15 177	63.27 201	83.79 155	91.39 116	71.10 206	90.07 86	91.28 153	74.04 172	93.63 90	81.48 83	93.67 175	95.79 53

3Dnovator	80.37 7	84.80 122	84.71 126	85.06 119	86.36 229	74.71 108	88.77 70	90.00 158	75.65 140	84.96 179	93.17 105	74.06 170	91.19 177	78.28 124	91.09 229	89.29 224

IterMVS-LS		84.73 123	84.98 119	83.96 152	87.35 203	63.66 193	83.25 175	89.88 161	76.06 128	89.62 107	92.37 129	73.40 187	92.52 147	78.16 125	94.77 150	95.69 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 124	84.34 144	85.49 115	90.18 137	75.86 103	79.23 262	87.13 203	73.35 166	85.56 174	89.34 205	83.60 64	90.50 198	76.64 141	94.05 164	90.09 215

HQP-MVS		84.61 125	84.06 147	86.27 96	91.19 115	70.66 143	84.77 129	92.68 70	73.30 169	80.55 256	90.17 194	72.10 206	94.61 56	77.30 134	94.47 157	93.56 121

v1192		84.57 126	84.69 127	84.21 145	87.75 193	62.88 208	83.02 180	91.43 111	69.08 223	89.98 91	90.89 173	72.70 200	93.62 94	82.41 73	94.97 140	96.13 43

mvs-test1		84.55 127	82.12 176	91.84 17	87.13 213	89.54 5	85.05 128	88.42 180	74.57 152	80.60 253	82.98 294	78.49 117	93.98 77	72.04 176	89.77 255	92.00 165

FMVSNet1		84.55 127	85.45 112	81.85 196	90.27 135	61.05 240	86.83 101	88.27 185	78.57 96	89.66 103	95.64 33	75.43 150	90.68 193	69.09 198	95.33 125	93.82 110

v1144		84.54 129	84.72 125	84.00 150	87.67 196	62.55 217	82.97 182	90.93 128	70.32 212	89.80 99	90.99 168	73.50 183	93.48 110	81.69 82	94.65 154	95.97 49

Gipuma		84.44 130	86.33 96	78.78 234	84.20 270	73.57 114	89.55 53	90.44 138	84.24 32	84.38 194	94.89 53	76.35 145	80.40 310	76.14 143	96.80 77	82.36 308
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1neww		84.43 131	84.66 129	83.75 157	87.81 189	62.34 221	83.59 160	90.27 150	72.33 185	89.93 94	91.22 155	73.28 189	93.29 118	80.25 102	93.25 186	95.62 61

v7new		84.43 131	84.66 129	83.75 157	87.81 189	62.34 221	83.59 160	90.27 150	72.33 185	89.93 94	91.22 155	73.28 189	93.29 118	80.25 102	93.25 186	95.62 61

v6		84.43 131	84.66 129	83.75 157	87.81 189	62.34 221	83.59 160	90.26 152	72.33 185	89.94 92	91.19 159	73.30 188	93.29 118	80.26 101	93.26 185	95.62 61

testing_2		84.36 134	84.64 132	83.50 170	86.74 223	63.97 191	84.56 135	90.31 144	66.22 244	91.62 67	94.55 63	75.88 147	91.95 158	77.02 138	94.89 142	94.56 84

MCST-MVS		84.36 134	83.93 151	85.63 112	91.59 102	71.58 138	83.52 164	92.13 84	61.82 277	83.96 201	89.75 199	79.93 109	93.46 111	78.33 123	94.34 159	91.87 170

VDDNet		84.35 136	85.39 113	81.25 205	95.13 30	59.32 256	85.42 124	81.11 253	86.41 22	87.41 144	96.21 21	73.61 181	90.61 196	66.33 218	96.85 73	93.81 113

v1240		84.30 137	84.51 136	83.65 162	87.65 197	61.26 237	82.85 185	91.54 101	67.94 233	90.68 82	90.65 182	71.71 212	93.64 89	82.84 70	94.78 148	96.07 45

MVS_111021_LR		84.28 138	83.76 152	85.83 110	89.23 150	83.07 48	80.99 231	83.56 237	72.71 179	86.07 165	89.07 208	81.75 92	86.19 273	77.11 136	93.36 181	88.24 234

v144192		84.24 139	84.41 138	83.71 161	87.59 199	61.57 233	82.95 183	91.03 126	67.82 236	89.80 99	90.49 185	73.28 189	93.51 109	81.88 80	94.89 142	96.04 47

v1921920		84.23 140	84.37 143	83.79 155	87.64 198	61.71 228	82.91 184	91.20 122	67.94 233	90.06 87	90.34 187	72.04 209	93.59 95	82.32 75	94.91 141	96.07 45

VDD-MVS		84.23 140	84.58 134	83.20 174	91.17 118	65.16 181	83.25 175	84.97 233	79.79 76	87.18 146	94.27 75	74.77 162	90.89 187	69.24 194	96.54 83	93.55 123

v1141		84.16 142	84.38 140	83.52 167	87.32 205	61.70 230	82.79 187	89.74 162	71.90 199	89.64 104	91.12 162	72.68 201	93.10 129	80.39 100	93.80 170	95.75 55

divwei89l23v2f112		84.16 142	84.38 140	83.52 167	87.32 205	61.70 230	82.79 187	89.74 162	71.90 199	89.64 104	91.12 162	72.68 201	93.10 129	80.40 98	93.81 169	95.75 55

v1		84.16 142	84.38 140	83.52 167	87.33 204	61.71 228	82.79 187	89.73 164	71.89 201	89.64 104	91.11 164	72.72 198	93.10 129	80.40 98	93.79 171	95.75 55

v2v482		84.09 145	84.24 145	83.62 163	87.13 213	61.40 234	82.71 191	89.71 165	72.19 189	89.55 111	91.41 152	70.70 217	93.20 123	81.02 87	93.76 172	96.25 41

EG-PatchMatch MVS		84.08 146	84.11 146	83.98 151	92.22 89	72.61 121	82.20 207	87.02 207	72.63 180	88.86 120	91.02 167	78.52 116	91.11 179	73.41 164	91.09 229	88.21 235

DP-MVS Recon		84.05 147	83.22 159	86.52 91	91.73 100	75.27 105	83.23 177	92.40 77	72.04 190	82.04 227	88.33 221	77.91 123	93.95 80	66.17 219	95.12 135	90.34 209

TransMVSNet (Re)		84.02 148	85.74 106	78.85 233	91.00 121	55.20 286	82.29 201	87.26 198	79.65 79	88.38 132	95.52 37	83.00 69	86.88 255	67.97 208	96.60 81	94.45 91

Baseline_NR-MVSNet		84.00 149	85.90 104	78.29 243	91.47 110	53.44 296	82.29 201	87.00 208	79.06 88	89.55 111	95.72 30	77.20 129	86.14 274	72.30 174	98.51 18	95.28 72

TSAR-MVS + GP.		83.95 150	82.69 166	87.72 76	89.27 149	81.45 56	83.72 157	81.58 252	74.73 149	85.66 171	86.06 255	72.56 204	92.69 143	75.44 148	95.21 130	89.01 231

alignmvs		83.94 151	83.98 149	83.80 154	87.80 192	67.88 163	84.54 137	91.42 113	73.27 172	88.41 131	87.96 226	72.33 205	90.83 188	76.02 144	94.11 162	92.69 142

Effi-MVS+		83.90 152	84.01 148	83.57 165	87.22 211	65.61 178	86.55 112	92.40 77	78.64 95	81.34 239	84.18 281	83.65 63	92.93 137	74.22 153	87.87 276	92.17 163

