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.

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 33	98.48 19	97.22 25

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

Effi-MVS+-dtu		85.82 106	83.38 155	93.14 3	87.13 210	91.15 3	87.70 85	88.42 177	74.57 149	83.56 204	85.65 251	78.49 116	94.21 66	72.04 173	92.88 194	94.05 100

abl_6		93.02 4	93.16 4	92.60 4	94.73 38	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	93.71 112

mPP-MVS		91.69 10	91.47 19	92.37 5	96.04 11	88.48 8	92.72 14	92.60 72	83.09 41	91.54 68	94.25 78	87.67 32	95.51 30	87.21 24	98.11 34	93.12 127

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 52

anonymousdsp		89.73 46	88.88 58	92.27 7	89.82 143	86.67 12	90.51 37	90.20 152	69.87 212	95.06 13	96.14 23	84.28 58	93.07 131	87.68 13	96.34 92	97.09 29

XVS		91.54 12	91.36 21	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 26	89.99 88	94.03 85	86.57 42	95.80 16	87.35 20	97.62 54	94.20 94

X-MVStestdata		85.04 116	82.70 163	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 26	89.99 88	16.05 359	86.57 42	95.80 16	87.35 20	97.62 54	94.20 94

COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 50	86.15 18	93.37 8	95.10 7	90.28 8	92.11 57	95.03 49	89.75 14	94.93 49	79.95 105	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 49	92.39 56	94.14 82	89.15 16	95.62 22	87.35 20	98.24 29	94.56 83
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
CP-MVS		91.67 11	91.58 16	91.96 12	95.29 27	87.62 9	93.38 7	93.36 37	83.16 40	91.06 74	94.00 86	88.26 22	95.71 21	87.28 23	98.39 22	92.55 146

PGM-MVS		91.20 21	90.95 32	91.93 13	95.67 20	85.85 25	90.00 40	93.90 28	80.32 72	91.74 66	94.41 71	88.17 24	95.98 8	86.37 26	97.99 40	93.96 103

ACMMPR		91.49 14	91.35 23	91.92 14	95.74 18	85.88 24	92.58 18	93.25 46	81.99 55	91.40 71	94.17 81	87.51 33	95.87 13	87.74 11	97.76 47	93.99 101

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 45	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
region2R		91.44 17	91.30 25	91.87 16	95.75 17	85.90 23	92.63 17	93.30 42	81.91 57	90.88 79	94.21 79	87.75 29	95.87 13	87.60 17	97.71 52	93.83 105

mvs-test1		84.55 127	82.12 174	91.84 17	87.13 210	89.54 5	85.05 127	88.42 177	74.57 149	80.60 246	82.98 288	78.49 116	93.98 76	72.04 173	89.77 249	92.00 161

MP-MVS		91.14 23	90.91 33	91.83 18	96.18 10	86.88 11	92.20 22	93.03 56	82.59 48	88.52 127	94.37 75	86.74 39	95.41 33	86.32 27	98.21 31	93.19 126
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 49	85.72 28	96.79 1	95.51 4	88.86 12	95.63 10	96.99 8	84.81 54	93.16 124	91.10 1	97.53 60	96.58 40
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
APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 64	85.17 31	92.47 21	95.05 8	87.65 18	93.21 36	94.39 74	90.09 13	95.08 45	86.67 25	97.60 58	94.18 96

test_djsdf		89.62 47	89.01 55	91.45 21	92.36 81	82.98 49	91.98 25	90.08 153	71.54 198	94.28 22	96.54 13	81.57 93	94.27 62	86.26 28	96.49 87	97.09 29

UA-Net		91.49 14	91.53 17	91.39 22	94.98 32	82.95 50	93.52 5	92.79 65	88.22 15	88.53 126	97.64 2	83.45 65	94.55 60	86.02 34	98.60 14	96.67 37

LS3D		90.60 28	90.34 41	91.38 23	89.03 152	84.23 42	93.58 4	94.68 10	90.65 6	90.33 83	93.95 92	84.50 57	95.37 34	80.87 89	95.50 123	94.53 87

mvs_tets		89.78 45	89.27 53	91.30 24	93.51 52	84.79 36	89.89 45	90.63 131	70.00 211	94.55 17	96.67 11	87.94 28	93.59 94	84.27 49	95.97 108	95.52 66

zzz-MVS		91.27 19	91.26 26	91.29 25	96.59 3	86.29 14	88.94 66	91.81 92	84.07 33	92.00 60	94.40 72	86.63 40	95.28 38	88.59 4	98.31 25	92.30 153

MTAPA		91.52 13	91.60 15	91.29 25	96.59 3	86.29 14	92.02 24	91.81 92	84.07 33	92.00 60	94.40 72	86.63 40	95.28 38	88.59 4	98.31 25	92.30 153

RPSCF		88.00 67	86.93 88	91.22 27	90.08 137	89.30 6	89.68 49	91.11 122	79.26 84	89.68 101	94.81 60	82.44 76	87.74 241	76.54 140	88.74 260	96.61 39

jajsoiax		89.41 50	88.81 60	91.19 28	93.38 56	84.72 37	89.70 47	90.29 147	69.27 215	94.39 19	96.38 15	86.02 49	93.52 104	83.96 52	95.92 111	95.34 69

HSP-MVS		88.63 60	87.84 70	91.02 29	95.76 16	86.14 19	92.75 13	91.01 125	78.43 97	89.16 116	92.25 130	72.03 209	96.36 2	88.21 9	90.93 232	90.55 201

HFP-MVS		91.30 18	91.39 20	91.02 29	95.43 25	84.66 39	92.58 18	93.29 44	81.99 55	91.47 69	93.96 89	88.35 20	95.56 25	87.74 11	97.74 50	92.85 132

#test#		90.49 30	90.31 42	91.02 29	95.43 25	84.66 39	90.65 36	93.29 44	77.00 121	91.47 69	93.96 89	88.35 20	95.56 25	84.88 40	97.74 50	92.85 132

3Dnovator+	83.92 2	89.97 40	89.66 48	90.92 32	91.27 113	81.66 54	91.25 32	94.13 22	88.89 11	88.83 121	94.26 77	77.55 126	95.86 15	84.88 40	95.87 113	95.24 73

OurMVSNet-221017-0		90.01 37	89.74 47	90.83 33	93.16 61	80.37 59	91.91 27	93.11 50	81.10 65	95.32 12	97.24 5	72.94 194	94.85 51	85.07 37	97.78 46	97.26 22

LPG-MVS_test		91.47 16	91.68 14	90.82 34	94.75 36	81.69 51	90.00 40	94.27 13	82.35 50	93.67 29	94.82 57	91.18 5	95.52 28	85.36 35	98.73 8	95.23 74

LGP-MVS_train				90.82 34	94.75 36	81.69 51		94.27 13	82.35 50	93.67 29	94.82 57	91.18 5	95.52 28	85.36 35	98.73 8	95.23 74

CPTT-MVS		89.39 51	88.98 57	90.63 36	95.09 30	86.95 10	92.09 23	92.30 78	79.74 77	87.50 141	92.38 124	81.42 95	93.28 119	83.07 63	97.24 65	91.67 171

SteuartSystems-ACMMP		91.16 22	91.36 21	90.55 37	93.91 46	80.97 58	91.49 29	93.48 36	82.82 46	92.60 51	93.97 87	88.19 23	96.29 3	87.61 15	98.20 32	94.39 92
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	81.24 5	87.28 76	86.21 100	90.49 38	91.48 108	84.90 34	83.41 167	92.38 77	70.25 209	89.35 114	90.68 178	82.85 71	94.57 58	79.55 109	95.95 109	92.00 161

XVG-ACMP-BASELINE		89.98 38	89.84 45	90.41 39	94.91 34	84.50 41	89.49 58	93.98 24	79.68 78	92.09 58	93.89 94	83.80 61	93.10 127	82.67 70	98.04 35	93.64 114

HPM-MVS++		88.93 57	88.45 65	90.38 40	94.92 33	85.85 25	89.70 47	91.27 118	78.20 100	86.69 153	92.28 129	80.36 104	95.06 46	86.17 32	96.49 87	90.22 207

ACMP	79.16 10	90.54 29	90.60 36	90.35 41	94.36 40	80.98 57	89.16 63	94.05 23	79.03 89	92.87 41	93.74 96	90.60 11	95.21 42	82.87 66	98.76 5	94.87 79
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		89.59 48	89.37 52	90.28 42	94.47 39	85.95 21	86.84 99	93.91 27	80.07 75	86.75 152	93.26 102	93.64 2	90.93 181	84.60 45	90.75 237	93.97 102

XVG-OURS		89.18 54	88.83 59	90.23 43	94.28 41	86.11 20	85.91 115	93.60 34	80.16 74	89.13 117	93.44 99	83.82 60	90.98 179	83.86 55	95.30 130	93.60 116

OMC-MVS		88.19 65	87.52 75	90.19 44	91.94 95	81.68 53	87.49 89	93.17 49	76.02 130	88.64 124	91.22 153	84.24 59	93.37 114	77.97 126	97.03 70	95.52 66

ITE_SJBPF				90.11 45	90.72 126	84.97 33		90.30 144	81.56 61	90.02 87	91.20 156	82.40 77	90.81 186	73.58 158	94.66 153	94.56 83

MP-MVS-pluss		90.81 24	91.08 28	89.99 46	95.97 12	79.88 63	88.13 78	94.51 11	75.79 134	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
AllTest		87.97 68	87.40 79	89.68 47	91.59 101	83.40 45	89.50 57	95.44 5	79.47 80	88.00 134	93.03 107	82.66 73	91.47 167	70.81 177	96.14 101	94.16 97

TestCases				89.68 47	91.59 101	83.40 45		95.44 5	79.47 80	88.00 134	93.03 107	82.66 73	91.47 167	70.81 177	96.14 101	94.16 97

F-COLMAP		84.97 119	83.42 154	89.63 49	92.39 80	83.40 45	88.83 69	91.92 89	73.19 169	80.18 254	89.15 204	77.04 133	93.28 119	65.82 221	92.28 203	92.21 158

ACMM	79.39 9	90.65 26	90.99 30	89.63 49	95.03 31	83.53 44	89.62 53	93.35 38	79.20 85	93.83 27	93.60 98	90.81 8	92.96 132	85.02 39	98.45 20	92.41 149
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PS-MVSNAJss		88.31 64	87.90 69	89.56 51	93.31 57	77.96 79	87.94 80	91.97 87	70.73 204	94.19 23	96.67 11	76.94 135	94.57 58	83.07 63	96.28 94	96.15 43

SMA-MVS		90.18 31	90.38 40	89.55 52	95.15 28	79.52 69	90.98 34	93.24 47	75.37 143	92.84 43	94.93 53	85.58 51	96.00 6	87.61 15	97.76 47	93.12 127

