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.

EPNet		97.28 100	96.87 103	98.51 92	94.98 326	96.14 139	98.90 78	97.02 308	98.28 1	95.99 181	99.11 67	91.36 140	99.89 35	96.98 87	99.19 108	99.50 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	96.37 2	97.93 63	98.48 17	96.30 242	99.00 109	89.54 312	97.43 264	98.87 55	98.16 2	99.26 18	99.38 21	96.12 28	99.64 125	98.30 26	99.77 26	99.72 40

xxxxxxxxxxxxxcwj		98.70 9	98.50 14	99.30 30	99.46 51	98.38 35	98.21 195	98.52 158	97.95 3	99.32 15	99.39 14	96.22 20	99.84 52	97.72 52	99.73 43	99.67 61

save fliter					99.46 51	98.38 35	98.21 195	98.71 113	97.95 3

NCCC		98.61 17	98.35 24	99.38 17	99.28 82	98.61 24	98.45 162	98.76 98	97.82 5	98.45 71	98.93 97	96.65 14	99.83 55	97.38 76	99.41 97	99.71 44

CNVR-MVS		98.78 6	98.56 9	99.45 14	99.32 68	98.87 15	98.47 161	98.81 76	97.72 6	98.76 52	99.16 61	97.05 10	99.78 95	98.06 33	99.66 57	99.69 51

DeepC-MVS_fast	96.70 1	98.55 30	98.34 28	99.18 47	99.25 86	98.04 60	98.50 158	98.78 94	97.72 6	98.92 44	99.28 40	95.27 64	99.82 63	97.55 69	99.77 26	99.69 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		98.40 42	98.20 44	98.99 63	99.00 109	97.66 75	97.75 247	98.89 46	97.71 8	98.33 78	98.97 87	94.97 74	99.88 43	98.42 20	99.76 32	99.42 107
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
SED-MVS		99.09 1	98.91 1	99.63 3	99.71 20	99.24 4	99.02 58	98.87 55	97.65 9	99.73 1	99.48 6	97.53 4	99.94 3	98.43 18	99.81 10	99.70 48

test_241102_TWO								98.87 55	97.65 9	99.53 8	99.48 6	97.34 8	99.94 3	98.43 18	99.80 17	99.83 5

test_241102_ONE					99.71 20	99.24 4		98.87 55	97.62 11	99.73 1	99.39 14	97.53 4	99.74 106

DVP-MVS		99.03 2	98.83 3	99.63 3	99.72 12	99.25 2	98.97 68	98.58 146	97.62 11	99.45 9	99.46 9	97.42 6	99.94 3	98.47 15	99.81 10	99.69 51

test0726					99.72 12	99.25 2	99.06 51	98.88 49	97.62 11	99.56 5	99.50 4	97.42 6

DPE-MVS		98.92 4	98.67 6	99.65 2	99.58 32	99.20 7	98.42 169	98.91 43	97.58 14	99.54 7	99.46 9	97.10 9	99.94 3	97.64 60	99.84 8	99.83 5

MSP-MVS		98.74 8	98.55 10	99.29 31	99.75 3	98.23 49	99.26 18	98.88 49	97.52 15	99.41 11	98.78 112	96.00 34	99.79 91	97.79 48	99.59 71	99.85 2

HPM-MVS++		98.58 23	98.25 38	99.55 6	99.50 41	99.08 9	98.72 121	98.66 131	97.51 16	98.15 81	98.83 107	95.70 44	99.92 21	97.53 71	99.67 54	99.66 65

Regformer-1		98.66 12	98.51 13	99.12 57	99.35 60	97.81 73	98.37 173	98.76 98	97.49 17	99.20 22	99.21 48	96.08 29	99.79 91	98.42 20	99.73 43	99.75 28

Regformer-2		98.69 11	98.52 12	99.19 43	99.35 60	98.01 62	98.37 173	98.81 76	97.48 18	99.21 21	99.21 48	96.13 27	99.80 79	98.40 22	99.73 43	99.75 28

Regformer-4		98.64 14	98.53 11	98.99 63	99.43 57	97.37 86	98.40 171	98.79 91	97.46 19	99.09 30	99.31 35	95.86 42	99.80 79	98.64 3	99.76 32	99.79 10

Regformer-3		98.59 20	98.50 14	98.86 73	99.43 57	97.05 100	98.40 171	98.68 120	97.43 20	99.06 31	99.31 35	95.80 43	99.77 100	98.62 5	99.76 32	99.78 13

XVS		98.70 9	98.49 16	99.34 23	99.70 23	98.35 43	99.29 14	98.88 49	97.40 21	98.46 68	99.20 52	95.90 40	99.89 35	97.85 44	99.74 41	99.78 13

X-MVStestdata		94.06 261	92.30 281	99.34 23	99.70 23	98.35 43	99.29 14	98.88 49	97.40 21	98.46 68	43.50 357	95.90 40	99.89 35	97.85 44	99.74 41	99.78 13

UGNet		96.78 121	96.30 127	98.19 117	98.24 166	95.89 157	98.88 85	98.93 37	97.39 23	96.81 152	97.84 201	82.60 294	99.90 33	96.53 114	99.49 88	98.79 167
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
APDe-MVS		99.02 3	98.84 2	99.55 6	99.57 33	98.96 12	99.39 5	98.93 37	97.38 24	99.41 11	99.54 1	96.66 13	99.84 52	98.86 1	99.85 3	99.87 1

SteuartSystems-ACMMP		98.90 5	98.75 4	99.36 21	99.22 94	98.43 33	99.10 45	98.87 55	97.38 24	99.35 14	99.40 13	97.78 3	99.87 44	97.77 49	99.85 3	99.78 13
Skip Steuart: Steuart Systems R&D Blog.
CANet		98.05 56	97.76 61	98.90 71	98.73 128	97.27 90	98.35 175	98.78 94	97.37 26	97.72 113	98.96 93	91.53 138	99.92 21	98.79 2	99.65 58	99.51 89

test_0728_THIRD									97.32 27	99.45 9	99.46 9	97.88 1	99.94 3	98.47 15	99.86 1	99.85 2

PS-MVSNAJ		97.73 71	97.77 60	97.62 154	98.68 136	95.58 164	97.34 273	98.51 161	97.29 28	98.66 60	97.88 196	94.51 85	99.90 33	97.87 42	99.17 109	97.39 217

SD-MVS		98.64 14	98.68 5	98.53 91	99.33 65	98.36 42	98.90 78	98.85 64	97.28 29	99.72 3	99.39 14	96.63 15	97.60 318	98.17 28	99.85 3	99.64 70
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
MSLP-MVS++		98.56 28	98.57 8	98.55 87	99.26 85	96.80 109	98.71 122	99.05 24	97.28 29	98.84 46	99.28 40	96.47 18	99.40 154	98.52 13	99.70 51	99.47 98

HQP_MVS		96.14 142	95.90 139	96.85 195	97.42 229	94.60 211	98.80 104	98.56 149	97.28 29	95.34 186	98.28 164	87.09 230	99.03 193	96.07 127	94.27 219	96.92 234

plane_prior2							98.80 104		97.28 29

zzz-MVS		98.55 30	98.25 38	99.46 12	99.76 1	98.64 22	98.55 151	98.74 102	97.27 33	98.02 90	99.39 14	94.81 77	99.96 1	97.91 38	99.79 19	99.77 20

MTAPA		98.58 23	98.29 35	99.46 12	99.76 1	98.64 22	98.90 78	98.74 102	97.27 33	98.02 90	99.39 14	94.81 77	99.96 1	97.91 38	99.79 19	99.77 20

CANet_DTU		96.96 114	96.55 119	98.21 114	98.17 176	96.07 141	97.98 225	98.21 212	97.24 35	97.13 134	98.93 97	86.88 235	99.91 30	95.00 165	99.37 101	98.66 178

EI-MVSNet-Vis-set		98.47 38	98.39 19	98.69 78	99.46 51	96.49 124	98.30 186	98.69 117	97.21 36	98.84 46	99.36 26	95.41 54	99.78 95	98.62 5	99.65 58	99.80 9

MVS_111021_HR		98.47 38	98.34 28	98.88 72	99.22 94	97.32 87	97.91 231	99.58 3	97.20 37	98.33 78	99.00 85	95.99 35	99.64 125	98.05 35	99.76 32	99.69 51

TSAR-MVS + GP.		98.38 43	98.24 41	98.81 74	99.22 94	97.25 94	98.11 214	98.29 204	97.19 38	98.99 38	99.02 80	96.22 20	99.67 121	98.52 13	98.56 135	99.51 89

EI-MVSNet-UG-set		98.41 41	98.34 28	98.61 83	99.45 55	96.32 132	98.28 189	98.68 120	97.17 39	98.74 53	99.37 22	95.25 66	99.79 91	98.57 7	99.54 84	99.73 36

xiu_mvs_v2_base		97.66 75	97.70 63	97.56 158	98.61 142	95.46 170	97.44 262	98.46 171	97.15 40	98.65 61	98.15 175	94.33 91	99.80 79	97.84 46	98.66 131	97.41 215

MVS_111021_LR		98.34 48	98.23 42	98.67 80	99.27 83	96.90 106	97.95 227	99.58 3	97.14 41	98.44 72	99.01 84	95.03 73	99.62 130	97.91 38	99.75 38	99.50 91

xiu_mvs_v1_base_debu		97.60 78	97.56 69	97.72 144	98.35 156	95.98 142	97.86 238	98.51 161	97.13 42	99.01 35	98.40 149	91.56 134	99.80 79	98.53 9	98.68 127	97.37 219

xiu_mvs_v1_base		97.60 78	97.56 69	97.72 144	98.35 156	95.98 142	97.86 238	98.51 161	97.13 42	99.01 35	98.40 149	91.56 134	99.80 79	98.53 9	98.68 127	97.37 219

xiu_mvs_v1_base_debi		97.60 78	97.56 69	97.72 144	98.35 156	95.98 142	97.86 238	98.51 161	97.13 42	99.01 35	98.40 149	91.56 134	99.80 79	98.53 9	98.68 127	97.37 219

3Dnovator+	94.38 6	97.43 92	96.78 107	99.38 17	97.83 196	98.52 27	99.37 7	98.71 113	97.09 45	92.99 272	99.13 64	89.36 174	99.89 35	96.97 88	99.57 75	99.71 44

MCST-MVS		98.65 13	98.37 21	99.48 10	99.60 31	98.87 15	98.41 170	98.68 120	97.04 46	98.52 67	98.80 110	96.78 12	99.83 55	97.93 37	99.61 67	99.74 33

plane_prior3						94.61 209			97.02 47	95.34 186

3Dnovator	94.51 5	97.46 87	96.93 100	99.07 60	97.78 198	97.64 76	99.35 10	99.06 22	97.02 47	93.75 246	99.16 61	89.25 177	99.92 21	97.22 80	99.75 38	99.64 70

