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.

patch_mono-2		98.36 48	98.87 3	96.82 210	99.53 39	90.68 314	98.64 153	99.29 8	97.88 5	99.19 29	99.52 3	96.80 15	99.97 1	99.11 1	99.86 1	99.82 10

zzz-MVS		98.55 31	98.25 43	99.46 15	99.76 2	98.64 27	98.55 168	98.74 108	97.27 40	98.02 104	99.39 18	94.81 84	99.96 2	97.91 49	99.79 23	99.77 23

MTAPA		98.58 24	98.29 40	99.46 15	99.76 2	98.64 27	98.90 93	98.74 108	97.27 40	98.02 104	99.39 18	94.81 84	99.96 2	97.91 49	99.79 23	99.77 23

DVP-MVS++		99.08 2	98.89 2	99.64 3	99.17 103	99.23 7	99.69 1	98.88 51	97.32 33	99.53 9	99.47 10	97.81 3	99.94 4	98.47 22	99.72 54	99.74 37

MSC_two_6792asdad				99.62 6	99.17 103	99.08 11		98.63 143					99.94 4	98.53 14	99.80 19	99.86 2

No_MVS				99.62 6	99.17 103	99.08 11		98.63 143					99.94 4	98.53 14	99.80 19	99.86 2

SED-MVS		99.09 1	98.91 1	99.63 4	99.71 21	99.24 5	99.02 73	98.87 58	97.65 10	99.73 1	99.48 8	97.53 7	99.94 4	98.43 26	99.81 12	99.70 54

test_241102_TWO								98.87 58	97.65 10	99.53 9	99.48 8	97.34 11	99.94 4	98.43 26	99.80 19	99.83 7

DVP-MVS		99.03 3	98.83 5	99.63 4	99.72 13	99.25 2	98.97 83	98.58 153	97.62 12	99.45 11	99.46 13	97.42 9	99.94 4	98.47 22	99.81 12	99.69 57
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									97.32 33	99.45 11	99.46 13	97.88 1	99.94 4	98.47 22	99.86 1	99.85 4

test_0728_SECOND				99.71 1	99.72 13	99.35 1	98.97 83	98.88 51					99.94 4	98.47 22	99.81 12	99.84 6

DPE-MVS		98.92 5	98.67 8	99.65 2	99.58 34	99.20 9	98.42 186	98.91 45	97.58 15	99.54 8	99.46 13	97.10 12	99.94 4	97.64 73	99.84 10	99.83 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
region2R		98.61 18	98.38 25	99.29 34	99.74 8	98.16 60	99.23 30	98.93 39	96.15 91	98.94 45	99.17 63	95.91 43	99.94 4	97.55 82	99.79 23	99.78 16

ACMMPR		98.59 21	98.36 27	99.29 34	99.74 8	98.15 61	99.23 30	98.95 35	96.10 96	98.93 50	99.19 62	95.70 50	99.94 4	97.62 74	99.79 23	99.78 16

MP-MVS		98.33 54	98.01 58	99.28 38	99.75 4	98.18 58	99.22 34	98.79 96	96.13 93	97.92 117	99.23 52	94.54 90	99.94 4	96.74 126	99.78 27	99.73 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		98.49 37	98.23 47	99.27 41	99.72 13	98.08 64	98.99 79	99.49 5	95.43 126	99.03 39	99.32 37	95.56 53	99.94 4	96.80 122	99.77 30	99.78 16

mPP-MVS		98.51 36	98.26 42	99.25 42	99.75 4	98.04 65	99.28 25	98.81 80	96.24 88	98.35 89	99.23 52	95.46 57	99.94 4	97.42 88	99.81 12	99.77 23

CP-MVS		98.57 27	98.36 27	99.19 46	99.66 28	97.86 73	99.34 19	98.87 58	95.96 101	98.60 75	99.13 73	96.05 36	99.94 4	97.77 62	99.86 1	99.77 23

ZNCC-MVS		98.49 37	98.20 49	99.35 25	99.73 12	98.39 39	99.19 41	98.86 64	95.77 109	98.31 92	99.10 78	95.46 57	99.93 19	97.57 80	99.81 12	99.74 37

testtj		98.33 54	97.95 61	99.47 14	99.49 49	98.70 23	98.83 110	98.86 64	95.48 123	98.91 52	99.17 63	95.48 56	99.93 19	95.80 157	99.53 92	99.76 30