ACMMP_Plus		90.65 26	91.07 29	89.42 53	95.93 14	79.54 68	89.95 43	93.68 31	77.65 106	91.97 62	94.89 54	88.38 18	95.45 31	89.27 3	97.87 45	93.27 122

DeepC-MVS	82.31 4	89.15 55	89.08 54	89.37 54	93.64 51	79.07 71	88.54 74	94.20 17	73.53 158	89.71 100	94.82 57	85.09 52	95.77 18	84.17 51	98.03 37	93.26 123
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPM-MVS		89.80 44	89.97 43	89.27 55	94.76 35	79.86 64	86.76 103	92.78 66	78.78 92	92.51 52	93.64 97	88.13 26	93.84 83	84.83 42	97.55 59	94.10 99

APD-MVS		89.54 49	89.63 49	89.26 56	92.57 74	81.34 56	90.19 39	93.08 52	80.87 67	91.13 73	93.19 103	86.22 47	95.97 9	82.23 74	97.18 67	90.45 203
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APDe-MVS		91.22 20	91.92 9	89.14 57	92.97 66	78.04 78	92.84 12	94.14 21	83.33 38	93.90 25	95.73 30	88.77 17	96.41 1	87.60 17	97.98 42	92.98 131

TSAR-MVS + MP.		88.14 66	87.82 71	89.09 58	95.72 19	76.74 94	92.49 20	91.19 121	67.85 230	86.63 154	94.84 56	79.58 110	95.96 10	87.62 14	94.50 156	94.56 83

NCCC		87.36 74	86.87 89	88.83 59	92.32 84	78.84 74	86.58 110	91.09 123	78.77 93	84.85 180	90.89 171	80.85 99	95.29 36	81.14 85	95.32 127	92.34 152

Regformer-2		86.74 86	86.08 102	88.73 60	84.18 265	79.20 70	83.52 162	89.33 168	83.33 38	89.92 95	85.07 264	83.23 68	93.16 124	83.39 58	92.72 197	93.83 105

HQP_MVS		87.75 72	87.43 78	88.70 61	93.45 53	76.42 98	89.45 59	93.61 32	79.44 82	86.55 155	92.95 111	74.84 158	95.22 40	80.78 91	95.83 115	94.46 88

Regformer-4		86.41 91	85.71 107	88.52 62	84.27 261	77.57 84	84.07 142	88.00 187	82.82 46	89.84 97	85.48 254	82.06 83	92.77 139	83.83 56	91.04 225	95.22 76

ESAPD		90.05 35	90.56 39	88.50 63	93.86 47	77.77 80	89.63 51	93.93 25	84.39 29	92.84 43	93.43 100	87.19 35	96.26 4	82.18 75	97.61 56	91.48 177

ACMH+	77.89 11	90.73 25	91.50 18	88.44 64	93.00 65	76.26 100	89.65 50	95.55 3	87.72 17	93.89 26	94.94 52	91.62 4	93.44 110	78.35 120	98.76 5	95.61 65

TAPA-MVS	77.73 12	85.71 109	84.83 122	88.37 65	88.78 157	79.72 65	87.15 95	93.50 35	69.17 217	85.80 168	89.56 199	80.76 100	92.13 151	73.21 166	95.51 122	93.25 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PMVS	80.48 6	90.08 33	90.66 35	88.34 66	96.71 2	92.97 2	90.31 38	89.57 165	88.51 14	90.11 84	95.12 48	90.98 7	88.92 225	77.55 128	97.07 69	83.13 294
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
CNVR-MVS		87.81 71	87.68 74	88.21 67	92.87 68	77.30 89	85.25 124	91.23 119	77.31 116	87.07 148	91.47 149	82.94 70	94.71 54	84.67 44	96.27 96	92.62 145

agg_prior3		85.76 107	84.95 120	88.16 68	92.43 79	79.92 62	83.98 145	90.03 155	65.11 252	83.66 202	90.64 182	81.00 98	93.67 86	81.21 83	96.54 84	90.88 190

PHI-MVS		86.38 92	85.81 105	88.08 69	88.44 167	77.34 87	89.35 61	93.05 53	73.15 170	84.76 181	87.70 228	78.87 114	94.18 67	80.67 94	96.29 93	92.73 136

train_agg		85.98 104	85.28 114	88.07 70	92.34 82	79.70 66	83.94 146	90.32 140	65.79 243	84.49 189	90.97 167	81.93 87	93.63 89	81.21 83	96.54 84	90.88 190

v52		89.97 40	90.60 36	88.07 70	88.69 158	72.01 128	91.35 30	92.64 70	82.22 52	95.97 8	96.31 16	84.82 53	93.98 76	88.59 4	94.83 146	98.23 7

V4		89.97 40	90.60 36	88.07 70	88.69 158	72.01 128	91.35 30	92.64 70	82.22 52	95.98 7	96.31 16	84.80 55	93.98 76	88.59 4	94.83 146	98.23 7

CDPH-MVS		86.17 100	85.54 110	88.05 73	92.25 85	75.45 103	83.85 150	92.01 85	65.91 242	86.19 162	91.75 143	83.77 62	94.98 48	77.43 131	96.71 79	93.73 111

DeepC-MVS_fast	80.27 8	86.23 97	85.65 109	87.96 74	91.30 111	76.92 91	87.19 93	91.99 86	70.56 205	84.96 176	90.69 177	80.01 107	95.14 43	78.37 119	95.78 117	91.82 167
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		83.95 150	82.69 164	87.72 75	89.27 148	81.45 55	83.72 156	81.58 247	74.73 147	85.66 169	86.06 249	72.56 203	92.69 141	75.44 145	95.21 131	89.01 225

Vis-MVSNet		86.86 82	86.58 93	87.72 75	92.09 89	77.43 86	87.35 90	92.09 83	78.87 91	84.27 197	94.05 84	78.35 118	93.65 87	80.54 96	91.58 213	92.08 160
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v7n		90.13 32	90.96 31	87.65 77	91.95 93	71.06 140	89.99 42	93.05 53	86.53 21	94.29 21	96.27 19	82.69 72	94.08 72	86.25 30	97.63 53	97.82 10

MVS_0304		84.88 120	83.96 150	87.64 78	87.43 199	74.83 106	84.18 140	93.30 42	77.48 109	77.39 274	88.46 213	74.53 166	95.74 19	78.09 125	94.75 152	92.36 151

Regformer-1		86.00 101	85.50 111	87.49 79	84.18 265	76.90 92	83.52 162	87.94 189	82.18 54	89.19 115	85.07 264	82.28 79	91.89 158	82.40 72	92.72 197	93.69 113

PLC	73.85 16	82.09 177	80.31 196	87.45 80	90.86 124	80.29 60	85.88 116	90.65 130	68.17 224	76.32 280	86.33 245	73.12 193	92.61 144	61.40 248	90.02 247	89.44 216
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
wuykxyi23d		88.46 62	88.80 61	87.44 81	90.96 121	93.03 1	85.85 117	81.96 242	74.58 148	98.58 2	97.29 4	87.73 30	87.31 244	82.84 68	99.41 1	81.99 307

DP-MVS		88.60 61	89.01 55	87.36 82	91.30 111	77.50 85	87.55 87	92.97 59	87.95 16	89.62 106	92.87 113	84.56 56	93.89 80	77.65 127	96.62 81	90.70 195

agg_prior1		85.72 108	85.20 115	87.28 83	91.58 104	77.69 82	83.69 157	90.30 144	66.29 238	84.32 193	91.07 164	82.13 81	93.18 122	81.02 86	96.36 91	90.98 185

EI-MVSNet-Vis-set		85.12 114	84.53 135	86.88 84	84.01 267	72.76 118	83.91 149	85.18 222	80.44 69	88.75 122	85.49 253	80.08 106	91.92 156	82.02 77	90.85 235	95.97 50

EI-MVSNet-UG-set		85.04 116	84.44 137	86.85 85	83.87 270	72.52 121	83.82 151	85.15 223	80.27 73	88.75 122	85.45 257	79.95 108	91.90 157	81.92 79	90.80 236	96.13 44

EPP-MVSNet		85.47 111	85.04 117	86.77 86	91.52 107	69.37 149	91.63 28	87.98 188	81.51 62	87.05 149	91.83 139	66.18 232	95.29 36	70.75 179	96.89 73	95.64 60

CANet		83.79 153	82.85 162	86.63 87	86.17 233	72.21 127	83.76 155	91.43 109	77.24 117	74.39 297	87.45 232	75.36 150	95.42 32	77.03 136	92.83 195	92.25 157

test12				86.57 88	90.74 125	72.63 119		90.69 129		82.76 214		79.20 111	94.80 52		95.32 127	92.27 155

UniMVSNet (Re)		86.87 81	86.98 86	86.55 89	93.11 63	68.48 157	83.80 153	92.87 61	80.37 70	89.61 108	91.81 141	77.72 123	94.18 67	75.00 148	98.53 17	96.99 33

DP-MVS Recon		84.05 147	83.22 157	86.52 90	91.73 99	75.27 104	83.23 175	92.40 75	72.04 186	82.04 220	88.33 218	77.91 122	93.95 79	66.17 216	95.12 135	90.34 206

SixPastTwentyTwo		87.20 77	87.45 77	86.45 91	92.52 76	69.19 154	87.84 84	88.05 185	81.66 60	94.64 16	96.53 14	65.94 233	94.75 53	83.02 65	96.83 76	95.41 68

K. test v3		85.14 113	84.73 123	86.37 92	91.13 118	69.63 148	85.45 122	76.68 270	84.06 35	92.44 54	96.99 8	62.03 245	94.65 55	80.58 95	93.24 187	94.83 82

test_prior3		86.31 94	86.31 97	86.32 93	90.59 128	71.99 130	83.37 168	92.85 62	75.43 140	84.58 187	91.57 145	81.92 89	94.17 69	79.54 110	96.97 71	92.80 134

test_prior				86.32 93	90.59 128	71.99 130		92.85 62					94.17 69			92.80 134

HQP-MVS		84.61 125	84.06 147	86.27 95	91.19 114	70.66 142	84.77 128	92.68 68	73.30 165	80.55 249	90.17 191	72.10 205	94.61 56	77.30 132	94.47 157	93.56 118

EPNet		80.37 198	78.41 209	86.23 96	76.75 323	73.28 115	87.18 94	77.45 264	76.24 127	68.14 324	88.93 207	65.41 235	93.85 81	69.47 189	96.12 103	91.55 175
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-3		85.06 115	84.67 128	86.22 97	84.27 261	73.43 114	84.07 142	85.26 220	80.77 68	88.62 125	85.48 254	80.56 103	90.39 198	81.99 78	91.04 225	94.85 81