CANet		83.79 153	82.85 164	86.63 88	86.17 237	72.21 128	83.76 156	91.43 111	77.24 117	74.39 305	87.45 235	75.36 151	95.42 32	77.03 137	92.83 196	92.25 161

pm-mvs1		83.69 154	84.95 120	79.91 220	90.04 142	59.66 253	82.43 196	87.44 195	75.52 141	87.85 138	95.26 43	81.25 97	85.65 280	68.74 202	96.04 105	94.42 92

AdaColmap		83.66 155	83.69 153	83.57 165	90.05 141	72.26 126	86.29 115	90.00 158	78.19 101	81.65 234	87.16 238	83.40 66	94.24 65	61.69 250	94.76 151	84.21 283

casdiffmvs1		83.63 156	83.44 155	84.20 147	88.08 179	66.53 172	88.62 73	92.02 87	58.66 293	82.94 216	93.84 95	78.76 115	93.10 129	76.73 139	91.29 226	92.96 134

MIMVSNet1		83.63 156	84.59 133	80.74 211	94.06 43	62.77 210	82.72 190	84.53 235	77.57 108	90.34 83	95.92 26	76.88 141	85.83 278	61.88 248	97.42 60	93.62 118

WR-MVS		83.56 158	84.40 139	81.06 208	93.43 56	54.88 287	78.67 268	85.02 231	81.24 62	90.74 81	91.56 149	72.85 196	91.08 180	68.00 207	98.04 35	97.23 24

CNLPA		83.55 159	83.10 162	84.90 121	89.34 148	83.87 43	84.54 137	88.77 175	79.09 87	83.54 208	88.66 214	74.87 158	81.73 306	66.84 215	92.29 205	89.11 225

LCM-MVSNet-Re		83.48 160	85.06 116	78.75 235	85.94 245	55.75 281	80.05 239	94.27 13	76.47 125	96.09 5	94.54 65	83.31 67	89.75 216	59.95 262	94.89 142	90.75 197

V42		83.47 161	83.37 158	83.75 157	83.16 282	63.33 199	81.31 223	90.23 153	69.51 218	90.91 79	90.81 176	74.16 169	92.29 151	80.06 104	90.22 251	95.62 61

VPA-MVSNet		83.47 161	84.73 123	79.69 224	90.29 134	57.52 267	81.30 225	88.69 177	76.29 126	87.58 141	94.44 68	80.60 102	87.20 251	66.60 217	96.82 76	94.34 94

PAPM_NR		83.23 163	83.19 161	83.33 172	90.90 123	65.98 175	88.19 77	90.78 129	78.13 102	80.87 244	87.92 229	73.49 184	92.42 148	70.07 188	88.40 267	91.60 178

CLD-MVS		83.18 164	82.64 167	84.79 124	89.05 152	67.82 164	77.93 275	92.52 75	68.33 228	85.07 178	81.54 318	82.06 83	92.96 135	69.35 193	97.91 43	93.57 120
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 165	85.68 108	75.65 278	81.24 295	45.26 346	79.94 242	92.91 62	83.83 36	91.33 73	96.88 10	80.25 105	85.92 276	68.89 200	95.89 111	95.76 54

114514_t		83.10 166	82.54 170	84.77 125	92.90 69	69.10 156	86.65 109	90.62 134	54.66 313	81.46 236	90.81 176	76.98 134	94.38 61	72.62 172	96.18 97	90.82 196

casdiffmvs		82.99 167	82.51 171	84.42 138	86.34 230	67.92 162	87.86 81	92.28 81	60.95 284	81.12 240	93.08 106	76.07 146	93.43 114	79.41 115	85.45 297	91.93 169

diffmvs1		82.95 168	83.63 154	80.90 209	80.05 307	61.05 240	82.98 181	89.93 160	74.72 150	82.37 222	92.93 114	76.47 143	88.80 231	81.73 81	91.54 217	92.85 135

UGNet		82.78 169	81.64 182	86.21 100	86.20 236	76.24 102	86.86 99	85.68 219	77.07 120	73.76 308	92.82 116	69.64 218	91.82 164	69.04 199	93.69 174	90.56 203
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		82.75 170	81.93 180	85.19 117	82.08 287	80.15 62	85.53 122	88.76 176	68.01 230	85.58 173	87.75 230	71.80 211	86.85 256	74.02 156	93.87 168	88.58 233

EI-MVSNet		82.61 171	82.42 173	83.20 174	83.25 280	63.66 193	83.50 167	85.07 229	76.06 128	86.55 157	85.10 268	73.41 185	90.25 202	78.15 126	90.67 245	95.68 59

QAPM		82.59 172	82.59 169	82.58 184	86.44 224	66.69 170	89.94 45	90.36 141	67.97 232	84.94 181	92.58 123	72.71 199	92.18 152	70.63 185	87.73 278	88.85 232

Fast-Effi-MVS+-dtu		82.54 173	81.41 185	85.90 107	85.60 246	76.53 98	83.07 179	89.62 167	73.02 176	79.11 269	83.51 287	80.74 101	90.24 204	68.76 201	89.29 258	90.94 190

MVS_Test		82.47 174	83.22 159	80.22 217	82.62 286	57.75 266	82.54 195	91.96 91	71.16 205	82.89 217	92.52 125	77.41 128	90.50 198	80.04 105	87.84 277	92.40 154

v148		82.31 175	82.48 172	81.81 199	85.59 247	59.66 253	81.47 221	86.02 216	72.85 177	88.05 135	90.65 182	70.73 216	90.91 186	75.15 149	91.79 212	94.87 79

API-MVS		82.28 176	82.61 168	81.30 203	86.29 232	69.79 146	88.71 71	87.67 194	78.42 98	82.15 226	84.15 283	77.98 121	91.59 168	65.39 226	92.75 197	82.51 307

MVSFormer		82.23 177	81.57 184	84.19 148	85.54 248	69.26 152	91.98 25	90.08 155	71.54 202	76.23 288	85.07 270	58.69 264	94.27 62	86.26 29	88.77 264	89.03 229

PCF-MVS	74.62 15	82.15 178	80.92 193	85.84 109	89.43 146	72.30 125	80.53 235	91.82 94	57.36 301	87.81 139	89.92 197	77.67 125	93.63 90	58.69 274	95.08 136	91.58 179
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 179	80.31 198	87.45 81	90.86 125	80.29 61	85.88 117	90.65 132	68.17 229	76.32 287	86.33 251	73.12 194	92.61 146	61.40 254	90.02 253	89.44 219
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		82.02 180	82.07 177	81.85 196	86.38 226	61.05 240	86.83 101	88.27 185	72.43 181	86.00 166	95.64 33	63.78 241	90.68 193	65.95 220	93.34 182	93.82 110

test1		82.02 180	82.07 177	81.85 196	86.38 226	61.05 240	86.83 101	88.27 185	72.43 181	86.00 166	95.64 33	63.78 241	90.68 193	65.95 220	93.34 182	93.82 110

OpenMVS	76.72 13	81.98 182	82.00 179	81.93 192	84.42 263	68.22 160	88.50 75	89.48 169	66.92 239	81.80 233	91.86 138	72.59 203	90.16 207	71.19 179	91.25 227	87.40 246

tfpnnormal		81.79 183	82.95 163	78.31 242	88.93 156	55.40 282	80.83 234	82.85 241	76.81 122	85.90 169	94.14 81	74.58 166	86.51 268	66.82 216	95.68 120	93.01 132

diffmvs		81.78 184	82.36 174	80.02 219	79.06 315	59.93 250	83.30 172	88.41 182	73.47 163	78.38 273	92.05 135	75.85 148	88.38 241	80.73 94	89.98 254	91.76 173