GST-MVS		98.43 43	98.12 52	99.34 26	99.72 13	98.38 40	99.09 58	98.82 74	95.71 112	98.73 64	99.06 88	95.27 70	99.93 19	97.07 100	99.63 70	99.72 46

abl_6		98.30 57	98.03 57	99.13 57	99.56 37	97.76 80	99.13 50	98.82 74	96.14 92	99.26 22	99.37 26	93.33 109	99.93 19	96.96 105	99.67 60	99.69 57

QAPM		96.29 145	95.40 168	98.96 70	97.85 213	97.60 85	99.23 30	98.93 39	89.76 326	93.11 286	99.02 90	89.11 194	99.93 19	91.99 272	99.62 72	99.34 121

ACMMP		98.23 58	97.95 61	99.09 62	99.74 8	97.62 84	99.03 69	99.41 6	95.98 99	97.60 137	99.36 30	94.45 95	99.93 19	97.14 97	98.85 133	99.70 54
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
CANet		98.05 62	97.76 67	98.90 74	98.73 142	97.27 95	98.35 193	98.78 99	97.37 32	97.72 127	98.96 103	91.53 145	99.92 25	98.79 6	99.65 64	99.51 98

MP-MVS-pluss		98.31 56	97.92 63	99.49 12	99.72 13	98.88 18	98.43 184	98.78 99	94.10 183	97.69 129	99.42 16	95.25 72	99.92 25	98.09 40	99.80 19	99.67 67
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		98.61 18	98.30 39	99.55 9	99.62 32	98.95 17	98.82 113	98.81 80	95.80 108	99.16 32	99.47 10	95.37 63	99.92 25	97.89 52	99.75 42	99.79 13

HFP-MVS		98.63 17	98.40 23	99.32 31	99.72 13	98.29 51	99.23 30	98.96 33	96.10 96	98.94 45	99.17 63	96.06 34	99.92 25	97.62 74	99.78 27	99.75 32

#test#		98.54 33	98.27 41	99.32 31	99.72 13	98.29 51	98.98 82	98.96 33	95.65 117	98.94 45	99.17 63	96.06 34	99.92 25	97.21 96	99.78 27	99.75 32

HPM-MVS++		98.58 24	98.25 43	99.55 9	99.50 45	99.08 11	98.72 137	98.66 136	97.51 18	98.15 93	98.83 119	95.70 50	99.92 25	97.53 84	99.67 60	99.66 71

CPTT-MVS		97.72 77	97.32 89	98.92 72	99.64 30	97.10 104	99.12 52	98.81 80	92.34 261	98.09 97	99.08 86	93.01 113	99.92 25	96.06 147	99.77 30	99.75 32

3Dnovator	94.51 5	97.46 92	96.93 105	99.07 63	97.78 216	97.64 82	99.35 18	99.06 23	97.02 55	93.75 264	99.16 68	89.25 189	99.92 25	97.22 95	99.75 42	99.64 76

OpenMVS	93.04 13	95.83 169	95.00 192	98.32 111	97.18 262	97.32 93	99.21 37	98.97 31	89.96 322	91.14 323	99.05 89	86.64 250	99.92 25	93.38 230	99.47 99	97.73 222

CANet_DTU		96.96 119	96.55 124	98.21 119	98.17 193	96.07 150	97.98 242	98.21 222	97.24 42	97.13 147	98.93 107	86.88 247	99.91 34	95.00 182	99.37 110	98.66 190

PVSNet_Blended_VisFu		97.70 78	97.46 82	98.44 103	99.27 87	95.91 165	98.63 155	99.16 18	94.48 174	97.67 130	98.88 112	92.80 115	99.91 34	97.11 98	99.12 119	99.50 100

CSCG		97.85 72	97.74 68	98.20 120	99.67 27	95.16 193	99.22 34	99.32 7	93.04 237	97.02 154	98.92 109	95.36 64	99.91 34	97.43 87	99.64 68	99.52 94

PS-MVSNAJ		97.73 76	97.77 66	97.62 162	98.68 150	95.58 176	97.34 291	98.51 167	97.29 35	98.66 71	97.88 217	94.51 91	99.90 37	97.87 54	99.17 118	97.39 230