DeepC-MVS	95.98 3	97.88 64	97.58 67	98.77 75	99.25 86	96.93 104	98.83 94	98.75 101	96.96 49	96.89 148	99.50 4	90.46 159	99.87 44	97.84 46	99.76 32	99.52 85
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		97.81 67	97.60 66	98.44 98	99.12 103	95.97 147	97.75 247	98.78 94	96.89 50	98.46 68	99.22 47	93.90 99	99.68 120	94.81 170	99.52 87	99.67 61

ETV-MVS		97.96 58	97.81 59	98.40 103	98.42 152	97.27 90	98.73 117	98.55 151	96.84 51	98.38 75	97.44 236	95.39 55	99.35 158	97.62 61	98.89 118	98.58 184

TSAR-MVS + MP.		98.78 6	98.62 7	99.24 40	99.69 25	98.28 48	99.14 36	98.66 131	96.84 51	99.56 5	99.31 35	96.34 19	99.70 114	98.32 25	99.73 43	99.73 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EPNet_dtu		95.21 188	94.95 180	95.99 252	96.17 297	90.45 303	98.16 208	97.27 297	96.77 53	93.14 268	98.33 160	90.34 161	98.42 257	85.57 323	98.81 125	99.09 144
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
canonicalmvs		97.67 74	97.23 87	98.98 65	98.70 133	98.38 35	99.34 11	98.39 184	96.76 54	97.67 116	97.40 239	92.26 116	99.49 145	98.28 27	96.28 202	99.08 147

alignmvs		97.56 84	97.07 94	99.01 62	98.66 137	98.37 41	98.83 94	98.06 247	96.74 55	98.00 96	97.65 218	90.80 153	99.48 149	98.37 23	96.56 190	99.19 132

VNet		97.79 69	97.40 81	98.96 67	98.88 118	97.55 80	98.63 137	98.93 37	96.74 55	99.02 34	98.84 106	90.33 162	99.83 55	98.53 9	96.66 186	99.50 91

plane_prior						94.60 211	98.44 165		96.74 55						94.22 221

UA-Net		97.96 58	97.62 64	98.98 65	98.86 120	97.47 83	98.89 82	99.08 21	96.67 58	98.72 56	99.54 1	93.15 105	99.81 70	94.87 166	98.83 123	99.65 67

OPM-MVS		95.69 162	95.33 162	96.76 199	96.16 299	94.63 206	98.43 167	98.39 184	96.64 59	95.02 192	98.78 112	85.15 263	99.05 189	95.21 162	94.20 222	96.60 277

Vis-MVSNet		97.42 93	97.11 91	98.34 106	98.66 137	96.23 135	99.22 25	99.00 27	96.63 60	98.04 88	99.21 48	88.05 211	99.35 158	96.01 133	99.21 106	99.45 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
SR-MVS		98.57 26	98.35 24	99.24 40	99.53 36	98.18 53	99.09 46	98.82 70	96.58 61	99.10 29	99.32 33	95.39 55	99.82 63	97.70 57	99.63 64	99.72 40

Effi-MVS+-dtu		96.29 137	96.56 118	95.51 270	97.89 193	90.22 305	98.80 104	98.10 234	96.57 62	96.45 171	96.66 293	90.81 151	98.91 208	95.72 143	97.99 155	97.40 216

mvs-test1		96.60 125	96.68 115	96.37 236	97.89 193	91.81 276	98.56 149	98.10 234	96.57 62	96.52 167	97.94 191	90.81 151	99.45 152	95.72 143	98.01 154	97.86 205

test1172		98.56 28	98.35 24	99.16 50	99.53 36	97.94 66	99.09 46	98.83 68	96.52 64	99.05 32	99.34 31	95.34 59	99.82 63	97.86 43	99.64 62	99.73 36

SR-MVS-dyc-post		98.54 32	98.35 24	99.13 54	99.49 45	97.86 68	99.11 42	98.80 86	96.49 65	99.17 24	99.35 28	95.34 59	99.82 63	97.72 52	99.65 58	99.71 44

RE-MVS-def			98.34 28		99.49 45	97.86 68	99.11 42	98.80 86	96.49 65	99.17 24	99.35 28	95.29 63		97.72 52	99.65 58	99.71 44

HQP-NCC					97.20 243		98.05 218		96.43 67	94.45 209

ACMP_Plane					97.20 243		98.05 218		96.43 67	94.45 209

HQP-MVS		95.72 159	95.40 154	96.69 205	97.20 243	94.25 224	98.05 218	98.46 171	96.43 67	94.45 209	97.73 210	86.75 236	98.96 201	95.30 156	94.18 223	96.86 247

casdiffmvs		97.63 77	97.41 80	98.28 108	98.33 161	96.14 139	98.82 97	98.32 194	96.38 70	97.95 98	99.21 48	91.23 145	99.23 167	98.12 30	98.37 144	99.48 96

testdata1							97.32 275		96.34 71

baseline		97.64 76	97.44 79	98.25 112	98.35 156	96.20 136	99.00 62	98.32 194	96.33 72	98.03 89	99.17 56	91.35 141	99.16 173	98.10 31	98.29 149	99.39 108

APD-MVS_3200maxsize		98.53 34	98.33 32	99.15 53	99.50 41	97.92 67	99.15 35	98.81 76	96.24 73	99.20 22	99.37 22	95.30 62	99.80 79	97.73 51	99.67 54	99.72 40

mPP-MVS		98.51 35	98.26 37	99.25 39	99.75 3	98.04 60	99.28 16	98.81 76	96.24 73	98.35 77	99.23 45	95.46 51	99.94 3	97.42 74	99.81 10	99.77 20

diffmvs		97.58 82	97.40 81	98.13 120	98.32 163	95.81 159	98.06 217	98.37 187	96.20 75	98.74 53	98.89 101	91.31 143	99.25 164	98.16 29	98.52 136	99.34 111

CS-MVS		97.81 67	97.61 65	98.41 102	98.52 148	97.15 98	99.09 46	98.55 151	96.18 76	97.61 122	97.20 251	94.59 83	99.39 155	97.62 61	99.10 111	98.70 172

region2R		98.61 17	98.38 20	99.29 31	99.74 7	98.16 55	99.23 21	98.93 37	96.15 77	98.94 39	99.17 56	95.91 39	99.94 3	97.55 69	99.79 19	99.78 13

abl_6		98.30 53	98.03 51	99.13 54	99.56 34	97.76 74	99.13 39	98.82 70	96.14 78	99.26 18	99.37 22	93.33 102	99.93 15	96.96 90	99.67 54	99.69 51

MP-MVS		98.33 50	98.01 52	99.28 35	99.75 3	98.18 53	99.22 25	98.79 91	96.13 79	97.92 103	99.23 45	94.54 84	99.94 3	96.74 109	99.78 23	99.73 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test_prior3		98.22 55	97.90 58	99.19 43	99.31 70	98.22 50	97.80 243	98.84 65	96.12 80	97.89 105	98.69 119	95.96 36	99.70 114	96.89 95	99.60 68	99.65 67

test_prior2							97.80 243		96.12 80	97.89 105	98.69 119	95.96 36		96.89 95	99.60 68

HFP-MVS		98.63 16	98.40 18	99.32 28	99.72 12	98.29 46	99.23 21	98.96 32	96.10 82	98.94 39	99.17 56	96.06 30	99.92 21	97.62 61	99.78 23	99.75 28

ACMMPR		98.59 20	98.36 22	99.29 31	99.74 7	98.15 56	99.23 21	98.95 34	96.10 82	98.93 43	99.19 55	95.70 44	99.94 3	97.62 61	99.79 19	99.78 13

ACMMP		98.23 54	97.95 55	99.09 59	99.74 7	97.62 78	99.03 56	99.41 6	95.98 84	97.60 124	99.36 26	94.45 89	99.93 15	97.14 82	98.85 122	99.70 48
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		98.57 26	98.36 22	99.19 43	99.66 27	97.86 68	99.34 11	98.87 55	95.96 85	98.60 64	99.13 64	96.05 32	99.94 3	97.77 49	99.86 1	99.77 20

FIs		96.51 130	96.12 133	97.67 150	97.13 250	97.54 81	99.36 8	99.22 14	95.89 86	94.03 234	98.35 155	91.98 126	98.44 255	96.40 120	92.76 253	97.01 228

EIA-MVS		97.75 70	97.58 67	98.27 109	98.38 154	96.44 126	99.01 60	98.60 139	95.88 87	97.26 130	97.53 229	94.97 74	99.33 160	97.38 76	99.20 107	99.05 149

PS-MVSNAJss		96.43 132	96.26 129	96.92 192	95.84 310	95.08 186	99.16 34	98.50 166	95.87 88	93.84 242	98.34 159	94.51 85	98.61 236	96.88 98	93.45 243	97.06 226

FC-MVSNet-test		96.42 133	96.05 134	97.53 160	96.95 259	97.27 90	99.36 8	99.23 12	95.83 89	93.93 236	98.37 153	92.00 125	98.32 273	96.02 132	92.72 254	97.00 229

ACMMP_NAP		98.61 17	98.30 34	99.55 6	99.62 30	98.95 13	98.82 97	98.81 76	95.80 90	99.16 26	99.47 8	95.37 57	99.92 21	97.89 41	99.75 38	99.79 10

ZNCC-MVS		98.49 36	98.20 44	99.35 22	99.73 11	98.39 34	99.19 31	98.86 61	95.77 91	98.31 80	99.10 69	95.46 51	99.93 15	97.57 68	99.81 10	99.74 33

jajsoiax		95.45 171	95.03 175	96.73 201	95.42 323	94.63 206	99.14 36	98.52 158	95.74 92	93.22 263	98.36 154	83.87 288	98.65 234	96.95 91	94.04 228	96.91 239

mvs_tets		95.41 175	95.00 176	96.65 207	95.58 316	94.42 216	99.00 62	98.55 151	95.73 93	93.21 264	98.38 152	83.45 292	98.63 235	97.09 84	94.00 230	96.91 239

GST-MVS		98.43 40	98.12 47	99.34 23	99.72 12	98.38 35	99.09 46	98.82 70	95.71 94	98.73 55	99.06 78	95.27 64	99.93 15	97.07 85	99.63 64	99.72 40

CVMVSNet		95.43 172	96.04 135	93.57 313	97.93 190	83.62 344	98.12 212	98.59 141	95.68 95	96.56 161	99.02 80	87.51 222	97.51 322	93.56 212	97.44 172	99.60 78

VPNet		94.99 200	94.19 215	97.40 166	97.16 248	96.57 120	98.71 122	98.97 30	95.67 96	94.84 196	98.24 170	80.36 308	98.67 233	96.46 116	87.32 317	96.96 231

XVG-OURS		96.55 129	96.41 123	96.99 184	98.75 127	93.76 235	97.50 261	98.52 158	95.67 96	96.83 149	99.30 38	88.95 190	99.53 142	95.88 136	96.26 203	97.69 211