PVSNet_Blended_VisFu		81.55 185	80.49 197	84.70 129	91.58 105	73.24 117	84.21 140	91.67 98	62.86 269	80.94 242	87.16 238	67.27 228	92.87 140	69.82 190	88.94 263	87.99 239

DELS-MVS		81.44 186	81.25 187	82.03 191	84.27 267	62.87 209	76.47 290	92.49 76	70.97 207	81.64 235	83.83 284	75.03 155	92.70 142	74.29 152	92.22 210	90.51 205
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		81.31 187	81.13 190	81.88 195	84.89 255	63.05 205	82.37 198	90.50 136	62.75 270	89.00 119	88.29 222	67.55 227	91.68 166	73.55 162	91.24 228	90.89 192

FMVSNet2		81.31 187	81.61 183	80.41 215	86.38 226	58.75 262	83.93 149	86.58 211	72.43 181	87.65 140	92.98 110	63.78 241	90.22 205	66.86 213	93.92 167	92.27 159

DI_MVS_plusplus_test		81.27 189	81.26 186	81.29 204	84.98 253	61.65 232	81.98 210	87.25 199	63.56 262	87.56 142	89.60 201	73.62 179	91.83 163	72.20 175	90.59 249	90.38 208

TinyColmap		81.25 190	82.34 175	77.99 248	85.33 251	60.68 245	82.32 200	88.33 183	71.26 204	86.97 152	92.22 134	77.10 132	86.98 254	62.37 243	95.17 132	86.31 256

test_normal		81.23 191	81.16 189	81.43 202	84.77 258	61.99 227	81.46 222	86.95 209	63.16 267	87.22 145	89.63 200	73.62 179	91.65 167	72.92 170	90.70 244	90.65 201

tttt0517		81.07 192	79.58 206	85.52 114	88.99 155	66.45 173	87.03 97	75.51 282	73.76 160	88.32 134	90.20 191	37.96 354	94.16 72	79.36 116	95.13 133	95.93 52

Fast-Effi-MVS+		81.04 193	80.57 194	82.46 188	87.50 200	63.22 203	78.37 271	89.63 166	68.01 230	81.87 229	82.08 313	82.31 78	92.65 145	67.10 211	88.30 272	91.51 181

BH-untuned		80.96 194	80.99 191	80.84 210	88.55 164	68.23 159	80.33 237	88.46 179	72.79 178	86.55 157	86.76 242	74.72 163	91.77 165	61.79 249	88.99 262	82.52 306

1121		80.86 195	79.81 205	84.02 149	93.93 46	78.70 76	81.64 218	80.18 258	55.43 310	83.67 204	91.15 160	71.29 214	91.41 174	67.95 209	93.06 191	81.96 314

xiu_mvs_v1_base_debu		80.84 196	80.14 201	82.93 179	88.31 171	71.73 134	79.53 246	87.17 200	65.43 252	79.59 263	82.73 301	76.94 135	90.14 208	73.22 165	88.33 268	86.90 251

xiu_mvs_v1_base		80.84 196	80.14 201	82.93 179	88.31 171	71.73 134	79.53 246	87.17 200	65.43 252	79.59 263	82.73 301	76.94 135	90.14 208	73.22 165	88.33 268	86.90 251

xiu_mvs_v1_base_debi		80.84 196	80.14 201	82.93 179	88.31 171	71.73 134	79.53 246	87.17 200	65.43 252	79.59 263	82.73 301	76.94 135	90.14 208	73.22 165	88.33 268	86.90 251

BH-RMVSNet		80.53 199	80.22 200	81.49 201	87.19 212	66.21 174	77.79 277	86.23 213	74.21 155	83.69 203	88.50 215	73.25 193	90.75 190	63.18 240	87.90 275	87.52 244

Anonymous202405211		80.51 200	81.19 188	78.49 240	88.48 167	57.26 269	76.63 286	82.49 243	81.21 63	84.30 198	92.24 133	67.99 225	86.24 272	62.22 244	95.13 133	91.98 168

EPNet		80.37 201	78.41 214	86.23 97	76.75 331	73.28 116	87.18 94	77.45 269	76.24 127	68.14 332	88.93 210	65.41 236	93.85 82	69.47 192	96.12 102	91.55 180
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		80.32 202	80.94 192	78.47 241	88.18 174	52.62 303	82.29 201	85.01 232	72.01 191	79.24 268	92.54 124	69.36 219	93.36 117	70.65 184	89.19 261	89.45 218

VPNet		80.25 203	81.68 181	75.94 276	92.46 80	47.98 341	76.70 285	81.67 251	73.45 164	84.87 182	92.82 116	74.66 164	86.51 268	61.66 251	96.85 73	93.33 124

MAR-MVS		80.24 204	78.74 210	84.73 127	86.87 222	78.18 79	85.75 119	87.81 193	65.67 251	77.84 276	78.50 333	73.79 176	90.53 197	61.59 253	90.87 240	85.49 265
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		80.20 205	79.00 208	83.78 156	88.17 175	86.66 13	81.31 223	66.81 346	69.64 217	88.33 133	90.19 192	64.58 237	83.63 298	71.99 178	90.03 252	81.06 332

LFMVS		80.15 206	80.56 195	78.89 232	89.19 151	55.93 278	85.22 126	73.78 294	82.96 43	84.28 199	92.72 120	57.38 274	90.07 212	63.80 235	95.75 117	90.68 199

MSDG		80.06 207	79.99 204	80.25 216	83.91 275	68.04 161	77.51 280	89.19 172	77.65 106	81.94 228	83.45 289	76.37 144	86.31 271	63.31 239	86.59 287	86.41 254

ab-mvs		79.67 208	80.56 195	76.99 261	88.48 167	56.93 271	84.70 132	86.06 215	68.95 225	80.78 245	93.08 106	75.30 152	84.62 289	56.78 288	90.90 239	89.43 220

VNet		79.31 209	80.27 199	76.44 269	87.92 183	53.95 291	75.58 297	84.35 236	74.39 154	82.23 224	90.72 178	72.84 197	84.39 291	60.38 261	93.98 166	90.97 189

thisisatest0530		79.07 210	77.33 220	84.26 143	87.13 213	64.58 184	83.66 159	75.95 277	68.86 226	85.22 177	87.36 237	38.10 353	93.57 99	75.47 147	94.28 160	94.62 83

PAPR		78.84 211	78.10 215	81.07 207	85.17 252	60.22 248	82.21 205	90.57 135	62.51 272	75.32 298	84.61 277	74.99 156	92.30 150	59.48 272	88.04 274	90.68 199

PVSNet_BlendedMVS		78.80 212	77.84 216	81.65 200	84.43 261	63.41 196	79.49 249	90.44 138	61.70 280	75.43 296	87.07 240	69.11 221	91.44 172	60.68 259	92.24 208	90.11 214

FMVSNet3		78.80 212	78.55 211	79.57 226	82.89 284	56.89 273	81.76 215	85.77 218	69.04 224	86.00 166	90.44 186	51.75 293	90.09 211	65.95 220	93.34 182	91.72 174

0601test		78.71 214	78.51 212	79.32 229	84.32 265	58.84 259	78.38 269	85.33 223	75.99 131	82.49 220	86.57 243	58.01 267	90.02 213	62.74 241	92.73 198	89.10 226