SD-MVS		88.96 56	89.88 44	86.22 97	91.63 100	77.07 90	89.82 46	93.77 30	78.90 90	92.88 40	92.29 128	86.11 48	90.22 202	86.24 31	97.24 65	91.36 180

DU-MVS		86.80 84	86.99 85	86.21 99	93.24 59	67.02 166	83.16 176	92.21 80	81.73 59	90.92 76	91.97 134	77.20 129	93.99 74	74.16 151	98.35 23	97.61 12

UGNet		82.78 167	81.64 180	86.21 99	86.20 232	76.24 101	86.86 98	85.68 216	77.07 120	73.76 300	92.82 114	69.64 217	91.82 161	69.04 196	93.69 173	90.56 200
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
v748		88.91 58	89.82 46	86.19 101	90.06 139	68.53 156	88.81 70	91.48 102	84.36 31	94.19 23	95.98 26	82.52 75	92.67 142	84.30 48	96.67 80	97.37 19

UniMVSNet_NR-MVSNet		86.84 83	87.06 83	86.17 102	92.86 70	67.02 166	82.55 191	91.56 98	83.08 42	90.92 76	91.82 140	78.25 119	93.99 74	74.16 151	98.35 23	97.49 15

IS-MVSNet		86.66 87	86.82 92	86.17 102	92.05 91	66.87 168	91.21 33	88.64 175	86.30 23	89.60 109	92.59 119	69.22 219	94.91 50	73.89 155	97.89 44	96.72 36

lessismore_v0				85.95 104	91.10 119	70.99 141		70.91 314		91.79 64	94.42 70	61.76 246	92.93 134	79.52 112	93.03 191	93.93 104

nrg030		87.85 70	88.49 64	85.91 105	90.07 138	69.73 146	87.86 81	94.20 17	74.04 153	92.70 49	94.66 61	85.88 50	91.50 166	79.72 107	97.32 63	96.50 41

Fast-Effi-MVS+-dtu		82.54 171	81.41 183	85.90 106	85.60 242	76.53 97	83.07 177	89.62 164	73.02 172	79.11 262	83.51 281	80.74 101	90.24 201	68.76 198	89.29 252	90.94 187

test_0402		88.65 59	89.58 51	85.88 107	92.55 75	72.22 126	84.01 144	89.44 167	88.63 13	94.38 20	95.77 29	86.38 46	93.59 94	79.84 106	95.21 131	91.82 167

PCF-MVS	74.62 15	82.15 176	80.92 191	85.84 108	89.43 145	72.30 124	80.53 232	91.82 91	57.36 294	87.81 137	89.92 194	77.67 124	93.63 89	58.69 268	95.08 136	91.58 174
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_LR		84.28 138	83.76 152	85.83 109	89.23 149	83.07 48	80.99 228	83.56 232	72.71 175	86.07 163	89.07 205	81.75 92	86.19 267	77.11 135	93.36 180	88.24 228

WR-MVS_H		89.91 43	91.31 24	85.71 110	96.32 9	62.39 216	89.54 56	93.31 41	90.21 9	95.57 11	95.66 32	81.42 95	95.90 12	80.94 88	98.80 4	98.84 5

MCST-MVS		84.36 134	83.93 151	85.63 111	91.59 101	71.58 137	83.52 162	92.13 82	61.82 272	83.96 198	89.75 196	79.93 109	93.46 109	78.33 121	94.34 159	91.87 166

CSCG		86.26 95	86.47 95	85.60 112	90.87 123	74.26 110	87.98 79	91.85 90	80.35 71	89.54 112	88.01 222	79.09 112	92.13 151	75.51 144	95.06 137	90.41 204

MVS_111021_HR		84.63 124	84.34 144	85.49 113	90.18 136	75.86 102	79.23 259	87.13 200	73.35 162	85.56 172	89.34 202	83.60 64	90.50 195	76.64 139	94.05 163	90.09 212

NR-MVSNet		86.00 101	86.22 99	85.34 114	93.24 59	64.56 182	82.21 202	90.46 135	80.99 66	88.42 129	91.97 134	77.56 125	93.85 81	72.46 170	98.65 13	97.61 12

LF4IMVS		82.75 168	81.93 178	85.19 115	82.08 281	80.15 61	85.53 121	88.76 173	68.01 225	85.58 171	87.75 227	71.80 210	86.85 250	74.02 153	93.87 167	88.58 227

TranMVSNet+NR-MVSNet		87.86 69	88.76 62	85.18 116	94.02 43	64.13 185	84.38 138	91.29 117	84.88 28	92.06 59	93.84 95	86.45 44	93.73 84	73.22 162	98.66 12	97.69 11

3Dnovator	80.37 7	84.80 122	84.71 126	85.06 117	86.36 225	74.71 107	88.77 71	90.00 156	75.65 138	84.96 176	93.17 104	74.06 169	91.19 174	78.28 122	91.09 223	89.29 220

v13		87.31 75	88.10 66	84.94 118	88.84 155	63.75 189	87.85 83	91.47 105	79.12 86	93.72 28	95.82 28	75.20 152	93.58 97	84.76 43	96.16 99	97.48 16

CNLPA		83.55 158	83.10 160	84.90 119	89.34 147	83.87 43	84.54 136	88.77 172	79.09 87	83.54 205	88.66 211	74.87 157	81.73 300	66.84 212	92.29 202	89.11 221

Anonymous20240521		87.68 73	88.61 63	84.87 120	91.76 98	64.76 180	89.28 62	91.66 96	83.02 43	93.29 34	96.10 24	77.37 128	92.89 137	77.27 134	97.75 49	96.97 34

v12		87.15 78	87.91 68	84.84 121	88.69 158	63.52 192	87.58 86	91.46 106	78.74 94	93.57 31	95.66 32	74.94 156	93.57 98	84.50 46	96.08 104	97.43 17

v10		86.54 89	87.10 82	84.84 121	88.16 174	63.28 197	86.64 109	92.20 81	75.42 142	92.81 46	94.50 67	74.05 170	94.06 73	83.88 54	96.28 94	97.17 27

CLD-MVS		83.18 163	82.64 165	84.79 123	89.05 151	67.82 163	77.93 270	92.52 73	68.33 223	85.07 175	81.54 312	82.06 83	92.96 132	69.35 190	97.91 43	93.57 117
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
114514_t		83.10 165	82.54 168	84.77 124	92.90 67	69.10 155	86.65 108	90.62 132	54.66 306	81.46 229	90.81 174	76.98 134	94.38 61	72.62 169	96.18 98	90.82 193

V9		86.96 79	87.70 73	84.74 125	88.52 163	63.27 198	87.31 91	91.45 108	78.28 99	93.43 32	95.45 39	74.59 164	93.57 98	84.23 50	96.01 107	97.38 18

Anonymous20231211		88.40 63	89.62 50	84.73 126	90.46 131	65.27 176	88.86 68	93.02 57	87.15 19	93.05 38	97.10 6	82.28 79	92.02 154	76.70 138	97.99 40	96.88 35

MAR-MVS		80.24 201	78.74 207	84.73 126	86.87 218	78.18 77	85.75 118	87.81 190	65.67 246	77.84 269	78.50 327	73.79 175	90.53 194	61.59 247	90.87 234	85.49 259
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
PVSNet_Blended_VisFu		81.55 183	80.49 195	84.70 128	91.58 104	73.24 116	84.21 139	91.67 95	62.86 264	80.94 235	87.16 234	67.27 227	92.87 138	69.82 187	88.94 257	87.99 233

V14		86.75 85	87.46 76	84.62 129	88.35 168	63.00 203	87.02 97	91.42 111	77.78 105	93.27 35	95.23 45	74.22 167	93.56 101	83.95 53	95.93 110	97.31 21

原ACMM1				84.60 130	92.81 72	74.01 111		91.50 100	62.59 266	82.73 215	90.67 179	76.53 142	94.25 64	69.24 191	95.69 120	85.55 257

v15		86.56 88	87.25 80	84.51 131	88.15 175	62.72 208	86.72 107	91.40 113	77.38 110	93.11 37	95.00 50	73.93 172	93.55 102	83.67 57	95.86 114	97.26 22

v11		86.96 79	87.78 72	84.51 131	88.50 164	62.60 212	87.21 92	91.63 97	78.08 103	93.40 33	95.56 37	75.07 153	93.57 98	84.46 47	96.08 104	97.36 20

PEN-MVS		90.03 36	91.88 12	84.48 133	96.57 5	58.88 253	88.95 65	93.19 48	91.62 3	96.01 6	96.16 22	87.02 37	95.60 23	78.69 118	98.72 10	98.97 3

PS-CasMVS		90.06 34	91.92 9	84.47 134	96.56 6	58.83 254	89.04 64	92.74 67	91.40 4	96.12 4	96.06 25	87.23 34	95.57 24	79.42 113	98.74 7	99.00 2

v17		86.32 93	86.95 87	84.44 135	88.00 177	62.62 211	86.74 105	91.48 102	77.17 118	92.74 47	94.56 63	73.74 176	93.53 103	83.27 60	94.87 145	97.18 26

v16		86.24 96	86.85 90	84.43 136	87.96 179	62.59 213	86.73 106	91.48 102	77.17 118	92.67 50	94.55 64	73.63 177	93.52 104	83.26 61	94.16 160	97.17 27

casdiffmvs		82.99 166	82.51 169	84.42 137	86.34 226	67.92 161	87.86 81	92.28 79	60.95 279	81.12 233	93.08 105	76.07 145	93.43 112	79.41 114	85.45 291	91.93 165

CP-MVSNet		89.27 53	90.91 33	84.37 138	96.34 8	58.61 256	88.66 73	92.06 84	90.78 5	95.67 9	95.17 46	81.80 91	95.54 27	79.00 116	98.69 11	98.95 4

v8		86.22 98	86.83 91	84.36 139	87.82 185	62.35 217	86.42 112	91.33 116	76.78 123	92.73 48	94.48 68	73.41 184	93.72 85	83.10 62	95.41 124	97.01 32

semantic-postprocess				84.34 140	83.93 268	69.66 147		81.09 249	72.43 177	86.47 161	90.19 189	57.56 270	93.15 126	77.45 130	86.39 284	90.22 207

v18		85.99 103	86.55 94	84.30 141	87.73 191	62.29 221	86.40 113	91.49 101	76.64 124	92.40 55	94.20 80	73.28 188	93.52 104	82.87 66	93.99 164	97.09 29

v7		84.81 121	85.00 118	84.23 142	88.15 175	63.27 198	83.79 154	91.39 114	71.10 202	90.07 85	91.28 151	74.04 171	93.63 89	81.48 82	93.67 174	95.79 53

v1192		84.57 126	84.69 127	84.21 143	87.75 190	62.88 205	83.02 178	91.43 109	69.08 219	89.98 90	90.89 171	72.70 199	93.62 93	82.41 71	94.97 140	96.13 44