UGNet		96.78 126	96.30 133	98.19 122	98.24 183	95.89 167	98.88 100	98.93 39	97.39 29	96.81 165	97.84 221	82.60 309	99.90 37	96.53 131	99.49 96	98.79 181
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
SMA-MVS		98.58 24	98.25 43	99.56 8	99.51 43	99.04 15	98.95 87	98.80 91	93.67 213	99.37 16	99.52 3	96.52 21	99.89 39	98.06 41	99.81 12	99.76 30
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
XVS		98.70 10	98.49 20	99.34 26	99.70 24	98.35 48	99.29 23	98.88 51	97.40 27	98.46 79	99.20 59	95.90 44	99.89 39	97.85 56	99.74 45	99.78 16

X-MVStestdata		94.06 276	92.30 296	99.34 26	99.70 24	98.35 48	99.29 23	98.88 51	97.40 27	98.46 79	43.50 376	95.90 44	99.89 39	97.85 56	99.74 45	99.78 16

新几何1				99.16 53	99.34 66	98.01 67		98.69 122	90.06 321	98.13 94	98.95 105	94.60 89	99.89 39	91.97 273	99.47 99	99.59 87

testdata2													99.89 39	91.65 280

CHOSEN 1792x2688		97.12 114	96.80 109	98.08 129	99.30 79	94.56 226	98.05 235	99.71 1	93.57 217	97.09 148	98.91 110	88.17 218	99.89 39	96.87 117	99.56 86	99.81 11

EPNet		97.28 105	96.87 108	98.51 95	94.98 345	96.14 148	98.90 93	97.02 326	98.28 1	95.99 195	99.11 76	91.36 147	99.89 39	96.98 102	99.19 117	99.50 100
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator+	94.38 6	97.43 97	96.78 112	99.38 20	97.83 214	98.52 32	99.37 15	98.71 118	97.09 53	92.99 289	99.13 73	89.36 185	99.89 39	96.97 103	99.57 81	99.71 50

DELS-MVS		98.40 45	98.20 49	98.99 66	99.00 121	97.66 81	97.75 264	98.89 48	97.71 9	98.33 90	98.97 97	94.97 81	99.88 47	98.42 28	99.76 36	99.42 117
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
无先验							97.58 276	98.72 114	91.38 290				99.87 48	93.36 232		99.60 85

1121		97.37 102	96.77 116	99.16 53	99.34 66	97.99 70	98.19 219	98.68 125	90.14 320	98.01 108	98.97 97	94.80 86	99.87 48	93.36 232	99.46 102	99.61 82

SteuartSystems-ACMMP		98.90 6	98.75 6	99.36 24	99.22 98	98.43 38	99.10 57	98.87 58	97.38 30	99.35 17	99.40 17	97.78 5	99.87 48	97.77 62	99.85 5	99.78 16
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS	95.98 3	97.88 70	97.58 72	98.77 78	99.25 90	96.93 110	98.83 110	98.75 106	96.96 58	96.89 161	99.50 5	90.46 167	99.87 48	97.84 58	99.76 36	99.52 94
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		97.16 112	96.66 121	98.68 82	98.53 161	97.19 102	98.93 91	98.90 46	92.83 246	95.99 195	99.37 26	92.12 129	99.87 48	93.67 224	99.57 81	98.97 170

h-mvs33		96.17 150	95.62 164	97.81 145	99.03 117	94.45 228	98.64 153	98.75 106	97.48 20	98.67 67	98.72 132	89.76 177	99.86 53	97.95 46	81.59 355	99.11 155

Anonymous20240529		95.10 210	94.22 227	97.75 150	99.01 120	94.26 237	98.87 103	98.83 72	85.79 353	96.64 170	98.97 97	78.73 333	99.85 54	96.27 140	94.89 227	99.12 154

sss		97.39 100	96.98 104	98.61 86	98.60 157	96.61 124	98.22 211	98.93 39	93.97 191	98.01 108	98.48 158	91.98 133	99.85 54	96.45 134	98.15 162	99.39 118

DP-MVS		96.59 132	95.93 146	98.57 88	99.34 66	96.19 147	98.70 142	98.39 192	89.45 331	94.52 222	99.35 32	91.85 135	99.85 54	92.89 249	98.88 130	99.68 63

xxxxxxxxxxxxxcwj		98.70 10	98.50 17	99.30 33	99.46 55	98.38 40	98.21 212	98.52 164	97.95 3	99.32 18	99.39 18	96.22 24	99.84 57	97.72 65	99.73 47	99.67 67