Anonymous20240521		78.71 214	78.51 212	79.32 229	84.32 265	58.84 259	78.38 269	85.33 223	75.99 131	82.49 220	86.57 243	58.01 267	90.02 213	62.74 241	92.73 198	89.10 226

pmmvs-eth3d		78.42 216	77.04 222	82.57 186	87.44 201	74.41 110	80.86 233	79.67 261	55.68 308	84.69 185	90.31 190	60.91 250	85.42 281	62.20 245	91.59 215	87.88 242

mvs_anonymous		78.13 217	78.76 209	76.23 273	79.24 313	50.31 328	78.69 267	84.82 234	61.60 281	83.09 215	92.82 116	73.89 175	87.01 252	68.33 206	86.41 289	91.37 182

TAMVS		78.08 218	76.36 231	83.23 173	90.62 128	72.87 118	79.08 263	80.01 260	61.72 279	81.35 238	86.92 241	63.96 240	88.78 236	50.61 318	93.01 193	88.04 238

Vis-MVSNet (Re-imp)		77.82 219	77.79 217	77.92 249	88.82 158	51.29 316	83.28 173	71.97 310	74.04 156	82.23 224	89.78 198	57.38 274	89.41 220	57.22 286	95.41 123	93.05 131

CANet_DTU		77.81 220	77.05 221	80.09 218	81.37 294	59.90 251	83.26 174	88.29 184	69.16 222	67.83 335	83.72 285	60.93 249	89.47 217	69.22 196	89.70 256	90.88 193

OpenMVS_ROB	70.19 17	77.77 221	77.46 218	78.71 236	84.39 264	61.15 238	81.18 227	82.52 242	62.45 274	83.34 209	87.37 236	66.20 232	88.66 239	64.69 230	85.02 304	86.32 255

MDA-MVSNet-bldmvs		77.47 222	76.90 223	79.16 231	79.03 316	64.59 183	66.58 337	75.67 280	73.15 174	88.86 120	88.99 209	66.94 229	81.23 307	64.71 229	88.22 273	91.64 177

jason		77.42 223	75.75 237	82.43 189	87.10 217	69.27 151	77.99 274	81.94 249	51.47 333	77.84 276	85.07 270	60.32 253	89.00 228	70.74 183	89.27 260	89.03 229
jason: jason.
CDS-MVSNet		77.32 224	75.40 240	83.06 176	89.00 154	72.48 123	77.90 276	82.17 246	60.81 285	78.94 270	83.49 288	59.30 261	88.76 237	54.64 304	92.37 204	87.93 241
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		77.19 225	76.75 224	78.52 239	87.01 219	61.30 236	75.55 298	87.12 205	61.24 283	74.45 304	78.79 332	77.20 129	90.93 184	64.62 232	84.80 308	83.32 296

MVSTER		77.09 226	75.70 238	81.25 205	75.27 345	61.08 239	77.49 281	85.07 229	60.78 286	86.55 157	88.68 213	43.14 335	90.25 202	73.69 160	90.67 245	92.42 152

PS-MVSNAJ		77.04 227	76.53 230	78.56 238	87.09 218	61.40 234	75.26 299	87.13 203	61.25 282	74.38 306	77.22 339	76.94 135	90.94 183	64.63 231	84.83 307	83.35 295

IterMVS		76.91 228	76.34 232	78.64 237	80.91 299	64.03 189	76.30 291	79.03 262	64.88 259	83.11 213	89.16 206	59.90 257	84.46 290	68.61 204	85.15 303	87.42 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
view600		76.79 229	76.54 226	77.56 253	87.91 184	50.77 322	81.92 211	71.35 318	77.38 110	84.62 186	88.40 217	45.18 325	89.26 223	58.58 275	93.49 177	92.66 143

view800		76.79 229	76.54 226	77.56 253	87.91 184	50.77 322	81.92 211	71.35 318	77.38 110	84.62 186	88.40 217	45.18 325	89.26 223	58.58 275	93.49 177	92.66 143

conf0.05thres1000		76.79 229	76.54 226	77.56 253	87.91 184	50.77 322	81.92 211	71.35 318	77.38 110	84.62 186	88.40 217	45.18 325	89.26 223	58.58 275	93.49 177	92.66 143

tfpn		76.79 229	76.54 226	77.56 253	87.91 184	50.77 322	81.92 211	71.35 318	77.38 110	84.62 186	88.40 217	45.18 325	89.26 223	58.58 275	93.49 177	92.66 143

TR-MVS		76.77 233	75.79 235	79.72 223	86.10 244	65.79 177	77.14 282	83.02 239	65.20 256	81.40 237	82.10 312	66.30 231	90.73 192	55.57 295	85.27 300	82.65 302

USDC		76.63 234	76.73 225	76.34 271	83.46 278	57.20 270	80.02 240	88.04 189	52.14 328	83.65 206	91.25 154	63.24 244	86.65 267	54.66 303	94.11 162	85.17 267

BH-w/o		76.57 235	76.07 234	78.10 246	86.88 221	65.92 176	77.63 278	86.33 212	65.69 250	80.89 243	79.95 327	68.97 223	90.74 191	53.01 309	85.25 301	77.62 337

Patchmtry		76.56 236	77.46 218	73.83 290	79.37 312	46.60 343	82.41 197	76.90 272	73.81 159	85.56 174	92.38 126	48.07 302	83.98 295	63.36 238	95.31 128	90.92 191

PVSNet_Blended		76.49 237	75.40 240	79.76 222	84.43 261	63.41 196	75.14 300	90.44 138	57.36 301	75.43 296	78.30 334	69.11 221	91.44 172	60.68 259	87.70 279	84.42 279

lupinMVS		76.37 238	74.46 249	82.09 190	85.54 248	69.26 152	76.79 283	80.77 256	50.68 340	76.23 288	82.82 299	58.69 264	88.94 229	69.85 189	88.77 264	88.07 236

cascas		76.29 239	74.81 245	80.72 213	84.47 260	62.94 207	73.89 309	87.34 196	55.94 307	75.16 300	76.53 342	63.97 239	91.16 178	65.00 227	90.97 237	88.06 237

RPMNet		76.06 240	75.79 235	76.85 265	79.58 308	62.64 212	82.58 192	71.75 314	74.80 148	75.72 294	92.59 121	48.69 300	84.07 293	73.48 163	82.91 318	83.85 287

tfpn111		76.03 241	75.53 239	77.53 257	87.27 207	51.88 308	81.07 228	73.26 299	75.68 137	83.25 210	86.37 248	45.54 315	89.38 222	55.07 300	92.26 207	91.34 184

thres600view7		75.97 242	75.35 242	77.85 251	87.01 219	51.84 312	80.45 236	73.26 299	75.20 146	83.10 214	86.31 253	45.54 315	89.05 227	55.03 301	92.24 208	92.66 143

GA-MVS		75.83 243	74.61 246	79.48 228	81.87 289	59.25 257	73.42 312	82.88 240	68.68 227	79.75 262	81.80 315	50.62 295	89.46 218	66.85 214	85.64 296	89.72 216

MVP-Stereo		75.81 244	73.51 264	82.71 182	89.35 147	73.62 113	80.06 238	85.20 226	60.30 288	73.96 307	87.94 227	57.89 271	89.45 219	52.02 312	74.87 345	85.06 269
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
conf200view11		75.62 245	75.05 243	77.34 259	87.27 207	51.88 308	81.07 228	73.26 299	75.68 137	83.25 210	86.37 248	45.54 315	88.80 231	51.98 313	90.99 233	91.34 184

thres100view900		75.45 246	75.05 243	76.66 268	87.27 207	51.88 308	81.07 228	73.26 299	75.68 137	83.25 210	86.37 248	45.54 315	88.80 231	51.98 313	90.99 233	89.31 222

thres400		75.14 247	74.23 251	77.86 250	86.24 233	52.12 305	79.24 259	73.87 292	73.34 167	81.82 231	84.60 278	46.02 309	88.80 231	51.98 313	90.99 233	92.66 143

wuyk23d		75.13 248	79.30 207	62.63 333	75.56 339	75.18 106	80.89 232	73.10 303	75.06 147	94.76 14	95.32 40	87.73 31	52.85 363	34.16 357	97.11 67	59.85 358