DTE-MVSNet		89.98 38	91.91 11	84.21 143	96.51 7	57.84 257	88.93 67	92.84 64	91.92 2	96.16 3	96.23 20	86.95 38	95.99 7	79.05 115	98.57 16	98.80 6

MVSFormer		82.23 175	81.57 182	84.19 145	85.54 244	69.26 151	91.98 25	90.08 153	71.54 198	76.23 281	85.07 264	58.69 263	94.27 62	86.26 28	88.77 258	89.03 223

1121		80.86 192	79.81 203	84.02 146	93.93 45	78.70 75	81.64 215	80.18 253	55.43 303	83.67 201	91.15 158	71.29 213	91.41 171	67.95 206	93.06 190	81.96 308

v1144		84.54 129	84.72 125	84.00 147	87.67 193	62.55 214	82.97 179	90.93 126	70.32 208	89.80 98	90.99 166	73.50 182	93.48 108	81.69 81	94.65 154	95.97 50

EG-PatchMatch MVS		84.08 146	84.11 146	83.98 148	92.22 87	72.61 120	82.20 204	87.02 204	72.63 176	88.86 119	91.02 165	78.52 115	91.11 176	73.41 161	91.09 223	88.21 229

IterMVS-LS		84.73 123	84.98 119	83.96 149	87.35 200	63.66 190	83.25 173	89.88 158	76.06 128	89.62 106	92.37 127	73.40 186	92.52 145	78.16 123	94.77 150	95.69 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	76.49 14	89.34 52	91.14 27	83.96 149	92.50 77	70.36 144	89.55 54	93.84 29	81.89 58	94.70 15	95.44 40	90.69 9	88.31 237	83.33 59	98.30 27	93.20 125
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
alignmvs		83.94 151	83.98 149	83.80 151	87.80 189	67.88 162	84.54 136	91.42 111	73.27 168	88.41 130	87.96 223	72.33 204	90.83 185	76.02 142	94.11 161	92.69 138

v1921920		84.23 140	84.37 143	83.79 152	87.64 195	61.71 225	82.91 181	91.20 120	67.94 228	90.06 86	90.34 185	72.04 208	93.59 94	82.32 73	94.91 141	96.07 46

PM-MVS		80.20 202	79.00 205	83.78 153	88.17 173	86.66 13	81.31 220	66.81 338	69.64 213	88.33 132	90.19 189	64.58 236	83.63 292	71.99 175	90.03 246	81.06 326

v1neww		84.43 131	84.66 129	83.75 154	87.81 186	62.34 218	83.59 158	90.27 148	72.33 181	89.93 93	91.22 153	73.28 188	93.29 116	80.25 101	93.25 185	95.62 61

v7new		84.43 131	84.66 129	83.75 154	87.81 186	62.34 218	83.59 158	90.27 148	72.33 181	89.93 93	91.22 153	73.28 188	93.29 116	80.25 101	93.25 185	95.62 61

v6		84.43 131	84.66 129	83.75 154	87.81 186	62.34 218	83.59 158	90.26 150	72.33 181	89.94 91	91.19 157	73.30 187	93.29 116	80.26 100	93.26 184	95.62 61

V42		83.47 160	83.37 156	83.75 154	83.16 276	63.33 196	81.31 220	90.23 151	69.51 214	90.91 78	90.81 174	74.16 168	92.29 149	80.06 103	90.22 245	95.62 61

v144192		84.24 139	84.41 138	83.71 158	87.59 196	61.57 230	82.95 180	91.03 124	67.82 231	89.80 98	90.49 183	73.28 188	93.51 107	81.88 80	94.89 142	96.04 48

v1240		84.30 137	84.51 136	83.65 159	87.65 194	61.26 234	82.85 182	91.54 99	67.94 228	90.68 81	90.65 180	71.71 211	93.64 88	82.84 68	94.78 148	96.07 46

v2v482		84.09 145	84.24 145	83.62 160	87.13 210	61.40 231	82.71 188	89.71 162	72.19 185	89.55 110	91.41 150	70.70 216	93.20 121	81.02 86	93.76 171	96.25 42

canonicalmvs		85.50 110	86.14 101	83.58 161	87.97 178	67.13 165	87.55 87	94.32 12	73.44 161	88.47 128	87.54 231	86.45 44	91.06 178	75.76 143	93.76 171	92.54 147

Effi-MVS+		83.90 152	84.01 148	83.57 162	87.22 208	65.61 175	86.55 111	92.40 75	78.64 95	81.34 232	84.18 275	83.65 63	92.93 134	74.22 150	87.87 270	92.17 159

AdaColmap		83.66 155	83.69 153	83.57 162	90.05 140	72.26 125	86.29 114	90.00 156	78.19 101	81.65 227	87.16 234	83.40 66	94.24 65	61.69 244	94.76 151	84.21 277

v1141		84.16 142	84.38 140	83.52 164	87.32 202	61.70 227	82.79 184	89.74 159	71.90 195	89.64 103	91.12 160	72.68 200	93.10 127	80.39 99	93.80 169	95.75 55

divwei89l23v2f112		84.16 142	84.38 140	83.52 164	87.32 202	61.70 227	82.79 184	89.74 159	71.90 195	89.64 103	91.12 160	72.68 200	93.10 127	80.40 97	93.81 168	95.75 55

v1		84.16 142	84.38 140	83.52 164	87.33 201	61.71 225	82.79 184	89.73 161	71.89 197	89.64 103	91.11 162	72.72 197	93.10 127	80.40 97	93.79 170	95.75 55

testing_2		84.36 134	84.64 132	83.50 167	86.74 219	63.97 188	84.56 134	90.31 142	66.22 239	91.62 67	94.55 64	75.88 146	91.95 155	77.02 137	94.89 142	94.56 83

Anonymous20240529		86.20 99	87.13 81	83.42 168	90.19 135	64.55 183	84.55 135	90.71 128	85.85 24	89.94 91	95.24 44	82.13 81	90.40 197	69.19 194	96.40 90	95.31 71

PAPM_NR		83.23 162	83.19 159	83.33 169	90.90 122	65.98 172	88.19 77	90.78 127	78.13 102	80.87 237	87.92 226	73.49 183	92.42 146	70.07 185	88.40 261	91.60 173

TAMVS		78.08 212	76.36 225	83.23 170	90.62 127	72.87 117	79.08 260	80.01 255	61.72 274	81.35 231	86.92 237	63.96 239	88.78 230	50.61 312	93.01 192	88.04 232

VDD-MVS		84.23 140	84.58 134	83.20 171	91.17 117	65.16 178	83.25 173	84.97 228	79.79 76	87.18 144	94.27 76	74.77 161	90.89 184	69.24 191	96.54 84	93.55 120

EI-MVSNet		82.61 169	82.42 171	83.20 171	83.25 274	63.66 190	83.50 165	85.07 224	76.06 128	86.55 155	85.10 262	73.41 184	90.25 199	78.15 124	90.67 239	95.68 59

CDS-MVSNet		77.32 218	75.40 234	83.06 173	89.00 153	72.48 122	77.90 271	82.17 241	60.81 280	78.94 263	83.49 282	59.30 260	88.76 231	54.64 298	92.37 201	87.93 235
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ambc				82.98 174	90.55 130	64.86 179	88.20 76	89.15 170		89.40 113	93.96 89	71.67 212	91.38 173	78.83 117	96.55 83	92.71 137

新几何1				82.95 175	93.96 44	78.56 76		80.24 252	55.45 302	83.93 199	91.08 163	71.19 214	88.33 236	65.84 220	93.07 189	81.95 309

xiu_mvs_v1_base_debu		80.84 193	80.14 199	82.93 176	88.31 169	71.73 133	79.53 243	87.17 197	65.43 247	79.59 256	82.73 295	76.94 135	90.14 205	73.22 162	88.33 262	86.90 245

xiu_mvs_v1_base		80.84 193	80.14 199	82.93 176	88.31 169	71.73 133	79.53 243	87.17 197	65.43 247	79.59 256	82.73 295	76.94 135	90.14 205	73.22 162	88.33 262	86.90 245

xiu_mvs_v1_base_debi		80.84 193	80.14 199	82.93 176	88.31 169	71.73 133	79.53 243	87.17 197	65.43 247	79.59 256	82.73 295	76.94 135	90.14 205	73.22 162	88.33 262	86.90 245

MVP-Stereo		75.81 238	73.51 258	82.71 179	89.35 146	73.62 112	80.06 235	85.20 221	60.30 283	73.96 299	87.94 224	57.89 268	89.45 214	52.02 306	74.87 339	85.06 263
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FIs		85.35 112	86.27 98	82.60 180	91.86 96	57.31 261	85.10 126	93.05 53	75.83 133	91.02 75	93.97 87	73.57 181	92.91 136	73.97 154	98.02 38	97.58 14

FC-MVSNet-test		85.93 105	87.05 84	82.58 181	92.25 85	56.44 269	85.75 118	93.09 51	77.33 115	91.94 63	94.65 62	74.78 160	93.41 113	75.11 147	98.58 15	97.88 9

QAPM		82.59 170	82.59 167	82.58 181	86.44 220	66.69 169	89.94 44	90.36 139	67.97 227	84.94 178	92.58 121	72.71 198	92.18 150	70.63 182	87.73 272	88.85 226

pmmvs-eth3d		78.42 210	77.04 216	82.57 183	87.44 198	74.41 109	80.86 230	79.67 256	55.68 301	84.69 182	90.31 188	60.91 249	85.42 275	62.20 239	91.59 212	87.88 236

HyFIR lowres test		75.12 243	72.66 265	82.50 184	91.44 110	65.19 177	72.47 309	87.31 194	46.79 342	80.29 252	84.30 274	52.70 288	92.10 153	51.88 311	86.73 280	90.22 207

Fast-Effi-MVS+		81.04 190	80.57 192	82.46 185	87.50 197	63.22 200	78.37 266	89.63 163	68.01 225	81.87 222	82.08 307	82.31 78	92.65 143	67.10 208	88.30 266	91.51 176

jason		77.42 217	75.75 231	82.43 186	87.10 213	69.27 150	77.99 269	81.94 244	51.47 326	77.84 269	85.07 264	60.32 252	89.00 223	70.74 180	89.27 254	89.03 223
jason: jason.
lupinMVS		76.37 232	74.46 243	82.09 187	85.54 244	69.26 151	76.79 278	80.77 251	50.68 333	76.23 281	82.82 293	58.69 263	88.94 224	69.85 186	88.77 258	88.07 230