#test#		98.54 32	98.27 36	99.32 28	99.72 12	98.29 46	98.98 67	98.96 32	95.65 98	98.94 39	99.17 56	96.06 30	99.92 21	97.21 81	99.78 23	99.75 28

testgi		93.06 280	92.45 279	94.88 290	96.43 288	89.90 306	98.75 110	97.54 279	95.60 99	91.63 302	97.91 193	74.46 341	97.02 328	86.10 319	93.67 236	97.72 210

UniMVSNet (Re)		95.78 157	95.19 168	97.58 156	96.99 258	97.47 83	98.79 108	99.18 16	95.60 99	93.92 237	97.04 268	91.68 131	98.48 249	95.80 140	87.66 313	96.79 253

Fast-Effi-MVS+-dtu		95.87 152	95.85 140	95.91 257	97.74 202	91.74 280	98.69 128	98.15 226	95.56 101	94.92 194	97.68 217	88.98 188	98.79 224	93.19 221	97.78 163	97.20 223

bset_n11_16_dypcd		94.89 208	94.27 211	96.76 199	94.41 333	95.15 182	95.67 331	95.64 334	95.53 102	94.65 202	97.52 230	87.10 229	98.29 280	96.58 113	91.35 266	96.83 251

CLD-MVS		95.62 165	95.34 160	96.46 231	97.52 221	93.75 237	97.27 279	98.46 171	95.53 102	94.42 214	98.00 186	86.21 246	98.97 198	96.25 124	94.37 217	96.66 272
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OMC-MVS		97.55 85	97.34 83	98.20 115	99.33 65	95.92 154	98.28 189	98.59 141	95.52 104	97.97 97	99.10 69	93.28 104	99.49 145	95.09 163	98.88 119	99.19 132

testtj		98.33 50	97.95 55	99.47 11	99.49 45	98.70 19	98.83 94	98.86 61	95.48 105	98.91 45	99.17 56	95.48 50	99.93 15	95.80 140	99.53 85	99.76 26

nrg030		96.28 139	95.72 143	97.96 131	96.90 264	98.15 56	99.39 5	98.31 196	95.47 106	94.42 214	98.35 155	92.09 123	98.69 229	97.50 72	89.05 298	97.04 227

XVG-OURS-SEG-HR		96.51 130	96.34 125	97.02 183	98.77 126	93.76 235	97.79 245	98.50 166	95.45 107	96.94 143	99.09 74	87.87 216	99.55 141	96.76 108	95.83 212	97.74 208

PGM-MVS		98.49 36	98.23 42	99.27 38	99.72 12	98.08 59	98.99 64	99.49 5	95.43 108	99.03 33	99.32 33	95.56 47	99.94 3	96.80 105	99.77 26	99.78 13

DU-MVS		95.42 173	94.76 186	97.40 166	96.53 282	96.97 102	98.66 135	98.99 29	95.43 108	93.88 239	97.69 214	88.57 196	98.31 275	95.81 138	87.25 319	96.92 234

IS-MVSNet		97.22 102	96.88 102	98.25 112	98.85 122	96.36 130	99.19 31	97.97 252	95.39 110	97.23 131	98.99 86	91.11 147	98.93 206	94.60 175	98.59 133	99.47 98

thres100view900		95.38 176	94.70 189	97.41 164	98.98 112	94.92 195	98.87 87	96.90 314	95.38 111	96.61 159	96.88 283	84.29 276	99.56 136	88.11 306	96.29 199	97.76 206

thres600view7		95.49 168	94.77 185	97.67 150	98.98 112	95.02 187	98.85 90	96.90 314	95.38 111	96.63 158	96.90 282	84.29 276	99.59 132	88.65 305	96.33 197	98.40 189

baseline1		95.84 154	95.12 171	98.01 127	98.49 150	95.98 142	98.73 117	97.03 306	95.37 113	96.22 175	98.19 173	89.96 167	99.16 173	94.60 175	87.48 314	98.90 162

tfpn200view9		95.32 183	94.62 192	97.43 163	98.94 114	94.98 191	98.68 129	96.93 312	95.33 114	96.55 163	96.53 299	84.23 279	99.56 136	88.11 306	96.29 199	97.76 206

thres400		95.38 176	94.62 192	97.65 153	98.94 114	94.98 191	98.68 129	96.93 312	95.33 114	96.55 163	96.53 299	84.23 279	99.56 136	88.11 306	96.29 199	98.40 189

CNLPA		97.45 90	97.03 95	98.73 76	99.05 105	97.44 85	98.07 216	98.53 156	95.32 116	96.80 153	98.53 136	93.32 103	99.72 108	94.31 188	99.31 104	99.02 151

OurMVSNet-221017-0		94.21 248	94.00 228	94.85 291	95.60 315	89.22 317	98.89 82	97.43 289	95.29 117	92.18 294	98.52 139	82.86 293	98.59 241	93.46 213	91.76 262	96.74 259

IU-MVS					99.71 20	99.23 6		98.64 136	95.28 118	99.63 4				98.35 24	99.81 10	99.83 5

WTY-MVS		97.37 97	96.92 101	98.72 77	98.86 120	96.89 108	98.31 184	98.71 113	95.26 119	97.67 116	98.56 135	92.21 119	99.78 95	95.89 135	96.85 181	99.48 96

CHOSEN 280x420		97.18 106	97.18 89	97.20 172	98.81 124	93.27 256	95.78 330	99.15 18	95.25 120	96.79 154	98.11 178	92.29 115	99.07 188	98.56 8	99.85 3	99.25 126

ACMM	93.85 9	95.69 162	95.38 158	96.61 212	97.61 209	93.84 233	98.91 77	98.44 175	95.25 120	94.28 220	98.47 142	86.04 251	99.12 179	95.50 152	93.95 232	96.87 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres200		95.25 185	94.57 194	97.28 169	98.81 124	94.92 195	98.20 198	97.11 301	95.24 122	96.54 165	96.22 311	84.58 273	99.53 142	87.93 310	96.50 193	97.39 217

PAPM_NR		97.46 87	97.11 91	98.50 93	99.50 41	96.41 128	98.63 137	98.60 139	95.18 123	97.06 139	98.06 181	94.26 93	99.57 134	93.80 204	98.87 121	99.52 85

UniMVSNet_NR-MVSNet		95.71 160	95.15 169	97.40 166	96.84 267	96.97 102	98.74 113	99.24 10	95.16 124	93.88 239	97.72 212	91.68 131	98.31 275	95.81 138	87.25 319	96.92 234

RRT_MVS		96.04 145	95.53 151	97.56 158	97.07 254	97.32 87	98.57 148	98.09 238	95.15 125	95.02 192	98.44 144	88.20 205	98.58 243	96.17 126	93.09 250	96.79 253

VPA-MVSNet		95.75 158	95.11 172	97.69 148	97.24 239	97.27 90	98.94 74	99.23 12	95.13 126	95.51 185	97.32 242	85.73 253	98.91 208	97.33 78	89.55 291	96.89 242

SF-MVS		98.59 20	98.32 33	99.41 16	99.54 35	98.71 18	99.04 53	98.81 76	95.12 127	99.32 15	99.39 14	96.22 20	99.84 52	97.72 52	99.73 43	99.67 61

test-LLR		95.10 194	94.87 183	95.80 262	96.77 269	89.70 309	96.91 300	95.21 336	95.11 128	94.83 198	95.72 321	87.71 218	98.97 198	93.06 224	98.50 138	98.72 170

test0.0.03 1		94.08 259	93.51 258	95.80 262	95.53 318	92.89 265	97.38 267	95.97 329	95.11 128	92.51 287	96.66 293	87.71 218	96.94 329	87.03 314	93.67 236	97.57 213

LCM-MVSNet-Re		95.22 187	95.32 163	94.91 288	98.18 174	87.85 335	98.75 110	95.66 333	95.11 128	88.96 323	96.85 286	90.26 164	97.65 316	95.65 148	98.44 141	99.22 128

ITE_SJBPF				95.44 274	97.42 229	91.32 289		97.50 282	95.09 131	93.59 248	98.35 155	81.70 298	98.88 214	89.71 291	93.39 245	96.12 312

TranMVSNet+NR-MVSNet		95.14 192	94.48 199	97.11 179	96.45 287	96.36 130	99.03 56	99.03 25	95.04 132	93.58 249	97.93 192	88.27 203	98.03 299	94.13 193	86.90 324	96.95 233

VDD-MVS		95.82 156	95.23 166	97.61 155	98.84 123	93.98 229	98.68 129	97.40 291	95.02 133	97.95 98	99.34 31	74.37 342	99.78 95	98.64 3	96.80 182	99.08 147

MVSFormer		97.57 83	97.49 75	97.84 135	98.07 181	95.76 160	99.47 2	98.40 182	94.98 134	98.79 49	98.83 107	92.34 113	98.41 264	96.91 92	99.59 71	99.34 111

test_djsdf		96.00 147	95.69 148	96.93 190	95.72 312	95.49 169	99.47 2	98.40 182	94.98 134	94.58 204	97.86 198	89.16 180	98.41 264	96.91 92	94.12 227	96.88 243

NR-MVSNet		94.98 202	94.16 218	97.44 162	96.53 282	97.22 95	98.74 113	98.95 34	94.96 136	89.25 322	97.69 214	89.32 175	98.18 286	94.59 177	87.40 316	96.92 234

XVG-ACMP-BASELINE		94.54 229	94.14 220	95.75 265	96.55 281	91.65 282	98.11 214	98.44 175	94.96 136	94.22 224	97.90 194	79.18 315	99.11 182	94.05 198	93.85 234	96.48 298

Vis-MVSNet (Re-imp)		96.87 118	96.55 119	97.83 136	98.73 128	95.46 170	99.20 29	98.30 202	94.96 136	96.60 160	98.87 103	90.05 165	98.59 241	93.67 208	98.60 132	99.46 102

ACMP	93.49 10	95.34 181	94.98 178	96.43 233	97.67 205	93.48 248	98.73 117	98.44 175	94.94 139	92.53 285	98.53 136	84.50 275	99.14 177	95.48 153	94.00 230	96.66 272
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSTER		96.06 144	95.72 143	97.08 181	98.23 167	95.93 153	98.73 117	98.27 205	94.86 140	95.07 190	98.09 179	88.21 204	98.54 245	96.59 111	93.46 241	96.79 253

DPM-MVS		97.55 85	96.99 98	99.23 42	99.04 107	98.55 26	97.17 286	98.35 190	94.85 141	97.93 102	98.58 132	95.07 72	99.71 113	92.60 236	99.34 102	99.43 106

jason		97.32 99	97.08 93	98.06 125	97.45 228	95.59 163	97.87 237	97.91 258	94.79 142	98.55 66	98.83 107	91.12 146	99.23 167	97.58 65	99.60 68	99.34 111
jason: jason.
RRT_test8_iter05		94.56 227	94.19 215	95.67 267	97.60 210	91.34 286	98.93 75	98.42 179	94.75 143	93.39 258	97.87 197	79.00 316	98.61 236	96.78 107	90.99 274	97.07 225

test_yl		97.22 102	96.78 107	98.54 89	98.73 128	96.60 118	98.45 162	98.31 196	94.70 144	98.02 90	98.42 147	90.80 153	99.70 114	96.81 103	96.79 183	99.34 111