EU-MVSNet		75.12 249	74.43 250	77.18 260	83.11 283	59.48 255	85.71 121	82.43 244	39.76 361	85.64 172	88.76 211	44.71 331	87.88 246	73.86 159	85.88 294	84.16 284

HyFIR lowres test		75.12 249	72.66 271	82.50 187	91.44 111	65.19 180	72.47 315	87.31 197	46.79 349	80.29 259	84.30 280	52.70 291	92.10 156	51.88 317	86.73 286	90.22 210

CMPMVS	59.41 20	75.12 249	73.57 257	79.77 221	75.84 337	67.22 165	81.21 226	82.18 245	50.78 338	76.50 284	87.66 232	55.20 285	82.99 300	62.17 247	90.64 248	89.09 228
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 252	72.98 269	80.73 212	84.95 254	71.71 137	76.23 292	77.59 268	52.83 322	77.73 279	86.38 247	56.35 278	84.97 285	57.72 285	87.05 284	85.51 264

tfpn200view9		74.86 253	74.23 251	76.74 267	86.24 233	52.12 305	79.24 259	73.87 292	73.34 167	81.82 231	84.60 278	46.02 309	88.80 231	51.98 313	90.99 233	89.31 222

1112_ss		74.82 254	73.74 254	78.04 247	89.57 145	60.04 249	76.49 289	87.09 206	54.31 314	73.66 309	79.80 328	60.25 254	86.76 265	58.37 279	84.15 311	87.32 247

ppachtmachnet_test		74.73 255	74.00 253	76.90 263	80.71 303	56.89 273	71.53 319	78.42 264	58.24 296	79.32 267	82.92 298	57.91 270	84.26 292	65.60 225	91.36 219	89.56 217

Patchmatch-RL test		74.48 256	73.68 255	76.89 264	84.83 256	66.54 171	72.29 316	69.16 330	57.70 299	86.76 153	86.33 251	45.79 314	82.59 302	69.63 191	90.65 247	81.54 322

PatchMatch-RL		74.48 256	73.22 266	78.27 244	87.70 195	85.26 30	75.92 293	70.09 325	64.34 261	76.09 290	81.25 320	65.87 235	78.07 316	53.86 306	83.82 312	71.48 348

XXY-MVS		74.44 258	76.19 233	69.21 313	84.61 259	52.43 304	71.70 318	77.18 270	60.73 287	80.60 253	90.96 171	75.44 149	69.35 337	56.13 291	88.33 268	85.86 261

conf0.01		74.17 259	73.53 258	76.08 274	86.13 238	50.06 331	79.45 250	68.54 331	72.01 191	80.76 246	82.50 304	41.39 339	86.83 257	59.66 265	91.36 219	91.34 184

conf0.002		74.17 259	73.53 258	76.08 274	86.13 238	50.06 331	79.45 250	68.54 331	72.01 191	80.76 246	82.50 304	41.39 339	86.83 257	59.66 265	91.36 219	91.34 184

CR-MVSNet		74.00 261	73.04 268	76.85 265	79.58 308	62.64 212	82.58 192	76.90 272	50.50 341	75.72 294	92.38 126	48.07 302	84.07 293	68.72 203	82.91 318	83.85 287

Test_1112_low_res		73.90 262	73.08 267	76.35 270	90.35 133	55.95 277	73.40 313	86.17 214	50.70 339	73.14 310	85.94 256	58.31 266	85.90 277	56.51 289	83.22 315	87.20 248

test20.03		73.75 263	74.59 248	71.22 307	81.11 297	51.12 318	70.15 324	72.10 309	70.42 210	80.28 260	91.50 150	64.21 238	74.72 327	46.96 336	94.58 155	87.82 243

thresconf0.02		73.65 264	73.53 258	73.98 285	86.13 238	50.06 331	79.45 250	68.54 331	72.01 191	80.76 246	82.50 304	41.39 339	86.83 257	59.66 265	91.36 219	85.06 269

tfpn_n400		73.65 264	73.53 258	73.98 285	86.13 238	50.06 331	79.45 250	68.54 331	72.01 191	80.76 246	82.50 304	41.39 339	86.83 257	59.66 265	91.36 219	85.06 269

tfpnconf		73.65 264	73.53 258	73.98 285	86.13 238	50.06 331	79.45 250	68.54 331	72.01 191	80.76 246	82.50 304	41.39 339	86.83 257	59.66 265	91.36 219	85.06 269

tfpnview11		73.65 264	73.53 258	73.98 285	86.13 238	50.06 331	79.45 250	68.54 331	72.01 191	80.76 246	82.50 304	41.39 339	86.83 257	59.66 265	91.36 219	85.06 269

tfpn1000		73.63 268	73.58 256	73.79 291	85.46 250	50.31 328	79.99 241	68.18 337	72.33 185	80.66 252	83.05 292	39.80 350	86.74 266	60.96 257	91.78 213	84.32 281

1314		73.22 269	72.56 274	75.20 279	80.41 306	57.84 264	81.64 218	85.36 222	51.68 331	73.10 311	76.65 341	61.45 248	85.19 283	63.54 236	79.21 336	82.59 303

MVS		73.21 270	72.59 273	75.06 280	80.97 298	60.81 244	81.64 218	85.92 217	46.03 352	71.68 318	77.54 336	68.47 224	89.77 215	55.70 294	85.39 298	74.60 343

HY-MVS	64.64 18	73.03 271	72.47 275	74.71 282	83.36 279	54.19 289	82.14 208	81.96 247	56.76 306	69.57 328	86.21 254	60.03 255	84.83 288	49.58 324	82.65 320	85.11 268

thisisatest0515		73.00 272	70.52 288	80.46 214	81.45 293	59.90 251	73.16 314	74.31 291	57.86 298	76.08 291	77.78 335	37.60 355	92.12 155	65.00 227	91.45 218	89.35 221

EPNet_dtu		72.87 273	71.33 285	77.49 258	77.72 325	60.55 246	82.35 199	75.79 278	66.49 242	58.39 362	81.06 321	53.68 289	85.98 275	53.55 307	92.97 194	85.95 259
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test1		72.75 274	72.61 272	73.19 295	81.62 291	55.86 279	78.89 265	71.37 317	61.73 278	74.93 301	82.15 311	60.46 252	81.80 304	59.68 264	82.63 322	81.92 316

CVMVSNet		72.62 275	71.41 284	76.28 272	83.25 280	60.34 247	83.50 167	79.02 263	37.77 362	76.33 286	85.10 268	49.60 298	87.41 249	70.54 186	77.54 341	81.08 330

tfpn_ndepth		72.54 276	72.30 276	73.24 294	84.81 257	51.42 314	79.24 259	70.49 324	69.26 220	78.48 272	79.80 328	40.16 349	86.77 264	58.08 284	90.43 250	81.53 323

CHOSEN 1792x2688		72.45 277	70.56 287	78.13 245	90.02 143	63.08 204	68.72 328	83.16 238	42.99 358	75.92 292	85.46 262	57.22 276	85.18 284	49.87 322	81.67 324	86.14 257

testgi		72.36 278	74.61 246	65.59 326	80.56 305	42.82 354	68.29 329	73.35 298	66.87 240	81.84 230	89.93 196	72.08 208	66.92 345	46.05 338	92.54 202	87.01 250

thres200		72.34 279	71.55 283	74.70 283	83.48 277	51.60 313	75.02 301	73.71 295	70.14 214	78.56 271	80.57 322	46.20 307	88.20 244	46.99 335	89.29 258	84.32 281