DELS-MVS		81.44 184	81.25 185	82.03 188	84.27 261	62.87 206	76.47 285	92.49 74	70.97 203	81.64 228	83.83 278	75.03 154	92.70 140	74.29 149	92.22 207	90.51 202
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
OpenMVS	76.72 13	81.98 180	82.00 177	81.93 189	84.42 259	68.22 159	88.50 75	89.48 166	66.92 234	81.80 226	91.86 136	72.59 202	90.16 204	71.19 176	91.25 221	87.40 240

pmmvs6		86.52 90	88.06 67	81.90 190	92.22 87	62.28 222	84.66 132	89.15 170	83.54 37	89.85 96	97.32 3	88.08 27	86.80 257	70.43 184	97.30 64	96.62 38

MSLP-MVS++		85.00 118	86.03 103	81.90 190	91.84 97	71.56 138	86.75 104	93.02 57	75.95 131	87.12 145	89.39 201	77.98 120	89.40 216	77.46 129	94.78 148	84.75 270

Test4		81.31 185	81.13 188	81.88 192	84.89 251	63.05 202	82.37 195	90.50 134	62.75 265	89.00 118	88.29 219	67.55 226	91.68 163	73.55 159	91.24 222	90.89 189

GBi-Net		82.02 178	82.07 175	81.85 193	86.38 222	61.05 237	86.83 100	88.27 182	72.43 177	86.00 164	95.64 34	63.78 240	90.68 190	65.95 217	93.34 181	93.82 107

test1		82.02 178	82.07 175	81.85 193	86.38 222	61.05 237	86.83 100	88.27 182	72.43 177	86.00 164	95.64 34	63.78 240	90.68 190	65.95 217	93.34 181	93.82 107

FMVSNet1		84.55 127	85.45 112	81.85 193	90.27 134	61.05 237	86.83 100	88.27 182	78.57 96	89.66 102	95.64 34	75.43 149	90.68 190	69.09 195	95.33 126	93.82 107

v148		82.31 173	82.48 170	81.81 196	85.59 243	59.66 248	81.47 218	86.02 213	72.85 173	88.05 133	90.65 180	70.73 215	90.91 183	75.15 146	91.79 209	94.87 79

PVSNet_BlendedMVS		78.80 208	77.84 211	81.65 197	84.43 257	63.41 193	79.49 246	90.44 136	61.70 275	75.43 288	87.07 236	69.11 220	91.44 169	60.68 253	92.24 205	90.11 211

BH-RMVSNet		80.53 196	80.22 198	81.49 198	87.19 209	66.21 171	77.79 272	86.23 210	74.21 152	83.69 200	88.50 212	73.25 192	90.75 187	63.18 236	87.90 269	87.52 238

test_normal		81.23 189	81.16 187	81.43 199	84.77 254	61.99 224	81.46 219	86.95 206	63.16 262	87.22 143	89.63 197	73.62 178	91.65 164	72.92 167	90.70 238	90.65 198

API-MVS		82.28 174	82.61 166	81.30 200	86.29 228	69.79 145	88.71 72	87.67 191	78.42 98	82.15 219	84.15 277	77.98 120	91.59 165	65.39 223	92.75 196	82.51 301

DI_MVS_plusplus_test		81.27 187	81.26 184	81.29 201	84.98 249	61.65 229	81.98 207	87.25 196	63.56 257	87.56 140	89.60 198	73.62 178	91.83 160	72.20 172	90.59 243	90.38 205

VDDNet		84.35 136	85.39 113	81.25 202	95.13 29	59.32 251	85.42 123	81.11 248	86.41 22	87.41 142	96.21 21	73.61 180	90.61 193	66.33 215	96.85 74	93.81 110

MVSTER		77.09 220	75.70 232	81.25 202	75.27 337	61.08 236	77.49 276	85.07 224	60.78 281	86.55 155	88.68 210	43.14 332	90.25 199	73.69 157	90.67 239	92.42 148

PAPR		78.84 207	78.10 210	81.07 204	85.17 248	60.22 244	82.21 202	90.57 133	62.51 267	75.32 290	84.61 271	74.99 155	92.30 148	59.48 266	88.04 268	90.68 196

WR-MVS		83.56 157	84.40 139	81.06 205	93.43 55	54.88 280	78.67 265	85.02 226	81.24 63	90.74 80	91.56 147	72.85 195	91.08 177	68.00 204	98.04 35	97.23 24

BH-untuned		80.96 191	80.99 189	80.84 206	88.55 162	68.23 158	80.33 234	88.46 176	72.79 174	86.55 155	86.76 238	74.72 162	91.77 162	61.79 243	88.99 256	82.52 300

MIMVSNet1		83.63 156	84.59 133	80.74 207	94.06 42	62.77 207	82.72 187	84.53 230	77.57 108	90.34 82	95.92 27	76.88 141	85.83 272	61.88 242	97.42 61	93.62 115

pmmvs4		74.92 246	72.98 263	80.73 208	84.95 250	71.71 136	76.23 287	77.59 263	52.83 315	77.73 272	86.38 241	56.35 275	84.97 279	57.72 279	87.05 278	85.51 258

cascas		76.29 233	74.81 239	80.72 209	84.47 256	62.94 204	73.89 304	87.34 193	55.94 300	75.16 292	76.53 335	63.97 238	91.16 175	65.00 224	90.97 231	88.06 231

FMVSNet2		81.31 185	81.61 181	80.41 210	86.38 222	58.75 255	83.93 148	86.58 208	72.43 177	87.65 138	92.98 109	63.78 240	90.22 202	66.86 210	93.92 166	92.27 155

MSDG		80.06 204	79.99 202	80.25 211	83.91 269	68.04 160	77.51 275	89.19 169	77.65 106	81.94 221	83.45 283	76.37 143	86.31 265	63.31 235	86.59 281	86.41 248

MVS_Test		82.47 172	83.22 157	80.22 212	82.62 280	57.75 259	82.54 192	91.96 88	71.16 201	82.89 213	92.52 123	77.41 127	90.50 195	80.04 104	87.84 271	92.40 150

CANet_DTU		77.81 214	77.05 215	80.09 213	81.37 287	59.90 247	83.26 172	88.29 181	69.16 218	67.83 327	83.72 279	60.93 248	89.47 212	69.22 193	89.70 250	90.88 190

diffmvs		81.78 182	82.36 172	80.02 214	79.06 307	59.93 246	83.30 170	88.41 179	73.47 159	78.38 266	92.05 133	75.85 147	88.38 235	80.73 93	89.98 248	91.76 169

pm-mvs1		83.69 154	84.95 120	79.91 215	90.04 141	59.66 248	82.43 193	87.44 192	75.52 139	87.85 136	95.26 43	81.25 97	85.65 274	68.74 199	96.04 106	94.42 91

CMPMVS	59.41 20	75.12 243	73.57 251	79.77 216	75.84 329	67.22 164	81.21 223	82.18 240	50.78 331	76.50 277	87.66 229	55.20 282	82.99 294	62.17 241	90.64 242	89.09 222
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_Blended		76.49 231	75.40 234	79.76 217	84.43 257	63.41 193	75.14 295	90.44 136	57.36 294	75.43 288	78.30 328	69.11 220	91.44 169	60.68 253	87.70 273	84.42 273

TR-MVS		76.77 227	75.79 229	79.72 218	86.10 240	65.79 174	77.14 277	83.02 234	65.20 251	81.40 230	82.10 306	66.30 230	90.73 189	55.57 289	85.27 294	82.65 296

VPA-MVSNet		83.47 160	84.73 123	79.69 219	90.29 133	57.52 260	81.30 222	88.69 174	76.29 126	87.58 139	94.44 69	80.60 102	87.20 245	66.60 214	96.82 77	94.34 93

IB-MVS	62.13 19	71.64 277	68.97 291	79.66 220	80.80 295	62.26 223	73.94 303	76.90 267	63.27 261	68.63 323	76.79 333	33.83 353	91.84 159	59.28 267	87.26 276	84.88 268
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
FMVSNet3		78.80 208	78.55 208	79.57 221	82.89 278	56.89 266	81.76 212	85.77 215	69.04 220	86.00 164	90.44 184	51.75 290	90.09 208	65.95 217	93.34 181	91.72 170

testdata				79.54 222	92.87 68	72.34 123		80.14 254	59.91 285	85.47 174	91.75 143	67.96 225	85.24 276	68.57 202	92.18 208	81.06 326

GA-MVS		75.83 237	74.61 240	79.48 223	81.87 283	59.25 252	73.42 307	82.88 235	68.68 222	79.75 255	81.80 309	50.62 292	89.46 213	66.85 211	85.64 290	89.72 213

MDA-MVSNet-bldmvs		77.47 216	76.90 217	79.16 224	79.03 308	64.59 181	66.58 331	75.67 274	73.15 170	88.86 119	88.99 206	66.94 228	81.23 301	64.71 225	88.22 267	91.64 172

LFMVS		80.15 203	80.56 193	78.89 225	89.19 150	55.93 271	85.22 125	73.78 286	82.96 44	84.28 196	92.72 118	57.38 271	90.07 209	63.80 231	95.75 118	90.68 196

TransMVSNet (Re)		84.02 148	85.74 106	78.85 226	91.00 120	55.20 279	82.29 198	87.26 195	79.65 79	88.38 131	95.52 38	83.00 69	86.88 249	67.97 205	96.60 82	94.45 90

Gipuma		84.44 130	86.33 96	78.78 227	84.20 264	73.57 113	89.55 54	90.44 136	84.24 32	84.38 191	94.89 54	76.35 144	80.40 304	76.14 141	96.80 78	82.36 302
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet-Re		83.48 159	85.06 116	78.75 228	85.94 241	55.75 274	80.05 236	94.27 13	76.47 125	96.09 5	94.54 66	83.31 67	89.75 211	59.95 256	94.89 142	90.75 194

OpenMVS_ROB	70.19 17	77.77 215	77.46 213	78.71 229	84.39 260	61.15 235	81.18 224	82.52 237	62.45 269	83.34 206	87.37 233	66.20 231	88.66 233	64.69 226	85.02 298	86.32 249

IterMVS		76.91 222	76.34 226	78.64 230	80.91 292	64.03 186	76.30 286	79.03 257	64.88 254	83.11 210	89.16 203	59.90 256	84.46 284	68.61 201	85.15 297	87.42 239
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PS-MVSNAJ		77.04 221	76.53 224	78.56 231	87.09 214	61.40 231	75.26 294	87.13 200	61.25 277	74.38 298	77.22 332	76.94 135	90.94 180	64.63 227	84.83 301	83.35 289

xiu_mvs_v2_base		77.19 219	76.75 218	78.52 232	87.01 215	61.30 233	75.55 293	87.12 202	61.24 278	74.45 296	78.79 326	77.20 129	90.93 181	64.62 228	84.80 302	83.32 290

Anonymous202405211		80.51 197	81.19 186	78.49 233	88.48 165	57.26 262	76.63 281	82.49 238	81.21 64	84.30 195	92.24 131	67.99 224	86.24 266	62.22 238	95.13 134	91.98 164