DCV-MVSNet		97.22 102	96.78 107	98.54 89	98.73 128	96.60 118	98.45 162	98.31 196	94.70 144	98.02 90	98.42 147	90.80 153	99.70 114	96.81 103	96.79 183	99.34 111

EU-MVSNet		93.66 266	94.14 220	92.25 324	95.96 306	83.38 345	98.52 153	98.12 230	94.69 146	92.61 282	98.13 177	87.36 227	96.39 340	91.82 258	90.00 284	96.98 230

SCA		95.46 169	95.13 170	96.46 231	97.67 205	91.29 290	97.33 274	97.60 272	94.68 147	96.92 146	97.10 255	83.97 285	98.89 212	92.59 238	98.32 148	99.20 129

LPG-MVS_test		95.62 165	95.34 160	96.47 228	97.46 224	93.54 244	98.99 64	98.54 154	94.67 148	94.36 216	98.77 114	85.39 258	99.11 182	95.71 145	94.15 225	96.76 257

LGP-MVS_train				96.47 228	97.46 224	93.54 244		98.54 154	94.67 148	94.36 216	98.77 114	85.39 258	99.11 182	95.71 145	94.15 225	96.76 257

ETH3D-3000-0.1		98.35 46	98.00 53	99.38 17	99.47 48	98.68 21	98.67 132	98.84 65	94.66 150	99.11 28	99.25 43	95.46 51	99.81 70	96.80 105	99.73 43	99.63 73

HPM-MVS		98.36 45	98.10 48	99.13 54	99.74 7	97.82 72	99.53 1	98.80 86	94.63 151	98.61 63	98.97 87	95.13 70	99.77 100	97.65 59	99.83 9	99.79 10
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
BH-RMVSNet		95.92 151	95.32 163	97.69 148	98.32 163	94.64 205	98.19 202	97.45 287	94.56 152	96.03 179	98.61 127	85.02 264	99.12 179	90.68 276	99.06 112	99.30 120

ET-MVSNet_ETH3D		94.13 254	92.98 269	97.58 156	98.22 168	96.20 136	97.31 276	95.37 335	94.53 153	79.56 346	97.63 222	86.51 239	97.53 321	96.91 92	90.74 276	99.02 151

API-MVS		97.41 94	97.25 86	97.91 132	98.70 133	96.80 109	98.82 97	98.69 117	94.53 153	98.11 83	98.28 164	94.50 88	99.57 134	94.12 194	99.49 88	97.37 219

APD-MVS		98.35 46	98.00 53	99.42 15	99.51 39	98.72 17	98.80 104	98.82 70	94.52 155	99.23 20	99.25 43	95.54 49	99.80 79	96.52 115	99.77 26	99.74 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
lupinMVS		97.44 91	97.22 88	98.12 122	98.07 181	95.76 160	97.68 251	97.76 263	94.50 156	98.79 49	98.61 127	92.34 113	99.30 161	97.58 65	99.59 71	99.31 117

PVSNet_Blended_VisFu		97.70 73	97.46 77	98.44 98	99.27 83	95.91 155	98.63 137	99.16 17	94.48 157	97.67 116	98.88 102	92.80 108	99.91 30	97.11 83	99.12 110	99.50 91

HPM-MVS_fast		98.38 43	98.13 46	99.12 57	99.75 3	97.86 68	99.44 4	98.82 70	94.46 158	98.94 39	99.20 52	95.16 69	99.74 106	97.58 65	99.85 3	99.77 20

AdaColmap		97.15 108	96.70 112	98.48 95	99.16 99	96.69 114	98.01 222	98.89 46	94.44 159	96.83 149	98.68 121	90.69 156	99.76 102	94.36 184	99.29 105	98.98 155

9.14			98.06 49		99.47 48		98.71 122	98.82 70	94.36 160	99.16 26	99.29 39	96.05 32	99.81 70	97.00 86	99.71 50

PVSNet_BlendedMVS		96.73 122	96.60 117	97.12 178	99.25 86	95.35 175	98.26 192	99.26 8	94.28 161	97.94 100	97.46 233	92.74 109	99.81 70	96.88 98	93.32 246	96.20 310

MVS_Test		97.28 100	97.00 97	98.13 120	98.33 161	95.97 147	98.74 113	98.07 242	94.27 162	98.44 72	98.07 180	92.48 111	99.26 163	96.43 119	98.19 150	99.16 137

tttt0517		96.07 143	95.51 153	97.78 139	98.41 153	94.84 197	99.28 16	94.33 346	94.26 163	97.64 120	98.64 126	84.05 283	99.47 150	95.34 154	97.60 170	99.03 150

WR-MVS		95.15 191	94.46 201	97.22 171	96.67 277	96.45 125	98.21 195	98.81 76	94.15 164	93.16 265	97.69 214	87.51 222	98.30 277	95.29 158	88.62 304	96.90 241

EPMVS		94.99 200	94.48 199	96.52 224	97.22 241	91.75 279	97.23 280	91.66 354	94.11 165	97.28 129	96.81 288	85.70 254	98.84 218	93.04 226	97.28 175	98.97 156

MP-MVS-pluss		98.31 52	97.92 57	99.49 9	99.72 12	98.88 14	98.43 167	98.78 94	94.10 166	97.69 115	99.42 12	95.25 66	99.92 21	98.09 32	99.80 17	99.67 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PatchmatchNet		95.71 160	95.52 152	96.29 243	97.58 212	90.72 299	96.84 309	97.52 280	94.06 167	97.08 136	96.96 277	89.24 178	98.90 211	92.03 254	98.37 144	99.26 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thisisatest0530		96.01 146	95.36 159	97.97 129	98.38 154	95.52 168	98.88 85	94.19 348	94.04 168	97.64 120	98.31 162	83.82 290	99.46 151	95.29 158	97.70 167	98.93 160

K. test v3		92.55 286	91.91 287	94.48 303	95.64 314	89.24 316	99.07 50	94.88 340	94.04 168	86.78 333	97.59 224	77.64 328	97.64 317	92.08 250	89.43 293	96.57 281

D2MVS		95.18 190	95.08 173	95.48 271	97.10 252	92.07 272	98.30 186	99.13 19	94.02 170	92.90 273	96.73 290	89.48 171	98.73 228	94.48 181	93.60 240	95.65 322

mvs_anonymous		96.70 123	96.53 121	97.18 174	98.19 172	93.78 234	98.31 184	98.19 215	94.01 171	94.47 208	98.27 167	92.08 124	98.46 252	97.39 75	97.91 157	99.31 117

GA-MVS		94.81 211	94.03 224	97.14 176	97.15 249	93.86 232	96.76 312	97.58 273	94.00 172	94.76 201	97.04 268	80.91 303	98.48 249	91.79 259	96.25 204	99.09 144

ACMH+	92.99 14	94.30 243	93.77 244	95.88 260	97.81 197	92.04 274	98.71 122	98.37 187	93.99 173	90.60 312	98.47 142	80.86 305	99.05 189	92.75 234	92.40 256	96.55 285

sss		97.39 95	96.98 99	98.61 83	98.60 143	96.61 117	98.22 194	98.93 37	93.97 174	98.01 94	98.48 141	91.98 126	99.85 49	96.45 117	98.15 151	99.39 108

HY-MVS	93.96 8	96.82 120	96.23 131	98.57 85	98.46 151	97.00 101	98.14 209	98.21 212	93.95 175	96.72 155	97.99 187	91.58 133	99.76 102	94.51 180	96.54 191	98.95 159

TAMVS		97.02 112	96.79 106	97.70 147	98.06 183	95.31 177	98.52 153	98.31 196	93.95 175	97.05 140	98.61 127	93.49 101	98.52 247	95.33 155	97.81 161	99.29 122

CP-MVSNet		94.94 206	94.30 210	96.83 196	96.72 274	95.56 165	99.11 42	98.95 34	93.89 177	92.42 290	97.90 194	87.19 228	98.12 291	94.32 187	88.21 307	96.82 252

SixPastTwentyTwo		93.34 272	92.86 271	94.75 295	95.67 313	89.41 315	98.75 110	96.67 324	93.89 177	90.15 316	98.25 169	80.87 304	98.27 283	90.90 272	90.64 277	96.57 281

WR-MVS_H		95.05 197	94.46 201	96.81 197	96.86 266	95.82 158	99.24 20	99.24 10	93.87 179	92.53 285	96.84 287	90.37 160	98.24 284	93.24 219	87.93 310	96.38 303

ab-mvs		96.42 133	95.71 146	98.55 87	98.63 140	96.75 112	97.88 236	98.74 102	93.84 180	96.54 165	98.18 174	85.34 261	99.75 104	95.93 134	96.35 196	99.15 138

USDC		93.33 273	92.71 274	95.21 279	96.83 268	90.83 296	96.91 300	97.50 282	93.84 180	90.72 310	98.14 176	77.69 325	98.82 221	89.51 296	93.21 249	95.97 316

AUN-MVS		94.53 230	93.73 248	96.92 192	98.50 149	93.52 246	98.34 176	98.10 234	93.83 182	95.94 183	97.98 188	85.59 256	99.03 193	94.35 185	80.94 340	98.22 196

LF4IMVS		93.14 279	92.79 273	94.20 308	95.88 308	88.67 325	97.66 253	97.07 303	93.81 183	91.71 300	97.65 218	77.96 324	98.81 222	91.47 265	91.92 261	95.12 329

IterMVS-SCA-FT		94.11 256	93.87 237	94.85 291	97.98 189	90.56 302	97.18 284	98.11 232	93.75 184	92.58 283	97.48 232	83.97 285	97.41 323	92.48 245	91.30 268	96.58 279

anonymousdsp		95.42 173	94.91 181	96.94 189	95.10 325	95.90 156	99.14 36	98.41 180	93.75 184	93.16 265	97.46 233	87.50 224	98.41 264	95.63 149	94.03 229	96.50 296

MDTV_nov1_ep13			95.40 154		97.48 222	88.34 329	96.85 308	97.29 295	93.74 186	97.48 128	97.26 245	89.18 179	99.05 189	91.92 257	97.43 173

BH-untuned		95.95 149	95.72 143	96.65 207	98.55 146	92.26 269	98.23 193	97.79 262	93.73 187	94.62 203	98.01 185	88.97 189	99.00 197	93.04 226	98.51 137	98.68 175

PatchMatch-RL		96.59 127	96.03 136	98.27 109	99.31 70	96.51 123	97.91 231	99.06 22	93.72 188	96.92 146	98.06 181	88.50 200	99.65 123	91.77 260	99.00 114	98.66 178