FPMVS		72.29 280	72.00 278	73.14 296	88.63 163	85.00 32	74.65 304	67.39 340	71.94 198	77.80 278	87.66 232	50.48 296	75.83 323	49.95 320	79.51 332	58.58 360

FMVSNet5		72.10 281	71.69 280	73.32 292	81.57 292	53.02 299	76.77 284	78.37 265	63.31 265	76.37 285	91.85 139	36.68 356	78.98 314	47.87 331	92.45 203	87.95 240

our_test_3		71.85 282	71.59 281	72.62 301	80.71 303	53.78 293	69.72 326	71.71 316	58.80 292	78.03 275	80.51 323	56.61 277	78.84 315	62.20 245	86.04 293	85.23 266

PAPM		71.77 283	70.06 294	76.92 262	86.39 225	53.97 290	76.62 287	86.62 210	53.44 319	63.97 349	84.73 276	57.79 272	92.34 149	39.65 348	81.33 327	84.45 278

IB-MVS	62.13 19	71.64 284	68.97 298	79.66 225	80.80 302	62.26 226	73.94 308	76.90 272	63.27 266	68.63 331	76.79 340	33.83 359	91.84 162	59.28 273	87.26 282	84.88 274
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		71.63 285	72.30 276	69.62 311	76.47 333	52.70 302	70.03 325	80.97 255	59.18 291	79.36 266	88.21 223	60.50 251	69.12 338	58.33 281	77.62 340	87.04 249

no-one		71.52 286	70.43 291	74.81 281	78.45 321	63.41 196	57.73 354	77.03 271	51.46 334	77.17 282	90.33 188	54.96 287	80.35 311	47.41 332	99.29 2	80.68 334

Anonymous20231206		71.38 287	71.88 279	69.88 308	86.31 231	54.37 288	70.39 323	74.62 287	52.57 324	76.73 283	88.76 211	59.94 256	72.06 330	44.35 341	93.23 189	83.23 298

MIMVSNet		71.09 288	71.59 281	69.57 312	87.23 210	50.07 330	78.91 264	71.83 312	60.20 289	71.26 320	91.76 144	55.08 286	76.09 321	41.06 346	87.02 285	82.54 305

MS-PatchMatch		70.93 289	70.22 292	73.06 297	81.85 290	62.50 218	73.82 310	77.90 266	52.44 325	75.92 292	81.27 319	55.67 282	81.75 305	55.37 297	77.70 339	74.94 342

pmmvs5		70.73 290	70.07 293	72.72 299	77.03 330	52.73 301	74.14 306	75.65 281	50.36 342	72.17 316	85.37 266	55.42 284	80.67 309	52.86 310	87.59 280	84.77 275

PatchT		70.52 291	72.76 270	63.79 332	79.38 311	33.53 362	77.63 278	65.37 348	73.61 161	71.77 317	92.79 119	44.38 332	75.65 324	64.53 233	85.37 299	82.18 311

testmv		70.47 292	70.70 286	69.77 310	86.22 235	53.89 292	67.32 334	71.91 311	63.32 264	78.16 274	89.47 203	56.12 280	73.10 328	36.43 354	87.33 281	82.33 309

N_pmnet		70.20 293	68.80 300	74.38 284	80.91 299	84.81 35	59.12 351	76.45 276	55.06 311	75.31 299	82.36 310	55.74 281	54.82 362	47.02 334	87.24 283	83.52 291

tpmvs		70.16 294	69.56 296	71.96 305	74.71 349	48.13 339	79.63 244	75.45 283	65.02 258	70.26 325	81.88 314	45.34 322	85.68 279	58.34 280	75.39 344	82.08 312

new-patchmatchnet		70.10 295	73.37 265	60.29 341	81.23 296	16.95 369	59.54 348	74.62 287	62.93 268	80.97 241	87.93 228	62.83 245	71.90 331	55.24 298	95.01 139	92.00 165

YYNet1		70.06 296	70.44 289	68.90 314	73.76 351	53.42 297	58.99 352	67.20 342	58.42 295	87.10 148	85.39 265	59.82 258	67.32 342	59.79 263	83.50 314	85.96 258

MDA-MVSNet_test_wron		70.05 297	70.44 289	68.88 315	73.84 350	53.47 295	58.93 353	67.28 341	58.43 294	87.09 149	85.40 264	59.80 259	67.25 343	59.66 265	83.54 313	85.92 260

CostFormer		69.98 298	68.68 301	73.87 289	77.14 328	50.72 326	79.26 258	74.51 289	51.94 330	70.97 323	84.75 275	45.16 329	87.49 248	55.16 299	79.23 335	83.40 294

PatchmatchNet		69.71 299	68.83 299	72.33 303	77.66 326	53.60 294	79.29 257	69.99 326	57.66 300	72.53 313	82.93 297	46.45 306	80.08 313	60.91 258	72.09 350	83.31 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmp4_e23		69.43 300	67.33 307	75.72 277	78.53 320	52.75 300	82.13 209	74.91 284	49.23 346	66.37 338	84.17 282	41.28 345	88.67 238	49.73 323	79.63 331	85.75 262

LP		69.42 301	68.30 303	72.77 298	71.48 362	56.84 275	73.66 311	74.84 285	63.52 263	70.95 324	83.35 291	49.55 299	77.15 319	57.13 287	70.21 353	84.33 280

JIA-IIPM		69.41 302	66.64 312	77.70 252	73.19 354	71.24 140	75.67 295	65.56 347	70.42 210	65.18 344	92.97 111	33.64 360	83.06 299	53.52 308	69.61 357	78.79 336

UnsupCasMVSNet_bld		69.21 303	69.68 295	67.82 320	79.42 310	51.15 317	67.82 333	75.79 278	54.15 315	77.47 280	85.36 267	59.26 262	70.64 333	48.46 328	79.35 334	81.66 320

gg-mvs-nofinetune		68.96 304	69.11 297	68.52 319	76.12 336	45.32 345	83.59 160	55.88 363	86.68 20	64.62 348	97.01 7	30.36 363	83.97 296	44.78 340	82.94 317	76.26 340

tpm2		68.45 305	66.83 309	73.30 293	78.93 317	48.50 338	79.76 243	71.76 313	47.50 348	69.92 327	83.60 286	42.07 338	88.40 240	48.44 329	79.51 332	83.01 301

tpm		67.95 306	68.08 305	67.55 321	78.74 319	43.53 352	75.60 296	67.10 345	54.92 312	72.23 315	88.10 224	42.87 336	75.97 322	52.21 311	80.95 330	83.15 299

PatchFormer-LS_test		67.91 307	66.49 313	72.17 304	75.29 344	51.85 311	75.68 294	73.62 297	57.23 303	68.64 329	68.13 358	42.19 337	82.76 301	64.06 234	73.51 347	81.89 317