MG-MVS		80.32 199	80.94 190	78.47 234	88.18 172	52.62 296	82.29 198	85.01 227	72.01 187	79.24 261	92.54 122	69.36 218	93.36 115	70.65 181	89.19 255	89.45 215

tfpnnormal		81.79 181	82.95 161	78.31 235	88.93 154	55.40 275	80.83 231	82.85 236	76.81 122	85.90 167	94.14 82	74.58 165	86.51 262	66.82 213	95.68 121	93.01 130

Baseline_NR-MVSNet		84.00 149	85.90 104	78.29 236	91.47 109	53.44 289	82.29 198	87.00 205	79.06 88	89.55 110	95.72 31	77.20 129	86.14 268	72.30 171	98.51 18	95.28 72

PatchMatch-RL		74.48 250	73.22 260	78.27 237	87.70 192	85.26 30	75.92 288	70.09 317	64.34 256	76.09 283	81.25 314	65.87 234	78.07 310	53.86 300	83.82 306	71.48 342

CHOSEN 1792x2688		72.45 270	70.56 281	78.13 238	90.02 142	63.08 201	68.72 322	83.16 233	42.99 351	75.92 284	85.46 256	57.22 273	85.18 278	49.87 316	81.67 318	86.14 251

BH-w/o		76.57 229	76.07 228	78.10 239	86.88 217	65.92 173	77.63 273	86.33 209	65.69 245	80.89 236	79.95 321	68.97 222	90.74 188	53.01 303	85.25 295	77.62 331

1112_ss		74.82 248	73.74 248	78.04 240	89.57 144	60.04 245	76.49 284	87.09 203	54.31 307	73.66 301	79.80 322	60.25 253	86.76 259	58.37 273	84.15 305	87.32 241

TinyColmap		81.25 188	82.34 173	77.99 241	85.33 247	60.68 241	82.32 197	88.33 180	71.26 200	86.97 150	92.22 132	77.10 132	86.98 248	62.37 237	95.17 133	86.31 250

Vis-MVSNet (Re-imp)		77.82 213	77.79 212	77.92 242	88.82 156	51.29 309	83.28 171	71.97 302	74.04 153	82.23 217	89.78 195	57.38 271	89.41 215	57.22 280	95.41 124	93.05 129

thres400		75.14 241	74.23 245	77.86 243	86.24 229	52.12 298	79.24 256	73.87 284	73.34 163	81.82 224	84.60 272	46.02 306	88.80 226	51.98 307	90.99 227	92.66 139

thres600view7		75.97 236	75.35 236	77.85 244	87.01 215	51.84 305	80.45 233	73.26 291	75.20 144	83.10 211	86.31 247	45.54 312	89.05 222	55.03 295	92.24 205	92.66 139

JIA-IIPM		69.41 295	66.64 305	77.70 245	73.19 346	71.24 139	75.67 290	65.56 339	70.42 206	65.18 336	92.97 110	33.64 354	83.06 293	53.52 302	69.61 351	78.79 330

view600		76.79 223	76.54 220	77.56 246	87.91 181	50.77 315	81.92 208	71.35 310	77.38 110	84.62 183	88.40 214	45.18 322	89.26 218	58.58 269	93.49 176	92.66 139

view800		76.79 223	76.54 220	77.56 246	87.91 181	50.77 315	81.92 208	71.35 310	77.38 110	84.62 183	88.40 214	45.18 322	89.26 218	58.58 269	93.49 176	92.66 139

conf0.05thres1000		76.79 223	76.54 220	77.56 246	87.91 181	50.77 315	81.92 208	71.35 310	77.38 110	84.62 183	88.40 214	45.18 322	89.26 218	58.58 269	93.49 176	92.66 139

tfpn		76.79 223	76.54 220	77.56 246	87.91 181	50.77 315	81.92 208	71.35 310	77.38 110	84.62 183	88.40 214	45.18 322	89.26 218	58.58 269	93.49 176	92.66 139

tfpn111		76.03 235	75.53 233	77.53 250	87.27 204	51.88 301	81.07 225	73.26 291	75.68 135	83.25 207	86.37 242	45.54 312	89.38 217	55.07 294	92.26 204	91.34 181

EPNet_dtu		72.87 266	71.33 279	77.49 251	77.72 317	60.55 242	82.35 196	75.79 272	66.49 237	58.39 354	81.06 315	53.68 286	85.98 269	53.55 301	92.97 193	85.95 253
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
conf200view11		75.62 239	75.05 237	77.34 252	87.27 204	51.88 301	81.07 225	73.26 291	75.68 135	83.25 207	86.37 242	45.54 312	88.80 226	51.98 307	90.99 227	91.34 181

EU-MVSNet		75.12 243	74.43 244	77.18 253	83.11 277	59.48 250	85.71 120	82.43 239	39.76 354	85.64 170	88.76 208	44.71 328	87.88 240	73.86 156	85.88 288	84.16 278

ab-mvs		79.67 205	80.56 193	76.99 254	88.48 165	56.93 264	84.70 131	86.06 212	68.95 221	80.78 238	93.08 105	75.30 151	84.62 283	56.78 282	90.90 233	89.43 217

PAPM		71.77 276	70.06 287	76.92 255	86.39 221	53.97 283	76.62 282	86.62 207	53.44 312	63.97 341	84.73 270	57.79 269	92.34 147	39.65 342	81.33 321	84.45 272

ppachtmachnet_test		74.73 249	74.00 247	76.90 256	80.71 296	56.89 266	71.53 313	78.42 259	58.24 290	79.32 260	82.92 292	57.91 267	84.26 286	65.60 222	91.36 214	89.56 214

Patchmatch-RL test		74.48 250	73.68 249	76.89 257	84.83 252	66.54 170	72.29 310	69.16 322	57.70 292	86.76 151	86.33 245	45.79 311	82.59 296	69.63 188	90.65 241	81.54 316

CR-MVSNet		74.00 255	73.04 262	76.85 258	79.58 300	62.64 209	82.58 189	76.90 267	50.50 334	75.72 286	92.38 124	48.07 299	84.07 287	68.72 200	82.91 312	83.85 281

RPMNet		76.06 234	75.79 229	76.85 258	79.58 300	62.64 209	82.58 189	71.75 306	74.80 146	75.72 286	92.59 119	48.69 297	84.07 287	73.48 160	82.91 312	83.85 281

tfpn200view9		74.86 247	74.23 245	76.74 260	86.24 229	52.12 298	79.24 256	73.87 284	73.34 163	81.82 224	84.60 272	46.02 306	88.80 226	51.98 307	90.99 227	89.31 218

thres100view900		75.45 240	75.05 237	76.66 261	87.27 204	51.88 301	81.07 225	73.26 291	75.68 135	83.25 207	86.37 242	45.54 312	88.80 226	51.98 307	90.99 227	89.31 218

VNet		79.31 206	80.27 197	76.44 262	87.92 180	53.95 284	75.58 292	84.35 231	74.39 151	82.23 217	90.72 176	72.84 196	84.39 285	60.38 255	93.98 165	90.97 186

Test_1112_low_res		73.90 256	73.08 261	76.35 263	90.35 132	55.95 270	73.40 308	86.17 211	50.70 332	73.14 302	85.94 250	58.31 265	85.90 271	56.51 283	83.22 309	87.20 242

USDC		76.63 228	76.73 219	76.34 264	83.46 272	57.20 263	80.02 237	88.04 186	52.14 321	83.65 203	91.25 152	63.24 243	86.65 261	54.66 297	94.11 161	85.17 261

CVMVSNet		72.62 268	71.41 278	76.28 265	83.25 274	60.34 243	83.50 165	79.02 258	37.77 355	76.33 279	85.10 262	49.60 295	87.41 243	70.54 183	77.54 335	81.08 324

mvs_anonymous		78.13 211	78.76 206	76.23 266	79.24 305	50.31 321	78.69 264	84.82 229	61.60 276	83.09 212	92.82 114	73.89 174	87.01 246	68.33 203	86.41 283	91.37 179

conf0.01		74.17 253	73.53 252	76.08 267	86.13 234	50.06 324	79.45 247	68.54 323	72.01 187	80.76 239	82.50 298	41.39 336	86.83 251	59.66 259	91.36 214	91.34 181

conf0.002		74.17 253	73.53 252	76.08 267	86.13 234	50.06 324	79.45 247	68.54 323	72.01 187	80.76 239	82.50 298	41.39 336	86.83 251	59.66 259	91.36 214	91.34 181

VPNet		80.25 200	81.68 179	75.94 269	92.46 78	47.98 334	76.70 280	81.67 246	73.45 160	84.87 179	92.82 114	74.66 163	86.51 262	61.66 245	96.85 74	93.33 121

tpmp4_e23		69.43 293	67.33 300	75.72 270	78.53 312	52.75 293	82.13 206	74.91 277	49.23 339	66.37 330	84.17 276	41.28 342	88.67 232	49.73 317	79.63 325	85.75 256

ANet_high		83.17 164	85.68 108	75.65 271	81.24 288	45.26 339	79.94 239	92.91 60	83.83 36	91.33 72	96.88 10	80.25 105	85.92 270	68.89 197	95.89 112	95.76 54

1314		73.22 263	72.56 268	75.20 272	80.41 299	57.84 257	81.64 215	85.36 219	51.68 324	73.10 303	76.65 334	61.45 247	85.19 277	63.54 232	79.21 330	82.59 297

MVS		73.21 264	72.59 267	75.06 273	80.97 291	60.81 240	81.64 215	85.92 214	46.03 345	71.68 310	77.54 329	68.47 223	89.77 210	55.70 288	85.39 292	74.60 337

no-one		71.52 279	70.43 284	74.81 274	78.45 313	63.41 193	57.73 348	77.03 266	51.46 327	77.17 275	90.33 186	54.96 284	80.35 305	47.41 326	99.29 2	80.68 328

HY-MVS	64.64 18	73.03 265	72.47 269	74.71 275	83.36 273	54.19 282	82.14 205	81.96 242	56.76 299	69.57 320	86.21 248	60.03 254	84.83 282	49.58 318	82.65 314	85.11 262

thres200		72.34 272	71.55 277	74.70 276	83.48 271	51.60 306	75.02 296	73.71 287	70.14 210	78.56 264	80.57 316	46.20 304	88.20 238	46.99 329	89.29 252	84.32 275