Effi-MVS+		97.12 109	96.69 113	98.39 104	98.19 172	96.72 113	97.37 269	98.43 178	93.71 189	97.65 119	98.02 183	92.20 120	99.25 164	96.87 101	97.79 162	99.19 132

IterMVS-LS		95.46 169	95.21 167	96.22 245	98.12 178	93.72 240	98.32 183	98.13 229	93.71 189	94.26 221	97.31 243	92.24 117	98.10 292	94.63 172	90.12 282	96.84 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		95.96 148	95.83 141	96.36 237	97.93 190	93.70 241	98.12 212	98.27 205	93.70 191	95.07 190	99.02 80	92.23 118	98.54 245	94.68 171	93.46 241	96.84 249

UnsupCasMVSNet_eth		90.99 298	89.92 301	94.19 309	94.08 336	89.83 307	97.13 289	98.67 128	93.69 192	85.83 338	96.19 312	75.15 337	96.74 332	89.14 301	79.41 341	96.00 315

PVSNet	91.96 18	96.35 135	96.15 132	96.96 188	99.17 98	92.05 273	96.08 323	98.68 120	93.69 192	97.75 110	97.80 207	88.86 191	99.69 119	94.26 190	99.01 113	99.15 138

PS-CasMVS		94.67 220	93.99 230	96.71 202	96.68 276	95.26 178	99.13 39	99.03 25	93.68 194	92.33 291	97.95 190	85.35 260	98.10 292	93.59 210	88.16 309	96.79 253

IterMVS		94.09 258	93.85 239	94.80 294	97.99 187	90.35 304	97.18 284	98.12 230	93.68 194	92.46 289	97.34 240	84.05 283	97.41 323	92.51 243	91.33 267	96.62 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SMA-MVS		98.58 23	98.25 38	99.56 5	99.51 39	99.04 11	98.95 72	98.80 86	93.67 196	99.37 13	99.52 3	96.52 17	99.89 35	98.06 33	99.81 10	99.76 26
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
FMVSNet3		94.97 203	94.26 212	97.11 179	98.18 174	96.62 115	98.56 149	98.26 209	93.67 196	94.09 230	97.10 255	84.25 278	98.01 300	92.08 250	92.14 257	96.70 266

CDS-MVSNet		96.99 113	96.69 113	97.90 133	98.05 184	95.98 142	98.20 198	98.33 193	93.67 196	96.95 142	98.49 140	93.54 100	98.42 257	95.24 161	97.74 165	99.31 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPP-MVSNet		97.46 87	97.28 85	97.99 128	98.64 139	95.38 172	99.33 13	98.31 196	93.61 199	97.19 132	99.07 77	94.05 95	99.23 167	96.89 95	98.43 143	99.37 110

CHOSEN 1792x2688		97.12 109	96.80 104	98.08 123	99.30 75	94.56 213	98.05 218	99.71 1	93.57 200	97.09 135	98.91 100	88.17 206	99.89 35	96.87 101	99.56 80	99.81 8

PEN-MVS		94.42 237	93.73 248	96.49 226	96.28 293	94.84 197	99.17 33	99.00 27	93.51 201	92.23 293	97.83 204	86.10 248	97.90 308	92.55 241	86.92 323	96.74 259

tpmrst		95.63 164	95.69 148	95.44 274	97.54 218	88.54 327	96.97 295	97.56 274	93.50 202	97.52 127	96.93 281	89.49 170	99.16 173	95.25 160	96.42 195	98.64 180

1314		96.25 141	95.73 142	97.79 138	97.13 250	95.55 167	98.19 202	98.59 141	93.47 203	92.03 297	97.82 205	91.33 142	99.49 145	94.62 174	98.44 141	98.32 194

baseline2		95.11 193	94.52 197	96.87 194	96.65 278	93.56 243	98.27 191	94.10 350	93.45 204	92.02 298	97.43 237	87.45 226	99.19 171	93.88 201	97.41 174	97.87 204

ACMH	92.88 16	94.55 228	93.95 232	96.34 239	97.63 208	93.26 257	98.81 103	98.49 170	93.43 205	89.74 318	98.53 136	81.91 297	99.08 187	93.69 205	93.30 247	96.70 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LFMVS		95.86 153	94.98 178	98.47 96	98.87 119	96.32 132	98.84 93	96.02 327	93.40 206	98.62 62	99.20 52	74.99 338	99.63 128	97.72 52	97.20 176	99.46 102

test20.03		90.89 299	90.38 297	92.43 322	93.48 340	88.14 332	98.33 178	97.56 274	93.40 206	87.96 329	96.71 292	80.69 307	94.13 349	79.15 344	86.17 328	95.01 334

PAPR		96.84 119	96.24 130	98.65 81	98.72 132	96.92 105	97.36 271	98.57 147	93.33 208	96.67 156	97.57 226	94.30 92	99.56 136	91.05 271	98.59 133	99.47 98

IB-MVS	91.98 17	93.27 274	91.97 285	97.19 173	97.47 223	93.41 251	97.09 290	95.99 328	93.32 209	92.47 288	95.73 319	78.06 323	99.53 142	94.59 177	82.98 333	98.62 181
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
PHI-MVS		98.34 48	98.06 49	99.18 47	99.15 101	98.12 58	99.04 53	99.09 20	93.32 209	98.83 48	99.10 69	96.54 16	99.83 55	97.70 57	99.76 32	99.59 80

XXY-MVS		95.20 189	94.45 203	97.46 161	96.75 272	96.56 121	98.86 89	98.65 135	93.30 211	93.27 262	98.27 167	84.85 268	98.87 215	94.82 169	91.26 270	96.96 231

原ACMM1				98.65 81	99.32 68	96.62 115		98.67 128	93.27 212	97.81 107	98.97 87	95.18 68	99.83 55	93.84 202	99.46 93	99.50 91

ZD-MVS					99.46 51	98.70 19		98.79 91	93.21 213	98.67 58	98.97 87	95.70 44	99.83 55	96.07 127	99.58 74

ETH3D cwj APD-0.16		97.96 58	97.52 72	99.29 31	99.05 105	98.52 27	98.33 178	98.68 120	93.18 214	98.68 57	99.13 64	94.62 81	99.83 55	96.45 117	99.55 83	99.52 85

TESTMET0.1,1		94.18 252	93.69 251	95.63 268	96.92 261	89.12 318	96.91 300	94.78 341	93.17 215	94.88 195	96.45 302	78.52 318	98.92 207	93.09 223	98.50 138	98.85 163

agg_prior1		97.95 61	97.51 74	99.28 35	99.30 75	98.38 35	97.81 242	98.72 109	93.16 216	97.57 125	98.66 124	96.14 26	99.81 70	96.63 110	99.56 80	99.66 65

PVSNet_Blended		97.38 96	97.12 90	98.14 118	99.25 86	95.35 175	97.28 278	99.26 8	93.13 217	97.94 100	98.21 171	92.74 109	99.81 70	96.88 98	99.40 99	99.27 124

DTE-MVSNet		93.98 263	93.26 266	96.14 248	96.06 302	94.39 218	99.20 29	98.86 61	93.06 218	91.78 299	97.81 206	85.87 252	97.58 319	90.53 277	86.17 328	96.46 300

CSCG		97.85 66	97.74 62	98.20 115	99.67 26	95.16 180	99.22 25	99.32 7	93.04 219	97.02 141	98.92 99	95.36 58	99.91 30	97.43 73	99.64 62	99.52 85

F-COLMAP		97.09 111	96.80 104	97.97 129	99.45 55	94.95 194	98.55 151	98.62 138	93.02 220	96.17 177	98.58 132	94.01 96	99.81 70	93.95 199	98.90 117	99.14 140

train_agg		97.97 57	97.52 72	99.33 27	99.31 70	98.50 29	97.92 229	98.73 107	92.98 221	97.74 111	98.68 121	96.20 23	99.80 79	96.59 111	99.57 75	99.68 57

test_8					99.29 78	98.44 31	97.89 235	98.72 109	92.98 221	97.70 114	98.66 124	96.20 23	99.80 79

thisisatest0515		95.61 167	94.89 182	97.76 141	98.15 177	95.15 182	96.77 311	94.41 344	92.95 223	97.18 133	97.43 237	84.78 269	99.45 152	94.63 172	97.73 166	98.68 175

1112_ss		96.63 124	96.00 137	98.50 93	98.56 144	96.37 129	98.18 206	98.10 234	92.92 224	94.84 196	98.43 145	92.14 121	99.58 133	94.35 185	96.51 192	99.56 84

DWT-MVSNet_test		94.82 210	94.36 208	96.20 246	97.35 234	90.79 297	98.34 176	96.57 326	92.91 225	95.33 188	96.44 303	82.00 296	99.12 179	94.52 179	95.78 213	98.70 172

test-mter		94.08 259	93.51 258	95.80 262	96.77 269	89.70 309	96.91 300	95.21 336	92.89 226	94.83 198	95.72 321	77.69 325	98.97 198	93.06 224	98.50 138	98.72 170

BH-w/o		95.38 176	95.08 173	96.26 244	98.34 160	91.79 277	97.70 250	97.43 289	92.87 227	94.24 223	97.22 249	88.66 194	98.84 218	91.55 264	97.70 167	98.16 198

PMMVS		96.60 125	96.33 126	97.41 164	97.90 192	93.93 230	97.35 272	98.41 180	92.84 228	97.76 109	97.45 235	91.10 148	99.20 170	96.26 123	97.91 157	99.11 143

LS3D		97.16 107	96.66 116	98.68 79	98.53 147	97.19 96	98.93 75	98.90 44	92.83 229	95.99 181	99.37 22	92.12 122	99.87 44	93.67 208	99.57 75	98.97 156

v2v482		94.69 215	94.03 224	96.65 207	96.17 297	94.79 202	98.67 132	98.08 240	92.72 230	94.00 235	97.16 253	87.69 221	98.45 253	92.91 229	88.87 302	96.72 262

eth_miper_zixun_eth		94.68 217	94.41 206	95.47 272	97.64 207	91.71 281	96.73 314	98.07 242	92.71 231	93.64 247	97.21 250	90.54 158	98.17 287	93.38 214	89.76 286	96.54 286

TEST9					99.31 70	98.50 29	97.92 229	98.73 107	92.63 232	97.74 111	98.68 121	96.20 23	99.80 79

tpm		94.13 254	93.80 241	95.12 282	96.50 284	87.91 334	97.44 262	95.89 332	92.62 233	96.37 173	96.30 306	84.13 282	98.30 277	93.24 219	91.66 264	99.14 140

DP-MVS Recon		97.86 65	97.46 77	99.06 61	99.53 36	98.35 43	98.33 178	98.89 46	92.62 233	98.05 86	98.94 96	95.34 59	99.65 123	96.04 131	99.42 96	99.19 132

v148		94.29 244	93.76 246	95.91 257	96.10 300	92.93 264	98.58 143	97.97 252	92.59 235	93.47 256	96.95 279	88.53 199	98.32 273	92.56 240	87.06 321	96.49 297