WTY-MVS		67.91 307	68.35 302	66.58 324	80.82 301	48.12 340	65.96 338	72.60 305	53.67 318	71.20 321	81.68 317	58.97 263	69.06 339	48.57 327	81.67 324	82.55 304

test-LLR		67.21 309	66.74 310	68.63 317	76.45 334	55.21 284	67.89 330	67.14 343	62.43 275	65.08 345	72.39 350	43.41 333	69.37 335	61.00 255	84.89 305	81.31 325

sss		66.92 310	67.26 308	65.90 325	77.23 327	51.10 319	64.79 339	71.72 315	52.12 329	70.13 326	80.18 325	57.96 269	65.36 352	50.21 319	81.01 329	81.25 327

tpm cat1		66.76 311	65.21 316	71.42 306	77.09 329	50.62 327	78.01 273	73.68 296	44.89 354	68.64 329	79.00 331	45.51 319	82.42 303	49.91 321	70.15 354	81.23 329

DWT-MVSNet_test		66.43 312	64.37 317	72.63 300	74.86 348	50.86 321	76.52 288	72.74 304	54.06 316	65.50 342	68.30 357	32.13 361	84.84 287	61.63 252	73.59 346	82.19 310

PVSNet	58.17 21	66.41 313	65.63 315	68.75 316	81.96 288	49.88 337	62.19 344	72.51 307	51.03 336	68.04 333	75.34 346	50.84 294	74.77 325	45.82 339	82.96 316	81.60 321

tpmrst		66.28 314	66.69 311	65.05 330	72.82 358	39.33 357	78.20 272	70.69 323	53.16 321	67.88 334	80.36 324	48.18 301	74.75 326	58.13 282	70.79 352	81.08 330

Patchmatch-test		65.91 315	67.38 306	61.48 338	75.51 341	43.21 353	68.84 327	63.79 350	62.48 273	72.80 312	83.42 290	44.89 330	59.52 359	48.27 330	86.45 288	81.70 318

ADS-MVSNet2		65.87 316	63.64 321	72.55 302	73.16 355	56.92 272	67.10 335	74.81 286	49.74 343	66.04 340	82.97 295	46.71 304	77.26 317	42.29 343	69.96 355	83.46 292

test1235678		65.57 317	65.73 314	65.06 329	82.84 285	50.90 320	62.90 342	69.26 328	57.17 304	72.36 314	83.04 293	46.02 309	70.10 334	32.79 359	85.24 302	74.19 344

test-mter		65.00 318	63.79 319	68.63 317	76.45 334	55.21 284	67.89 330	67.14 343	50.98 337	65.08 345	72.39 350	28.27 366	69.37 335	61.00 255	84.89 305	81.31 325

test0.0.03 1		64.66 319	64.36 318	65.57 327	75.03 347	46.89 342	64.69 340	61.58 356	62.43 275	71.18 322	77.54 336	43.41 333	68.47 340	40.75 347	82.65 320	81.35 324

pmmvs3		62.47 320	60.02 333	69.80 309	71.58 361	64.00 190	70.52 322	58.44 360	39.77 360	66.05 339	75.84 343	27.10 368	72.28 329	46.15 337	84.77 309	73.11 346

EPMVS		62.47 320	62.63 324	62.01 334	70.63 363	38.74 358	74.76 302	52.86 365	53.91 317	67.71 336	80.01 326	39.40 351	66.60 347	55.54 296	68.81 359	80.68 334

testus		62.33 322	63.03 322	60.20 342	78.78 318	40.74 355	59.14 349	69.80 327	49.26 345	71.41 319	74.72 348	52.33 292	63.52 354	29.84 361	82.01 323	76.36 339

ADS-MVSNet		61.90 323	62.19 325	61.03 340	73.16 355	36.42 360	67.10 335	61.75 354	49.74 343	66.04 340	82.97 295	46.71 304	63.21 356	42.29 343	69.96 355	83.46 292

1111		61.71 324	63.77 320	55.55 347	78.05 322	25.74 366	60.62 345	67.52 338	66.09 245	74.68 302	86.50 245	16.00 371	59.22 360	38.79 349	85.65 295	81.70 318

PMMVS		61.65 325	60.38 330	65.47 328	65.40 367	69.26 152	63.97 341	61.73 355	36.80 363	60.11 355	68.43 354	59.42 260	66.35 348	48.97 326	78.57 337	60.81 357

E-PMN		61.59 326	61.62 326	61.49 337	66.81 364	55.40 282	53.77 358	60.34 357	66.80 241	58.90 360	65.50 360	40.48 348	66.12 349	55.72 293	86.25 291	62.95 356

TESTMET0.1,1		61.29 327	60.32 331	64.19 331	72.06 359	51.30 315	67.89 330	62.09 352	45.27 353	60.65 354	69.01 353	27.93 367	64.74 353	56.31 290	81.65 326	76.53 338

MVS-HIRNet		61.16 328	62.92 323	55.87 345	79.09 314	35.34 361	71.83 317	57.98 362	46.56 350	59.05 359	91.14 161	49.95 297	76.43 320	38.74 351	71.92 351	55.84 361

EMVS		61.10 329	60.81 329	61.99 335	65.96 366	55.86 279	53.10 359	58.97 359	67.06 237	56.89 364	63.33 361	40.98 346	67.03 344	54.79 302	86.18 292	63.08 355

DSMNet-mixed		60.98 330	61.61 327	59.09 344	72.88 357	45.05 348	74.70 303	46.61 369	26.20 364	65.34 343	90.32 189	55.46 283	63.12 357	41.72 345	81.30 328	69.09 352

dp		60.70 331	60.29 332	61.92 336	72.04 360	38.67 359	70.83 320	64.08 349	51.28 335	60.75 353	77.28 338	36.59 357	71.58 332	47.41 332	62.34 361	75.52 341

CHOSEN 280x420		59.08 332	56.52 337	66.76 323	76.51 332	64.39 187	49.62 361	59.00 358	43.86 356	55.66 365	68.41 356	35.55 358	68.21 341	43.25 342	76.78 343	67.69 353

testpf		58.55 333	61.58 328	49.48 350	66.03 365	40.05 356	74.40 305	58.07 361	64.72 260	59.36 357	72.67 349	22.76 369	66.92 345	67.07 212	69.15 358	41.46 363

test2356		56.69 334	55.15 338	61.32 339	73.20 353	44.11 350	54.95 356	62.52 351	48.75 347	62.45 351	68.42 355	21.10 370	65.67 351	26.86 363	78.08 338	74.19 344

PVSNet_0	51.08 22	56.10 335	54.97 339	59.48 343	75.12 346	53.28 298	55.16 355	61.89 353	44.30 355	59.16 358	62.48 362	54.22 288	65.91 350	35.40 356	47.01 362	59.25 359

new_pmnet		55.69 336	57.66 335	49.76 349	75.47 342	30.59 363	59.56 347	51.45 367	43.62 357	62.49 350	75.48 344	40.96 347	49.15 365	37.39 353	72.52 348	69.55 351

PMMVS2		55.64 337	59.27 334	44.74 352	64.30 368	12.32 370	40.60 362	49.79 368	53.19 320	65.06 347	84.81 274	53.60 290	49.76 364	32.68 360	89.41 257	72.15 347

test12356		54.91 338	57.14 336	48.22 351	75.83 338	17.47 368	52.31 360	69.20 329	51.66 332	60.11 355	75.40 345	29.77 365	62.62 358	27.64 362	72.37 349	64.59 354

PNet_i23d		52.13 339	51.24 341	54.79 348	75.56 339	45.26 346	54.54 357	52.55 366	66.95 238	57.19 363	65.82 359	13.15 373	63.40 355	36.39 355	39.04 364	55.71 362