N_pmnet		70.20 286	68.80 293	74.38 277	80.91 292	84.81 35	59.12 345	76.45 271	55.06 304	75.31 291	82.36 304	55.74 278	54.82 356	47.02 328	87.24 277	83.52 285

thresconf0.02		73.65 258	73.53 252	73.98 278	86.13 234	50.06 324	79.45 247	68.54 323	72.01 187	80.76 239	82.50 298	41.39 336	86.83 251	59.66 259	91.36 214	85.06 263

tfpn_n400		73.65 258	73.53 252	73.98 278	86.13 234	50.06 324	79.45 247	68.54 323	72.01 187	80.76 239	82.50 298	41.39 336	86.83 251	59.66 259	91.36 214	85.06 263

tfpnconf		73.65 258	73.53 252	73.98 278	86.13 234	50.06 324	79.45 247	68.54 323	72.01 187	80.76 239	82.50 298	41.39 336	86.83 251	59.66 259	91.36 214	85.06 263

tfpnview11		73.65 258	73.53 252	73.98 278	86.13 234	50.06 324	79.45 247	68.54 323	72.01 187	80.76 239	82.50 298	41.39 336	86.83 251	59.66 259	91.36 214	85.06 263

CostFormer		69.98 291	68.68 294	73.87 282	77.14 320	50.72 319	79.26 255	74.51 282	51.94 323	70.97 315	84.75 269	45.16 326	87.49 242	55.16 293	79.23 329	83.40 288

Patchmtry		76.56 230	77.46 213	73.83 283	79.37 304	46.60 336	82.41 194	76.90 267	73.81 156	85.56 172	92.38 124	48.07 299	83.98 289	63.36 234	95.31 129	90.92 188

tfpn1000		73.63 262	73.58 250	73.79 284	85.46 246	50.31 321	79.99 238	68.18 329	72.33 181	80.66 245	83.05 286	39.80 347	86.74 260	60.96 251	91.78 210	84.32 275

FMVSNet5		72.10 274	71.69 274	73.32 285	81.57 286	53.02 292	76.77 279	78.37 260	63.31 260	76.37 278	91.85 137	36.68 350	78.98 308	47.87 325	92.45 200	87.95 234

tpm2		68.45 298	66.83 302	73.30 286	78.93 309	48.50 331	79.76 240	71.76 305	47.50 341	69.92 319	83.60 280	42.07 335	88.40 234	48.44 323	79.51 326	83.01 295

tfpn_ndepth		72.54 269	72.30 270	73.24 287	84.81 253	51.42 307	79.24 256	70.49 316	69.26 216	78.48 265	79.80 322	40.16 346	86.77 258	58.08 278	90.43 244	81.53 317

Patchmatch-test1		72.75 267	72.61 266	73.19 288	81.62 285	55.86 272	78.89 262	71.37 309	61.73 273	74.93 293	82.15 305	60.46 251	81.80 298	59.68 258	82.63 316	81.92 310

FPMVS		72.29 273	72.00 272	73.14 289	88.63 161	85.00 32	74.65 299	67.39 332	71.94 194	77.80 271	87.66 229	50.48 293	75.83 317	49.95 314	79.51 326	58.58 354

MS-PatchMatch		70.93 282	70.22 285	73.06 290	81.85 284	62.50 215	73.82 305	77.90 261	52.44 318	75.92 284	81.27 313	55.67 279	81.75 299	55.37 291	77.70 333	74.94 336

LP		69.42 294	68.30 296	72.77 291	71.48 354	56.84 268	73.66 306	74.84 278	63.52 258	70.95 316	83.35 285	49.55 296	77.15 313	57.13 281	70.21 347	84.33 274

pmmvs5		70.73 283	70.07 286	72.72 292	77.03 322	52.73 294	74.14 301	75.65 275	50.36 335	72.17 308	85.37 260	55.42 281	80.67 303	52.86 304	87.59 274	84.77 269

DWT-MVSNet_test		66.43 305	64.37 310	72.63 293	74.86 340	50.86 314	76.52 283	72.74 296	54.06 309	65.50 334	68.30 350	32.13 355	84.84 281	61.63 246	73.59 340	82.19 304

our_test_3		71.85 275	71.59 275	72.62 294	80.71 296	53.78 286	69.72 320	71.71 308	58.80 287	78.03 268	80.51 317	56.61 274	78.84 309	62.20 239	86.04 287	85.23 260

ADS-MVSNet2		65.87 309	63.64 314	72.55 295	73.16 347	56.92 265	67.10 329	74.81 279	49.74 336	66.04 332	82.97 289	46.71 301	77.26 311	42.29 337	69.96 349	83.46 286

PatchmatchNet		69.71 292	68.83 292	72.33 296	77.66 318	53.60 287	79.29 254	69.99 318	57.66 293	72.53 305	82.93 291	46.45 303	80.08 307	60.91 252	72.09 344	83.31 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchFormer-LS_test		67.91 300	66.49 306	72.17 297	75.29 336	51.85 304	75.68 289	73.62 289	57.23 296	68.64 321	68.13 351	42.19 334	82.76 295	64.06 230	73.51 341	81.89 311

tpmvs		70.16 287	69.56 289	71.96 298	74.71 341	48.13 332	79.63 241	75.45 276	65.02 253	70.26 317	81.88 308	45.34 319	85.68 273	58.34 274	75.39 338	82.08 306

tpm cat1		66.76 304	65.21 309	71.42 299	77.09 321	50.62 320	78.01 268	73.68 288	44.89 347	68.64 321	79.00 325	45.51 316	82.42 297	49.91 315	70.15 348	81.23 323

test20.03		73.75 257	74.59 242	71.22 300	81.11 290	51.12 311	70.15 318	72.10 301	70.42 206	80.28 253	91.50 148	64.21 237	74.72 321	46.96 330	94.58 155	87.82 237

Anonymous20231206		71.38 280	71.88 273	69.88 301	86.31 227	54.37 281	70.39 317	74.62 280	52.57 317	76.73 276	88.76 208	59.94 255	72.06 324	44.35 335	93.23 188	83.23 292

pmmvs3		62.47 313	60.02 326	69.80 302	71.58 353	64.00 187	70.52 316	58.44 352	39.77 353	66.05 331	75.84 336	27.10 362	72.28 323	46.15 331	84.77 303	73.11 340

testmv		70.47 285	70.70 280	69.77 303	86.22 231	53.89 285	67.32 328	71.91 303	63.32 259	78.16 267	89.47 200	56.12 277	73.10 322	36.43 348	87.33 275	82.33 303

UnsupCasMVSNet_eth		71.63 278	72.30 270	69.62 304	76.47 325	52.70 295	70.03 319	80.97 250	59.18 286	79.36 259	88.21 220	60.50 250	69.12 332	58.33 275	77.62 334	87.04 243

MIMVSNet		71.09 281	71.59 275	69.57 305	87.23 207	50.07 323	78.91 261	71.83 304	60.20 284	71.26 312	91.76 142	55.08 283	76.09 315	41.06 340	87.02 279	82.54 299

XXY-MVS		74.44 252	76.19 227	69.21 306	84.61 255	52.43 297	71.70 312	77.18 265	60.73 282	80.60 246	90.96 169	75.44 148	69.35 331	56.13 285	88.33 262	85.86 255

YYNet1		70.06 289	70.44 282	68.90 307	73.76 343	53.42 290	58.99 346	67.20 334	58.42 289	87.10 146	85.39 259	59.82 257	67.32 336	59.79 257	83.50 308	85.96 252

MDA-MVSNet_test_wron		70.05 290	70.44 282	68.88 308	73.84 342	53.47 288	58.93 347	67.28 333	58.43 288	87.09 147	85.40 258	59.80 258	67.25 337	59.66 259	83.54 307	85.92 254

PVSNet	58.17 21	66.41 306	65.63 308	68.75 309	81.96 282	49.88 330	62.19 338	72.51 299	51.03 329	68.04 325	75.34 339	50.84 291	74.77 319	45.82 333	82.96 310	81.60 315

test-LLR		67.21 302	66.74 303	68.63 310	76.45 326	55.21 277	67.89 324	67.14 335	62.43 270	65.08 337	72.39 343	43.41 330	69.37 329	61.00 249	84.89 299	81.31 319

test-mter		65.00 311	63.79 312	68.63 310	76.45 326	55.21 277	67.89 324	67.14 335	50.98 330	65.08 337	72.39 343	28.27 360	69.37 329	61.00 249	84.89 299	81.31 319

gg-mvs-nofinetune		68.96 297	69.11 290	68.52 312	76.12 328	45.32 338	83.59 158	55.88 355	86.68 20	64.62 340	97.01 7	30.36 357	83.97 290	44.78 334	82.94 311	76.26 334

UnsupCasMVSNet_bld		69.21 296	69.68 288	67.82 313	79.42 302	51.15 310	67.82 327	75.79 272	54.15 308	77.47 273	85.36 261	59.26 261	70.64 327	48.46 322	79.35 328	81.66 314

tpm		67.95 299	68.08 298	67.55 314	78.74 311	43.53 345	75.60 291	67.10 337	54.92 305	72.23 307	88.10 221	42.87 333	75.97 316	52.21 305	80.95 324	83.15 293

GG-mvs-BLEND				67.16 315	73.36 344	46.54 337	84.15 141	55.04 356		58.64 353	61.95 356	29.93 358	83.87 291	38.71 346	76.92 336	71.07 343

CHOSEN 280x420		59.08 325	56.52 330	66.76 316	76.51 324	64.39 184	49.62 355	59.00 350	43.86 349	55.66 357	68.41 349	35.55 352	68.21 335	43.25 336	76.78 337	67.69 347

WTY-MVS		67.91 300	68.35 295	66.58 317	80.82 294	48.12 333	65.96 332	72.60 297	53.67 311	71.20 313	81.68 311	58.97 262	69.06 333	48.57 321	81.67 318	82.55 298

sss		66.92 303	67.26 301	65.90 318	77.23 319	51.10 312	64.79 333	71.72 307	52.12 322	70.13 318	80.18 319	57.96 266	65.36 346	50.21 313	81.01 323	81.25 321

testgi		72.36 271	74.61 240	65.59 319	80.56 298	42.82 347	68.29 323	73.35 290	66.87 235	81.84 223	89.93 193	72.08 207	66.92 339	46.05 332	92.54 199	87.01 244

test0.0.03 1		64.66 312	64.36 311	65.57 320	75.03 339	46.89 335	64.69 334	61.58 348	62.43 270	71.18 314	77.54 329	43.41 330	68.47 334	40.75 341	82.65 314	81.35 318

PMMVS		61.65 318	60.38 323	65.47 321	65.40 359	69.26 151	63.97 335	61.73 347	36.80 356	60.11 347	68.43 347	59.42 259	66.35 342	48.97 320	78.57 331	60.81 351

test1235678		65.57 310	65.73 307	65.06 322	82.84 279	50.90 313	62.90 336	69.26 320	57.17 297	72.36 306	83.04 287	46.02 306	70.10 328	32.79 353	85.24 296	74.19 338

tpmrst		66.28 307	66.69 304	65.05 323	72.82 350	39.33 350	78.20 267	70.69 315	53.16 314	67.88 326	80.36 318	48.18 298	74.75 320	58.13 276	70.79 346	81.08 324