CDPH-MVS		97.94 62	97.49 75	99.28 35	99.47 48	98.44 31	97.91 231	98.67 128	92.57 236	98.77 51	98.85 104	95.93 38	99.72 108	95.56 150	99.69 52	99.68 57

CR-MVSNet		94.76 214	94.15 219	96.59 215	97.00 256	93.43 249	94.96 337	97.56 274	92.46 237	96.93 144	96.24 307	88.15 207	97.88 312	87.38 312	96.65 187	98.46 187

GBi-Net		94.49 233	93.80 241	96.56 219	98.21 169	95.00 188	98.82 97	98.18 218	92.46 237	94.09 230	97.07 262	81.16 300	97.95 304	92.08 250	92.14 257	96.72 262

test1		94.49 233	93.80 241	96.56 219	98.21 169	95.00 188	98.82 97	98.18 218	92.46 237	94.09 230	97.07 262	81.16 300	97.95 304	92.08 250	92.14 257	96.72 262

FMVSNet2		94.47 235	93.61 254	97.04 182	98.21 169	96.43 127	98.79 108	98.27 205	92.46 237	93.50 255	97.09 259	81.16 300	98.00 302	91.09 267	91.93 260	96.70 266

cl-mvsnet2		94.68 217	94.19 215	96.13 249	98.11 179	93.60 242	96.94 297	98.31 196	92.43 241	93.32 261	96.87 285	86.51 239	98.28 282	94.10 196	91.16 271	96.51 294

PLC	95.07 4	97.20 105	96.78 107	98.44 98	99.29 78	96.31 134	98.14 209	98.76 98	92.41 242	96.39 172	98.31 162	94.92 76	99.78 95	94.06 197	98.77 126	99.23 127
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		96.91 116	96.40 124	98.45 97	98.69 135	96.90 106	98.66 135	98.68 120	92.40 243	97.07 138	97.96 189	91.54 137	99.75 104	93.68 206	98.92 116	98.69 174
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
CPTT-MVS		97.72 72	97.32 84	98.92 69	99.64 28	97.10 99	99.12 41	98.81 76	92.34 244	98.09 84	99.08 76	93.01 106	99.92 21	96.06 130	99.77 26	99.75 28

HyFIR lowres test		96.90 117	96.49 122	98.14 118	99.33 65	95.56 165	97.38 267	99.65 2	92.34 244	97.61 122	98.20 172	89.29 176	99.10 185	96.97 88	97.60 170	99.77 20

pm-mvs1		93.94 264	93.06 268	96.59 215	96.49 285	95.16 180	98.95 72	98.03 249	92.32 246	91.08 306	97.84 201	84.54 274	98.41 264	92.16 248	86.13 330	96.19 311

V42		94.78 213	94.14 220	96.70 204	96.33 292	95.22 179	98.97 68	98.09 238	92.32 246	94.31 219	97.06 265	88.39 201	98.55 244	92.90 230	88.87 302	96.34 304

TR-MVS		94.94 206	94.20 214	97.17 175	97.75 199	94.14 226	97.59 257	97.02 308	92.28 248	95.75 184	97.64 220	83.88 287	98.96 201	89.77 289	96.15 207	98.40 189

miper_ehance_all_eth		95.01 198	94.69 190	95.97 254	97.70 204	93.31 255	97.02 293	98.07 242	92.23 249	93.51 254	96.96 277	91.85 128	98.15 288	93.68 206	91.16 271	96.44 301

cl_fuxian		94.79 212	94.43 205	95.89 259	97.75 199	93.12 262	97.16 287	98.03 249	92.23 249	93.46 257	97.05 267	91.39 139	98.01 300	93.58 211	89.21 296	96.53 288

MS-PatchMatch		93.84 265	93.63 253	94.46 305	96.18 296	89.45 313	97.76 246	98.27 205	92.23 249	92.13 295	97.49 231	79.50 312	98.69 229	89.75 290	99.38 100	95.25 326

miper_enhance_ethall		95.10 194	94.75 187	96.12 250	97.53 220	93.73 239	96.61 317	98.08 240	92.20 252	93.89 238	96.65 295	92.44 112	98.30 277	94.21 191	91.16 271	96.34 304

Test_1112_low_res		96.34 136	95.66 150	98.36 105	98.56 144	95.94 150	97.71 249	98.07 242	92.10 253	94.79 200	97.29 244	91.75 130	99.56 136	94.17 192	96.50 193	99.58 82

PVSNet_0	88.72 19	91.28 295	90.03 300	95.00 286	97.99 187	87.29 338	94.84 340	98.50 166	92.06 254	89.86 317	95.19 326	79.81 311	99.39 155	92.27 247	69.79 349	98.33 193

v7n		94.19 250	93.43 261	96.47 228	95.90 307	94.38 219	99.26 18	98.34 192	91.99 255	92.76 277	97.13 254	88.31 202	98.52 247	89.48 297	87.70 312	96.52 291

our_test_3		93.65 268	93.30 264	94.69 296	95.45 321	89.68 311	96.91 300	97.65 268	91.97 256	91.66 301	96.88 283	89.67 169	97.93 307	88.02 309	91.49 265	96.48 298

v8		94.47 235	93.77 244	96.57 218	96.36 290	94.83 199	99.05 52	98.19 215	91.92 257	93.16 265	96.97 275	88.82 193	98.48 249	91.69 262	87.79 311	96.39 302

testdata				98.26 111	99.20 97	95.36 173		98.68 120	91.89 258	98.60 64	99.10 69	94.44 90	99.82 63	94.27 189	99.44 95	99.58 82

Patchmatch-RL test		91.49 293	90.85 294	93.41 314	91.37 347	84.40 342	92.81 347	95.93 331	91.87 259	87.25 331	94.87 329	88.99 185	96.53 338	92.54 242	82.00 335	99.30 120

v1144		94.59 225	93.92 233	96.60 214	96.21 294	94.78 203	98.59 141	98.14 228	91.86 260	94.21 225	97.02 270	87.97 212	98.41 264	91.72 261	89.57 289	96.61 276

cl-mvsnet1		94.52 231	94.03 224	95.99 252	97.57 216	93.38 253	97.05 291	97.94 255	91.74 261	92.81 275	97.10 255	89.12 181	98.07 296	92.60 236	90.30 280	96.53 288

Fast-Effi-MVS+		96.28 139	95.70 147	98.03 126	98.29 165	95.97 147	98.58 143	98.25 210	91.74 261	95.29 189	97.23 248	91.03 150	99.15 176	92.90 230	97.96 156	98.97 156

cl-mvsnet_		94.51 232	94.01 227	96.02 251	97.58 212	93.40 252	97.05 291	97.96 254	91.73 263	92.76 277	97.08 261	89.06 184	98.13 290	92.61 235	90.29 281	96.52 291

LTVRE_ROB	92.95 15	94.60 223	93.90 235	96.68 206	97.41 232	94.42 216	98.52 153	98.59 141	91.69 264	91.21 304	98.35 155	84.87 267	99.04 192	91.06 269	93.44 244	96.60 277
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
miper_lstm_enhance		94.33 241	94.07 223	95.11 283	97.75 199	90.97 294	97.22 281	98.03 249	91.67 265	92.76 277	96.97 275	90.03 166	97.78 314	92.51 243	89.64 288	96.56 283

ETH3 D test6400		97.59 81	97.01 96	99.34 23	99.40 59	98.56 25	98.20 198	98.81 76	91.63 266	98.44 72	98.85 104	93.98 98	99.82 63	94.11 195	99.69 52	99.64 70

MVP-Stereo		94.28 246	93.92 233	95.35 276	94.95 327	92.60 267	97.97 226	97.65 268	91.61 267	90.68 311	97.09 259	86.32 245	98.42 257	89.70 292	99.34 102	95.02 333
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		94.32 242	93.58 255	96.53 223	96.10 300	94.45 215	98.50 158	98.17 223	91.54 268	94.19 226	97.06 265	86.95 234	98.43 256	90.14 281	89.57 289	96.70 266

TDRefinement		91.06 297	89.68 302	95.21 279	85.35 353	91.49 285	98.51 157	97.07 303	91.47 269	88.83 326	97.84 201	77.31 329	99.09 186	92.79 233	77.98 342	95.04 332

v144192		94.39 239	93.70 250	96.48 227	96.06 302	94.35 220	98.58 143	98.16 225	91.45 270	94.33 218	97.02 270	87.50 224	98.45 253	91.08 268	89.11 297	96.63 274

Baseline_NR-MVSNet		94.35 240	93.81 240	95.96 255	96.20 295	94.05 228	98.61 140	96.67 324	91.44 271	93.85 241	97.60 223	88.57 196	98.14 289	94.39 183	86.93 322	95.68 321

无先验							97.58 258	98.72 109	91.38 272				99.87 44	93.36 216		99.60 78

AllTest		95.24 186	94.65 191	96.99 184	99.25 86	93.21 259	98.59 141	98.18 218	91.36 273	93.52 252	98.77 114	84.67 271	99.72 108	89.70 292	97.87 159	98.02 201

TestCases				96.99 184	99.25 86	93.21 259		98.18 218	91.36 273	93.52 252	98.77 114	84.67 271	99.72 108	89.70 292	97.87 159	98.02 201

v10		94.29 244	93.55 256	96.51 225	96.39 289	94.80 201	98.99 64	98.19 215	91.35 275	93.02 271	96.99 273	88.09 209	98.41 264	90.50 278	88.41 306	96.33 306

v1921920		94.20 249	93.47 260	96.40 235	95.98 305	94.08 227	98.52 153	98.15 226	91.33 276	94.25 222	97.20 251	86.41 243	98.42 257	90.04 286	89.39 294	96.69 271

MSDG		95.93 150	95.30 165	97.83 136	98.90 116	95.36 173	96.83 310	98.37 187	91.32 277	94.43 213	98.73 118	90.27 163	99.60 131	90.05 285	98.82 124	98.52 185

旧先验2							97.57 259		91.30 278	98.67 58			99.80 79	95.70 147

tpmvs		94.60 223	94.36 208	95.33 277	97.46 224	88.60 326	96.88 306	97.68 266	91.29 279	93.80 244	96.42 304	88.58 195	99.24 166	91.06 269	96.04 210	98.17 197

PM-MVS		87.77 314	86.55 318	91.40 327	91.03 349	83.36 346	96.92 298	95.18 338	91.28 280	86.48 336	93.42 338	53.27 354	96.74 332	89.43 298	81.97 336	94.11 339

MIMVSNet		93.26 275	92.21 282	96.41 234	97.73 203	93.13 261	95.65 332	97.03 306	91.27 281	94.04 233	96.06 314	75.33 336	97.19 326	86.56 316	96.23 205	98.92 161