MVE	40.22 23	51.82 340	50.47 343	55.87 345	62.66 369	51.91 307	31.61 364	39.28 370	40.65 359	50.76 366	74.98 347	56.24 279	44.67 366	33.94 358	64.11 360	71.04 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
.test1245		48.02 341	54.41 340	28.84 354	78.05 322	25.74 366	60.62 345	67.52 338	66.09 245	74.68 302	86.50 245	16.00 371	59.22 360	38.79 349	1.47 366	1.55 367

pcd1.5k->3k		38.83 342	41.11 344	32.01 353	93.13 64	0.00 374	0.00 365	91.38 117	0.00 369	0.00 371	0.00 371	89.24 15	0.00 371	0.00 368	96.24 96	96.02 48

v1.0		38.20 343	50.94 342	0.00 359	93.86 48	0.00 374	0.00 365	93.93 26	84.39 30	92.84 43	93.43 102	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

cdsmvs_eth3d_5k		20.81 344	27.75 345	0.00 359	0.00 374	0.00 374	0.00 365	85.44 221	0.00 369	0.00 371	82.82 299	81.46 94	0.00 371	0.00 368	0.00 369	0.00 369

tmp_tt		20.25 345	24.50 346	7.49 356	4.47 371	8.70 371	34.17 363	25.16 372	1.00 366	32.43 368	18.49 365	39.37 352	9.21 368	21.64 364	43.75 363	4.57 365

ab-mvs-re		6.65 346	8.87 347	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	79.80 328	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

pcd_1.5k_mvsjas		6.41 347	8.55 348	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	76.94 135	0.00 371	0.00 368	0.00 369	0.00 369

test123		6.27 348	8.08 349	0.84 357	1.11 373	0.57 372	62.90 342	0.82 374	0.54 367	1.07 370	2.75 370	1.26 374	0.30 369	1.04 366	1.26 368	1.66 366

testmvs		5.91 349	7.65 350	0.72 358	1.20 372	0.37 373	59.14 349	0.67 375	0.49 368	1.11 369	2.76 369	0.94 375	0.24 370	1.02 367	1.47 366	1.55 367

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

GSMVS																83.88 285

test_part2					93.86 48	77.77 82				92.84 43

test_part1				0.00 359		0.00 374	0.00 365	93.93 26				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sam_mvs1												46.11 308				83.88 285

sam_mvs												45.92 313

semantic-postprocess				84.34 141	83.93 274	69.66 148		81.09 254	72.43 181	86.47 163	90.19 192	57.56 273	93.15 128	77.45 132	86.39 290	90.22 210

ambc				82.98 177	90.55 131	64.86 182	88.20 76	89.15 173		89.40 114	93.96 89	71.67 213	91.38 176	78.83 119	96.55 82	92.71 141

MTGPA								91.81 95

test_post1							78.85 266				3.13 367	45.19 324	80.13 312	58.11 283

test_post											3.10 368	45.43 320	77.22 318

patchmatchnet-post											81.71 316	45.93 312	87.01 252

GG-mvs-BLEND				67.16 322	73.36 352	46.54 344	84.15 142	55.04 364		58.64 361	61.95 363	29.93 364	83.87 297	38.71 352	76.92 342	71.07 349

MTMP							90.66 36	33.14 371

gm-plane-assit					75.42 343	44.97 349			52.17 326		72.36 352		87.90 245	54.10 305

test9_res														80.83 91	96.45 88	90.57 202

TEST9					92.34 84	79.70 67	83.94 147	90.32 142	65.41 255	84.49 192	90.97 169	82.03 85	93.63 90

test_8					92.09 91	78.87 74	83.82 152	90.31 144	65.79 248	84.36 195	90.96 171	81.93 87	93.44 112

agg_prior2														79.68 109	96.16 98	90.22 210

agg_prior					91.58 105	77.69 83		90.30 146		84.32 196			93.18 124

TestCases				89.68 49	91.59 102	83.40 45		95.44 5	79.47 80	88.00 136	93.03 108	82.66 73	91.47 170	70.81 180	96.14 100	94.16 98

test_prior4						78.97 73	84.59 134

test_prior2							83.37 170		75.43 142	84.58 190	91.57 147	81.92 89		79.54 111	96.97 70

test_prior				86.32 94	90.59 129	71.99 131		92.85 64					94.17 69			92.80 138

旧先验2							81.73 216		56.88 305	86.54 162			84.90 286	72.81 171

新几何2							81.72 217

新几何1				82.95 178	93.96 45	78.56 78		80.24 257	55.45 309	83.93 202	91.08 165	71.19 215	88.33 242	65.84 223	93.07 190	81.95 315

旧先验1					91.97 94	71.77 133		81.78 250			91.84 140	73.92 174			93.65 176	83.61 290

无先验							82.81 186	85.62 220	58.09 297				91.41 174	67.95 209		84.48 277

原ACMM2							82.26 204

原ACMM1				84.60 131	92.81 74	74.01 112		91.50 102	62.59 271	82.73 219	90.67 181	76.53 142	94.25 64	69.24 194	95.69 119	85.55 263

test222					93.31 59	76.54 96	79.38 256	77.79 267	52.59 323	82.36 223	90.84 175	66.83 230			91.69 214	81.25 327

testdata2													86.43 270	63.52 237

segment_acmp												81.94 86

testdata				79.54 227	92.87 70	72.34 124		80.14 259	59.91 290	85.47 176	91.75 145	67.96 226	85.24 282	68.57 205	92.18 211	81.06 332

testdata1							79.62 245		73.95 158

test12				86.57 89	90.74 126	72.63 120		90.69 131		82.76 218		79.20 111	94.80 52		95.32 126	92.27 159

plane_prior7					93.45 54	77.31 89

plane_prior6					92.61 75	76.54 96						74.84 159

plane_prior5								93.61 34					95.22 40	80.78 92	95.83 114	94.46 89

plane_prior4											92.95 112

plane_prior3						76.85 94			77.79 104	86.55 157

plane_prior2							89.45 58		79.44 82

plane_prior1					92.83 73

plane_prior						76.42 99	87.15 95		75.94 134						95.03 138

n2								0.00 376

nn								0.00 376

door-mid								74.45 290

lessismore_v0				85.95 105	91.10 120	70.99 142		70.91 322		91.79 64	94.42 69	61.76 247	92.93 137	79.52 113	93.03 192	93.93 105

LGP-MVS_train				90.82 34	94.75 37	81.69 52		94.27 13	82.35 49	93.67 30	94.82 56	91.18 5	95.52 28	85.36 36	98.73 8	95.23 74

test11								91.46 108

door								72.57 306

HQP5-MVS						70.66 143

HQP-NCC					91.19 115		84.77 129		73.30 169	80.55 256

ACMP_Plane					91.19 115		84.77 129		73.30 169	80.55 256

BP-MVS														77.30 134

HQP4-MVS										80.56 255			94.61 56			93.56 121

HQP3-MVS								92.68 70							94.47 157

HQP2-MVS												72.10 206

NP-MVS					91.95 95	74.55 109					90.17 194

MDTV_nov1_ep13_2view						27.60 365	70.76 321		46.47 351	61.27 352		45.20 323		49.18 325		83.75 289

MDTV_nov1_ep13			68.29 304		78.03 324	43.87 351	74.12 307	72.22 308	52.17 326	67.02 337	85.54 258	45.36 321	80.85 308	55.73 292	84.42 310

ACMMP++_ref															95.74 118

ACMMP++															97.35 61

Test By Simon												79.09 112

ITE_SJBPF				90.11 46	90.72 127	84.97 33		90.30 146	81.56 60	90.02 88	91.20 158	82.40 77	90.81 189	73.58 161	94.66 153	94.56 84

DeepMVS_CX				24.13 355	32.95 370	29.49 364		21.63 373	12.07 365	37.95 367	45.07 364	30.84 362	19.21 367	17.94 365	33.06 365	23.69 364

High-resolution multi-view benchmark