TESTMET0.1,1		61.29 320	60.32 324	64.19 324	72.06 351	51.30 308	67.89 324	62.09 344	45.27 346	60.65 346	69.01 346	27.93 361	64.74 347	56.31 284	81.65 320	76.53 332

PatchT		70.52 284	72.76 264	63.79 325	79.38 303	33.53 355	77.63 273	65.37 340	73.61 157	71.77 309	92.79 117	44.38 329	75.65 318	64.53 229	85.37 293	82.18 305

wuyk23d		75.13 242	79.30 204	62.63 326	75.56 331	75.18 105	80.89 229	73.10 295	75.06 145	94.76 14	95.32 41	87.73 30	52.85 357	34.16 351	97.11 68	59.85 352

EPMVS		62.47 313	62.63 317	62.01 327	70.63 355	38.74 351	74.76 297	52.86 357	53.91 310	67.71 328	80.01 320	39.40 348	66.60 341	55.54 290	68.81 353	80.68 328

EMVS		61.10 322	60.81 322	61.99 328	65.96 358	55.86 272	53.10 353	58.97 351	67.06 232	56.89 356	63.33 354	40.98 343	67.03 338	54.79 296	86.18 286	63.08 349

dp		60.70 324	60.29 325	61.92 329	72.04 352	38.67 352	70.83 314	64.08 341	51.28 328	60.75 345	77.28 331	36.59 351	71.58 326	47.41 326	62.34 355	75.52 335

E-PMN		61.59 319	61.62 319	61.49 330	66.81 356	55.40 275	53.77 352	60.34 349	66.80 236	58.90 352	65.50 353	40.48 345	66.12 343	55.72 287	86.25 285	62.95 350

Patchmatch-test		65.91 308	67.38 299	61.48 331	75.51 333	43.21 346	68.84 321	63.79 342	62.48 268	72.80 304	83.42 284	44.89 327	59.52 353	48.27 324	86.45 282	81.70 312

test2356		56.69 327	55.15 331	61.32 332	73.20 345	44.11 343	54.95 350	62.52 343	48.75 340	62.45 343	68.42 348	21.10 364	65.67 345	26.86 357	78.08 332	74.19 338

ADS-MVSNet		61.90 316	62.19 318	61.03 333	73.16 347	36.42 353	67.10 329	61.75 346	49.74 336	66.04 332	82.97 289	46.71 301	63.21 350	42.29 337	69.96 349	83.46 286

new-patchmatchnet		70.10 288	73.37 259	60.29 334	81.23 289	16.95 362	59.54 342	74.62 280	62.93 263	80.97 234	87.93 225	62.83 244	71.90 325	55.24 292	95.01 139	92.00 161

testus		62.33 315	63.03 315	60.20 335	78.78 310	40.74 348	59.14 343	69.80 319	49.26 338	71.41 311	74.72 341	52.33 289	63.52 348	29.84 355	82.01 317	76.36 333

PVSNet_0	51.08 22	56.10 328	54.97 332	59.48 336	75.12 338	53.28 291	55.16 349	61.89 345	44.30 348	59.16 350	62.48 355	54.22 285	65.91 344	35.40 350	47.01 356	59.25 353

DSMNet-mixed		60.98 323	61.61 320	59.09 337	72.88 349	45.05 341	74.70 298	46.61 361	26.20 357	65.34 335	90.32 187	55.46 280	63.12 351	41.72 339	81.30 322	69.09 346

MVS-HIRNet		61.16 321	62.92 316	55.87 338	79.09 306	35.34 354	71.83 311	57.98 354	46.56 343	59.05 351	91.14 159	49.95 294	76.43 314	38.74 345	71.92 345	55.84 355

MVE	40.22 23	51.82 333	50.47 335	55.87 338	62.66 361	51.91 300	31.61 358	39.28 362	40.65 352	50.76 358	74.98 340	56.24 276	44.67 360	33.94 352	64.11 354	71.04 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
1111		61.71 317	63.77 313	55.55 340	78.05 314	25.74 359	60.62 339	67.52 330	66.09 240	74.68 294	86.50 239	16.00 365	59.22 354	38.79 343	85.65 289	81.70 312

PNet_i23d		52.13 332	51.24 334	54.79 341	75.56 331	45.26 339	54.54 351	52.55 358	66.95 233	57.19 355	65.82 352	13.15 367	63.40 349	36.39 349	39.04 358	55.71 356

new_pmnet		55.69 329	57.66 328	49.76 342	75.47 334	30.59 356	59.56 341	51.45 359	43.62 350	62.49 342	75.48 337	40.96 344	49.15 359	37.39 347	72.52 342	69.55 345

testpf		58.55 326	61.58 321	49.48 343	66.03 357	40.05 349	74.40 300	58.07 353	64.72 255	59.36 349	72.67 342	22.76 363	66.92 339	67.07 209	69.15 352	41.46 357

test12356		54.91 331	57.14 329	48.22 344	75.83 330	17.47 361	52.31 354	69.20 321	51.66 325	60.11 347	75.40 338	29.77 359	62.62 352	27.64 356	72.37 343	64.59 348

PMMVS2		55.64 330	59.27 327	44.74 345	64.30 360	12.32 363	40.60 356	49.79 360	53.19 313	65.06 339	84.81 268	53.60 287	49.76 358	32.68 354	89.41 251	72.15 341

pcd1.5k->3k		38.83 335	41.11 336	32.01 346	93.13 62	0.00 367	0.00 359	91.38 115	0.00 362	0.00 363	0.00 364	89.24 15	0.00 365	0.00 362	96.24 97	96.02 49

.test1245		48.02 334	54.41 333	28.84 347	78.05 314	25.74 359	60.62 339	67.52 330	66.09 240	74.68 294	86.50 239	16.00 365	59.22 354	38.79 343	1.47 360	1.55 361

DeepMVS_CX				24.13 348	32.95 362	29.49 357		21.63 365	12.07 358	37.95 359	45.07 357	30.84 356	19.21 361	17.94 359	33.06 359	23.69 358

tmp_tt		20.25 337	24.50 338	7.49 349	4.47 363	8.70 364	34.17 357	25.16 364	1.00 359	32.43 360	18.49 358	39.37 349	9.21 362	21.64 358	43.75 357	4.57 359

test123		6.27 340	8.08 341	0.84 350	1.11 365	0.57 365	62.90 336	0.82 366	0.54 360	1.07 362	2.75 363	1.26 368	0.30 363	1.04 360	1.26 362	1.66 360

testmvs		5.91 341	7.65 342	0.72 351	1.20 364	0.37 366	59.14 343	0.67 367	0.49 361	1.11 361	2.76 362	0.94 369	0.24 364	1.02 361	1.47 360	1.55 361

cdsmvs_eth3d_5k		20.81 336	27.75 337	0.00 352	0.00 366	0.00 367	0.00 359	85.44 218	0.00 362	0.00 363	82.82 293	81.46 94	0.00 365	0.00 362	0.00 363	0.00 363

pcd_1.5k_mvsjas		6.41 339	8.55 340	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	76.94 135	0.00 365	0.00 362	0.00 363	0.00 363

sosnet-low-res		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

sosnet		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

uncertanet		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

Regformer		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

ab-mvs-re		6.65 338	8.87 339	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	79.80 322	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

uanet		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

GSMVS																83.88 279

test_part3							89.63 51		84.39 29		93.43 100		96.26 4	82.18 75

test_part2					93.86 47	77.77 80				92.84 43

test_part1								93.93 25				87.19 35			97.61 56	91.48 177

sam_mvs1												46.11 305				83.88 279

sam_mvs												45.92 310

MTGPA								91.81 92

test_post1							78.85 263				3.13 360	45.19 321	80.13 306	58.11 277

test_post											3.10 361	45.43 317	77.22 312

patchmatchnet-post											81.71 310	45.93 309	87.01 246

MTMP							90.66 35	33.14 363

gm-plane-assit					75.42 335	44.97 342			52.17 319		72.36 345		87.90 239	54.10 299

test9_res														80.83 90	96.45 89	90.57 199

TEST9					92.34 82	79.70 66	83.94 146	90.32 140	65.41 250	84.49 189	90.97 167	82.03 85	93.63 89

test_8					92.09 89	78.87 73	83.82 151	90.31 142	65.79 243	84.36 192	90.96 169	81.93 87	93.44 110

agg_prior2														79.68 108	96.16 99	90.22 207

agg_prior					91.58 104	77.69 82		90.30 144		84.32 193			93.18 122

test_prior4						78.97 72	84.59 133

test_prior2							83.37 168		75.43 140	84.58 187	91.57 145	81.92 89		79.54 110	96.97 71

旧先验2							81.73 213		56.88 298	86.54 160			84.90 280	72.81 168

新几何2							81.72 214

旧先验1					91.97 92	71.77 132		81.78 245			91.84 138	73.92 173			93.65 175	83.61 284

无先验							82.81 183	85.62 217	58.09 291				91.41 171	67.95 206		84.48 271

原ACMM2							82.26 201

test222					93.31 57	76.54 95	79.38 253	77.79 262	52.59 316	82.36 216	90.84 173	66.83 229			91.69 211	81.25 321

testdata2													86.43 264	63.52 233

segment_acmp												81.94 86

testdata1							79.62 242		73.95 155

plane_prior7					93.45 53	77.31 88

plane_prior6					92.61 73	76.54 95						74.84 158

plane_prior5								93.61 32					95.22 40	80.78 91	95.83 115	94.46 88

plane_prior4											92.95 111

plane_prior3						76.85 93			77.79 104	86.55 155

plane_prior2							89.45 59		79.44 82

plane_prior1					92.83 71

plane_prior						76.42 98	87.15 95		75.94 132						95.03 138

n2								0.00 368

nn								0.00 368

door-mid								74.45 283

test11								91.46 106

door								72.57 298

HQP5-MVS						70.66 142

HQP-NCC					91.19 114		84.77 128		73.30 165	80.55 249

ACMP_Plane					91.19 114		84.77 128		73.30 165	80.55 249

BP-MVS														77.30 132

HQP4-MVS										80.56 248			94.61 56			93.56 118

HQP3-MVS								92.68 68							94.47 157

HQP2-MVS												72.10 205

NP-MVS					91.95 93	74.55 108					90.17 191

MDTV_nov1_ep13_2view						27.60 358	70.76 315		46.47 344	61.27 344		45.20 320		49.18 319		83.75 283

MDTV_nov1_ep13			68.29 297		78.03 316	43.87 344	74.12 302	72.22 300	52.17 319	67.02 329	85.54 252	45.36 318	80.85 302	55.73 286	84.42 304

ACMMP++_ref															95.74 119

ACMMP++															97.35 62

Test By Simon												79.09 112

High-resolution multi-view benchmark