PAPM		94.95 204	94.00 228	97.78 139	97.04 255	95.65 162	96.03 326	98.25 210	91.23 282	94.19 226	97.80 207	91.27 144	98.86 217	82.61 335	97.61 169	98.84 165

dp		94.15 253	93.90 235	94.90 289	97.31 236	86.82 340	96.97 295	97.19 300	91.22 283	96.02 180	96.61 298	85.51 257	99.02 196	90.00 287	94.30 218	98.85 163

UniMVSNet_ETH3D		94.24 247	93.33 263	96.97 187	97.19 246	93.38 253	98.74 113	98.57 147	91.21 284	93.81 243	98.58 132	72.85 345	98.77 226	95.05 164	93.93 233	98.77 169

v1240		94.06 261	93.29 265	96.34 239	96.03 304	93.90 231	98.44 165	98.17 223	91.18 285	94.13 229	97.01 272	86.05 249	98.42 257	89.13 302	89.50 292	96.70 266

MVS_0304		92.81 283	92.01 284	95.23 278	97.46 224	91.33 288	98.17 207	98.81 76	91.13 286	93.80 244	95.68 324	66.08 351	98.06 297	90.79 273	96.13 208	96.32 307

tfpnnormal		93.66 266	92.70 275	96.55 222	96.94 260	95.94 150	98.97 68	99.19 15	91.04 287	91.38 303	97.34 240	84.94 266	98.61 236	85.45 325	89.02 300	95.11 330

MDTV_nov1_ep13_2view						84.26 343	96.89 305		90.97 288	97.90 104		89.89 168		93.91 200		99.18 136

TransMVSNet (Re)		92.67 285	91.51 290	96.15 247	96.58 280	94.65 204	98.90 78	96.73 320	90.86 289	89.46 321	97.86 198	85.62 255	98.09 294	86.45 317	81.12 338	95.71 320

Anonymous202405211		95.28 184	94.49 198	97.67 150	99.00 109	93.75 237	98.70 126	97.04 305	90.66 290	96.49 168	98.80 110	78.13 322	99.83 55	96.21 125	95.36 215	99.44 105

ppachtmachnet_test		93.22 276	92.63 276	94.97 287	95.45 321	90.84 295	96.88 306	97.88 259	90.60 291	92.08 296	97.26 245	88.08 210	97.86 313	85.12 327	90.33 279	96.22 309

CL-MVSNet_2432*1600		90.11 304	89.14 307	93.02 320	91.86 346	88.23 331	96.51 320	98.07 242	90.49 292	90.49 313	94.41 331	84.75 270	95.34 344	80.79 339	74.95 346	95.50 323

Anonymous20231206		91.66 292	91.10 292	93.33 316	94.02 339	87.35 337	98.58 143	97.26 298	90.48 293	90.16 315	96.31 305	83.83 289	96.53 338	79.36 343	89.90 285	96.12 312

VDDNet		95.36 179	94.53 196	97.86 134	98.10 180	95.13 184	98.85 90	97.75 264	90.46 294	98.36 76	99.39 14	73.27 344	99.64 125	97.98 36	96.58 189	98.81 166

TinyColmap		92.31 288	91.53 289	94.65 298	96.92 261	89.75 308	96.92 298	96.68 323	90.45 295	89.62 319	97.85 200	76.06 334	98.81 222	86.74 315	92.51 255	95.41 324

pmmvs4		94.69 215	93.99 230	96.81 197	95.74 311	95.94 150	97.40 265	97.67 267	90.42 296	93.37 259	97.59 224	89.08 183	98.20 285	92.97 228	91.67 263	96.30 308

FMVSNet1		93.19 278	92.07 283	96.56 219	97.54 218	95.00 188	98.82 97	98.18 218	90.38 297	92.27 292	97.07 262	73.68 343	97.95 304	89.36 299	91.30 268	96.72 262

DIV-MVS_2432*1600		90.38 302	89.38 305	93.40 315	92.85 343	88.94 322	97.95 227	97.94 255	90.35 298	90.25 314	93.96 336	79.82 310	95.94 341	84.62 331	76.69 344	95.33 325

RPSCF		94.87 209	95.40 154	93.26 318	98.89 117	82.06 349	98.33 178	98.06 247	90.30 299	96.56 161	99.26 42	87.09 230	99.49 145	93.82 203	96.32 198	98.24 195

ADS-MVSNet2		94.58 226	94.40 207	95.11 283	98.00 185	88.74 324	96.04 324	97.30 294	90.15 300	96.47 169	96.64 296	87.89 214	97.56 320	90.08 283	97.06 177	99.02 151

ADS-MVSNet		95.00 199	94.45 203	96.63 210	98.00 185	91.91 275	96.04 324	97.74 265	90.15 300	96.47 169	96.64 296	87.89 214	98.96 201	90.08 283	97.06 177	99.02 151

1121		97.37 97	96.77 111	99.16 50	99.34 62	97.99 65	98.19 202	98.68 120	90.14 302	98.01 94	98.97 87	94.80 79	99.87 44	93.36 216	99.46 93	99.61 75

新几何1				99.16 50	99.34 62	98.01 62		98.69 117	90.06 303	98.13 82	98.95 95	94.60 82	99.89 35	91.97 256	99.47 90	99.59 80

OpenMVS	93.04 13	95.83 155	95.00 176	98.32 107	97.18 247	97.32 87	99.21 28	98.97 30	89.96 304	91.14 305	99.05 79	86.64 238	99.92 21	93.38 214	99.47 90	97.73 209

COLMAP_ROB	93.27 12	95.33 182	94.87 183	96.71 202	99.29 78	93.24 258	98.58 143	98.11 232	89.92 305	93.57 250	99.10 69	86.37 244	99.79 91	90.78 274	98.10 153	97.09 224
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
KD-MVS_2432*1600		89.61 309	87.96 313	94.54 300	94.06 337	91.59 283	95.59 333	97.63 270	89.87 306	88.95 324	94.38 333	78.28 320	96.82 330	84.83 328	68.05 350	95.21 327

miper_refine_blended		89.61 309	87.96 313	94.54 300	94.06 337	91.59 283	95.59 333	97.63 270	89.87 306	88.95 324	94.38 333	78.28 320	96.82 330	84.83 328	68.05 350	95.21 327

QAPM		96.29 137	95.40 154	98.96 67	97.85 195	97.60 79	99.23 21	98.93 37	89.76 308	93.11 269	99.02 80	89.11 182	99.93 15	91.99 255	99.62 66	99.34 111

gm-plane-assit					95.88 308	87.47 336			89.74 309		96.94 280		99.19 171	93.32 218

pmmvs5		93.65 268	92.97 270	95.68 266	95.49 319	92.37 268	98.20 198	97.28 296	89.66 310	92.58 283	97.26 245	82.14 295	98.09 294	93.18 222	90.95 275	96.58 279

CostFormer		94.95 204	94.73 188	95.60 269	97.28 237	89.06 319	97.53 260	96.89 316	89.66 310	96.82 151	96.72 291	86.05 249	98.95 205	95.53 151	96.13 208	98.79 167

new-patchmatchnet		88.50 313	87.45 316	91.67 326	90.31 350	85.89 341	97.16 287	97.33 293	89.47 312	83.63 343	92.77 340	76.38 332	95.06 347	82.70 334	77.29 343	94.06 341

Patchmatch-test		94.42 237	93.68 252	96.63 210	97.60 210	91.76 278	94.83 341	97.49 284	89.45 313	94.14 228	97.10 255	88.99 185	98.83 220	85.37 326	98.13 152	99.29 122

DP-MVS		96.59 127	95.93 138	98.57 85	99.34 62	96.19 138	98.70 126	98.39 184	89.45 313	94.52 206	99.35 28	91.85 128	99.85 49	92.89 232	98.88 119	99.68 57

FMVSNet5		91.81 290	90.92 293	94.49 302	97.21 242	92.09 271	98.00 224	97.55 278	89.31 315	90.86 308	95.61 325	74.48 340	95.32 345	85.57 323	89.70 287	96.07 314

EG-PatchMatch MVS		91.13 296	90.12 299	94.17 310	94.73 331	89.00 321	98.13 211	97.81 261	89.22 316	85.32 340	96.46 301	67.71 348	98.42 257	87.89 311	93.82 235	95.08 331

DSMNet-mixed		92.52 287	92.58 277	92.33 323	94.15 335	82.65 347	98.30 186	94.26 347	89.08 317	92.65 281	95.73 319	85.01 265	95.76 342	86.24 318	97.76 164	98.59 182

pmmvs-eth3d		90.36 303	89.05 308	94.32 307	91.10 348	92.12 270	97.63 256	96.95 311	88.86 318	84.91 341	93.13 339	78.32 319	96.74 332	88.70 304	81.81 337	94.09 340

test222					99.23 93	97.17 97	97.40 265	98.66 131	88.68 319	98.05 86	98.96 93	94.14 94			99.53 85	99.61 75

MDA-MVSNet-bldmvs		89.97 306	88.35 311	94.83 293	95.21 324	91.34 286	97.64 254	97.51 281	88.36 320	71.17 352	96.13 313	79.22 314	96.63 337	83.65 332	86.27 327	96.52 291

MIMVSNet1		89.67 308	88.28 312	93.82 311	92.81 344	91.08 293	98.01 222	97.45 287	87.95 321	87.90 330	95.87 317	67.63 349	94.56 348	78.73 346	88.18 308	95.83 319

MDA-MVSNet_test_wron		90.71 300	89.38 305	94.68 297	94.83 329	90.78 298	97.19 283	97.46 285	87.60 322	72.41 351	95.72 321	86.51 239	96.71 335	85.92 321	86.80 325	96.56 283

YYNet1		90.70 301	89.39 304	94.62 299	94.79 330	90.65 300	97.20 282	97.46 285	87.54 323	72.54 350	95.74 318	86.51 239	96.66 336	86.00 320	86.76 326	96.54 286

Patchmtry		93.22 276	92.35 280	95.84 261	96.77 269	93.09 263	94.66 342	97.56 274	87.37 324	92.90 273	96.24 307	88.15 207	97.90 308	87.37 313	90.10 283	96.53 288

tpm2		94.19 250	93.76 246	95.46 273	97.23 240	89.04 320	97.31 276	96.85 319	87.08 325	96.21 176	96.79 289	83.75 291	98.74 227	92.43 246	96.23 205	98.59 182

PatchT		93.06 280	91.97 285	96.35 238	96.69 275	92.67 266	94.48 343	97.08 302	86.62 326	97.08 136	92.23 343	87.94 213	97.90 308	78.89 345	96.69 185	98.49 186

TAPA-MVS	93.98 7	95.35 180	94.56 195	97.74 143	99.13 102	94.83 199	98.33 178	98.64 136	86.62 326	96.29 174	98.61 127	94.00 97	99.29 162	80.00 341	99.41 97	99.09 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Anonymous20231211		94.10 257	93.26 266	96.61 212	99.11 104	94.28 221	99.01 60	98.88 49	86.43 328	92.81 275	97.57 226	81.66 299	98.68 232	94.83 168	89.02 300	96.88 243

new_pmnet		90.06 305	89.00 309	93.22 319	94.18 334	88.32 330	96.42 322	96.89 316	86.19 329	85.67 339	93.62 337	77.18 330	97.10 327	81.61 337	89.29 295	94.23 337

pmmvs6		91.77 291	90.63 295	95.17 281	94.69 332	91.24 291	98.67 132	97.92 257	86.14 330	89.62 319	97.56 228	75.79 335	98.34 271	90.75 275	84.56 332	95.94 317

test_0402		91.32 294	90.27 298	94.48 303	96.60 279	91.12 292	98.50 158	97.22 299	86.10 331	88.30 328	96.98 274	77.65 327	97.99 303	78.13 347	92.94 252	94.34 336

JIA-IIPM		93.35 271	92.49 278	95.92 256	96.48 286	90.65 300	95.01 336	96.96 310	85.93 332	96.08 178	87.33 348	87.70 220	98.78 225	91.35 266	95.58 214	98.34 192

N_pmnet		87.12 316	87.77 315	85.17 333	95.46 320	61.92 358	97.37 269	70.66 363	85.83 333	88.73 327	96.04 315	85.33 262	97.76 315	80.02 340	90.48 278	95.84 318

Anonymous20240529		95.10 194	94.22 213	97.75 142	99.01 108	94.26 223	98.87 87	98.83 68	85.79 334	96.64 157	98.97 87	78.73 317	99.85 49	96.27 122	94.89 216	99.12 142

cascas		94.63 222	93.86 238	96.93 190	96.91 263	94.27 222	96.00 327	98.51 161	85.55 335	94.54 205	96.23 309	84.20 281	98.87 215	95.80 140	96.98 180	97.66 212

gg-mvs-nofinetune		92.21 289	90.58 296	97.13 177	96.75 272	95.09 185	95.85 328	89.40 357	85.43 336	94.50 207	81.98 351	80.80 306	98.40 270	92.16 248	98.33 147	97.88 203

114514_t		96.93 115	96.27 128	98.92 69	99.50 41	97.63 77	98.85 90	98.90 44	84.80 337	97.77 108	99.11 67	92.84 107	99.66 122	94.85 167	99.77 26	99.47 98

PCF-MVS	93.45 11	94.68 217	93.43 261	98.42 101	98.62 141	96.77 111	95.48 335	98.20 214	84.63 338	93.34 260	98.32 161	88.55 198	99.81 70	84.80 330	98.96 115	98.68 175
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UnsupCasMVSNet_bld		87.17 315	85.12 319	93.31 317	91.94 345	88.77 323	94.92 339	98.30 202	84.30 339	82.30 344	90.04 345	63.96 353	97.25 325	85.85 322	74.47 348	93.93 343

test_part1		92.87 282	91.72 288	96.32 241	97.55 217	93.50 247	99.04 53	98.74 102	83.31 340	90.81 309	97.70 213	76.61 331	98.60 240	94.43 182	87.30 318	96.85 248

ANet_high		69.08 322	65.37 326	80.22 335	65.99 361	71.96 356	90.91 351	90.09 356	82.62 341	49.93 358	78.39 353	29.36 362	81.75 355	62.49 353	38.52 356	86.95 350

RPMNet		92.81 283	91.34 291	97.24 170	97.00 256	93.43 249	94.96 337	98.80 86	82.27 342	96.93 144	92.12 344	86.98 233	99.82 63	76.32 349	96.65 187	98.46 187

tpm cat1		93.36 270	92.80 272	95.07 285	97.58 212	87.97 333	96.76 312	97.86 260	82.17 343	93.53 251	96.04 315	86.13 247	99.13 178	89.24 300	95.87 211	98.10 199

CMPMVS	66.06 21	89.70 307	89.67 303	89.78 328	93.19 341	76.56 351	97.00 294	98.35 190	80.97 344	81.57 345	97.75 209	74.75 339	98.61 236	89.85 288	93.63 238	94.17 338
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		86.67 317	84.86 320	92.11 325	88.16 351	87.19 339	96.63 316	94.75 342	79.88 345	87.22 332	92.75 341	66.56 350	95.20 346	81.24 338	76.56 345	93.96 342

OpenMVS_ROB	86.42 20	89.00 312	87.43 317	93.69 312	93.08 342	89.42 314	97.91 231	96.89 316	78.58 346	85.86 337	94.69 330	69.48 347	98.29 280	77.13 348	93.29 248	93.36 345

MVS		94.67 220	93.54 257	98.08 123	96.88 265	96.56 121	98.19 202	98.50 166	78.05 347	92.69 280	98.02 183	91.07 149	99.63 128	90.09 282	98.36 146	98.04 200

DeepMVS_CX				86.78 330	97.09 253	72.30 354		95.17 339	75.92 348	84.34 342	95.19 326	70.58 346	95.35 343	79.98 342	89.04 299	92.68 346

MVS-HIRNet		89.46 311	88.40 310	92.64 321	97.58 212	82.15 348	94.16 346	93.05 353	75.73 349	90.90 307	82.52 350	79.42 313	98.33 272	83.53 333	98.68 127	97.43 214

PMMVS2		77.95 320	75.44 324	85.46 332	82.54 354	74.95 353	94.23 345	93.08 352	72.80 350	74.68 348	87.38 347	36.36 360	91.56 352	73.95 350	63.94 352	89.87 347

FPMVS		77.62 321	77.14 321	79.05 336	79.25 357	60.97 359	95.79 329	95.94 330	65.96 351	67.93 353	94.40 332	37.73 359	88.88 354	68.83 351	88.46 305	87.29 348

Gipuma		78.40 319	76.75 322	83.38 334	95.54 317	80.43 350	79.42 355	97.40 291	64.67 352	73.46 349	80.82 352	45.65 356	93.14 350	66.32 352	87.43 315	76.56 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		78.70 318	76.24 323	86.08 331	77.26 359	71.99 355	94.34 344	96.72 321	61.62 353	76.53 347	89.33 346	33.91 361	92.78 351	81.85 336	74.60 347	93.46 344

PMVS	61.03 23	65.95 324	63.57 328	73.09 339	57.90 362	51.22 363	85.05 354	93.93 351	54.45 354	44.32 359	83.57 349	13.22 363	89.15 353	58.68 354	81.00 339	78.91 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 325	64.25 327	67.02 340	82.28 355	59.36 361	91.83 350	85.63 359	52.69 355	60.22 355	77.28 354	41.06 358	80.12 357	46.15 356	41.14 354	61.57 355

MVE	62.14 22	63.28 327	59.38 330	74.99 337	74.33 360	65.47 357	85.55 353	80.50 362	52.02 356	51.10 357	75.00 356	10.91 366	80.50 356	51.60 355	53.40 353	78.99 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		64.07 326	63.26 329	66.53 341	81.73 356	58.81 362	91.85 349	84.75 360	51.93 357	59.09 356	75.13 355	43.32 357	79.09 358	42.03 357	39.47 355	61.69 354

tmp_tt		68.90 323	66.97 325	74.68 338	50.78 363	59.95 360	87.13 352	83.47 361	38.80 358	62.21 354	96.23 309	64.70 352	76.91 359	88.91 303	30.49 357	87.19 349

wuyk23d		30.17 328	30.18 332	30.16 342	78.61 358	43.29 364	66.79 356	14.21 364	17.31 359	14.82 362	11.93 362	11.55 365	41.43 360	37.08 358	19.30 358	5.76 358

testmvs		21.48 330	24.95 333	11.09 344	14.89 364	6.47 366	96.56 318	9.87 365	7.55 360	17.93 360	39.02 358	9.43 367	5.90 362	16.56 360	12.72 359	20.91 357

test123		20.95 331	23.72 334	12.64 343	13.54 365	8.19 365	96.55 319	6.13 366	7.48 361	16.74 361	37.98 359	12.97 364	6.05 361	16.69 359	5.43 360	23.68 356

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

cdsmvs_eth3d_5k		23.98 329	31.98 331	0.00 345	0.00 366	0.00 367	0.00 357	98.59 141	0.00 362	0.00 363	98.61 127	90.60 157	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		7.88 333	10.50 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	94.51 85	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		8.20 332	10.94 335	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	98.43 145	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

OPU-MVS				99.37 20	99.24 92	99.05 10	99.02 58				99.16 61	97.81 2	99.37 157	97.24 79	99.73 43	99.70 48

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 68	98.88 49					99.94 3	98.47 15	99.81 10	99.84 4

GSMVS																99.20 129

test_part2					99.63 29	99.18 8				99.27 17

sam_mvs1												89.45 172				99.20 129

sam_mvs												88.99 185

ambc				89.49 329	86.66 352	75.78 352	92.66 348	96.72 321		86.55 335	92.50 342	46.01 355	97.90 308	90.32 279	82.09 334	94.80 335

MTGPA								98.74 102

test_post1							96.68 315				30.43 361	87.85 217	98.69 229	92.59 238

test_post											31.83 360	88.83 192	98.91 208

patchmatchnet-post											95.10 328	89.42 173	98.89 212

GG-mvs-BLEND				96.59 215	96.34 291	94.98 191	96.51 320	88.58 358		93.10 270	94.34 335	80.34 309	98.05 298	89.53 295	96.99 179	96.74 259

MTMP							98.89 82	94.14 349

test9_res														96.39 121	99.57 75	99.69 51

agg_prior2														95.87 137	99.57 75	99.68 57

agg_prior					99.30 75	98.38 35		98.72 109		97.57 125			99.81 70

test_prior4						98.01 62	97.86 238

test_prior				99.19 43	99.31 70	98.22 50		98.84 65					99.70 114			99.65 67

新几何2							97.64 254

旧先验1					99.29 78	97.48 82		98.70 116			99.09 74	95.56 47			99.47 90	99.61 75

原ACMM2							97.67 252

testdata2													99.89 35	91.65 263

segment_acmp												96.85 11

test12				99.18 47	99.16 99	98.19 52		98.53 156		98.07 85		95.13 70	99.72 108		99.56 80	99.63 73

plane_prior7					97.42 229	94.63 206

plane_prior6					97.35 234	94.61 209						87.09 230

plane_prior5								98.56 149					99.03 193	96.07 127	94.27 219	96.92 234

plane_prior4											98.28 164

plane_prior1					97.37 233

n2								0.00 367

nn								0.00 367

door-mid								94.37 345

lessismore_v0				94.45 306	94.93 328	88.44 328		91.03 355		86.77 334	97.64 220	76.23 333	98.42 257	90.31 280	85.64 331	96.51 294

test11								98.66 131

door								94.64 343

HQP5-MVS						94.25 224

BP-MVS														95.30 156

HQP4-MVS										94.45 209			98.96 201			96.87 245

HQP3-MVS								98.46 171							94.18 223

HQP2-MVS												86.75 236

NP-MVS					97.28 237	94.51 214					97.73 210

ACMMP++_ref															92.97 251

ACMMP++															93.61 239

Test By Simon												94.64 80

High-resolution multi-view benchmark