SF-MVS		98.59 21	98.32 38	99.41 19	99.54 38	98.71 22	99.04 66	98.81 80	95.12 144	99.32 18	99.39 18	96.22 24	99.84 57	97.72 65	99.73 47	99.67 67

APDe-MVS		99.02 4	98.84 4	99.55 9	99.57 35	98.96 16	99.39 13	98.93 39	97.38 30	99.41 13	99.54 1	96.66 17	99.84 57	98.86 4	99.85 5	99.87 1

ZD-MVS					99.46 55	98.70 23		98.79 96	93.21 230	98.67 67	98.97 97	95.70 50	99.83 60	96.07 144	99.58 80

ETH3D cwj APD-0.16		97.96 64	97.52 77	99.29 34	99.05 114	98.52 32	98.33 195	98.68 125	93.18 231	98.68 66	99.13 73	94.62 88	99.83 60	96.45 134	99.55 90	99.52 94

Anonymous202405211		95.28 200	94.49 214	97.67 158	99.00 121	93.75 252	98.70 142	97.04 323	90.66 308	96.49 181	98.80 122	78.13 338	99.83 60	96.21 143	95.36 226	99.44 114

原ACMM1				98.65 84	99.32 72	96.62 122		98.67 133	93.27 229	97.81 121	98.97 97	95.18 75	99.83 60	93.84 218	99.46 102	99.50 100

VNet		97.79 74	97.40 86	98.96 70	98.88 131	97.55 86	98.63 155	98.93 39	96.74 68	99.02 40	98.84 117	90.33 170	99.83 60	98.53 14	96.66 198	99.50 100

MCST-MVS		98.65 14	98.37 26	99.48 13	99.60 33	98.87 19	98.41 187	98.68 125	97.04 54	98.52 78	98.80 122	96.78 16	99.83 60	97.93 48	99.61 73	99.74 37

NCCC		98.61 18	98.35 29	99.38 20	99.28 86	98.61 29	98.45 179	98.76 103	97.82 6	98.45 82	98.93 107	96.65 18	99.83 60	97.38 90	99.41 106	99.71 50

PHI-MVS		98.34 52	98.06 55	99.18 50	99.15 109	98.12 63	99.04 66	99.09 21	93.32 226	98.83 57	99.10 78	96.54 20	99.83 60	97.70 70	99.76 36	99.59 87

test1172		98.56 29	98.35 29	99.16 53	99.53 39	97.94 71	99.09 58	98.83 72	96.52 77	99.05 38	99.34 35	95.34 65	99.82 68	97.86 55	99.64 68	99.73 42

SR-MVS-dyc-post		98.54 33	98.35 29	99.13 57	99.49 49	97.86 73	99.11 54	98.80 91	96.49 78	99.17 30	99.35 32	95.34 65	99.82 68	97.72 65	99.65 64	99.71 50

ETH3 D test6400		97.59 86	97.01 101	99.34 26	99.40 63	98.56 30	98.20 215	98.81 80	91.63 284	98.44 83	98.85 115	93.98 104	99.82 68	94.11 211	99.69 58	99.64 76

SR-MVS		98.57 27	98.35 29	99.24 43	99.53 39	98.18 58	99.09 58	98.82 74	96.58 74	99.10 35	99.32 37	95.39 61	99.82 68	97.70 70	99.63 70	99.72 46

testdata				98.26 116	99.20 101	95.36 186		98.68 125	91.89 276	98.60 75	99.10 78	94.44 96	99.82 68	94.27 205	99.44 104	99.58 91

RPMNet		92.81 297	91.34 305	97.24 179	97.00 270	93.43 264	94.96 354	98.80 91	82.27 360	96.93 157	92.12 363	86.98 245	99.82 68	76.32 368	96.65 199	98.46 199

DeepC-MVS_fast	96.70 1	98.55 31	98.34 33	99.18 50	99.25 90	98.04 65	98.50 175	98.78 99	97.72 7	98.92 51	99.28 44	95.27 70	99.82 68	97.55 82	99.77 30	99.69 57
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
9.14			98.06 55		99.47 52		98.71 138	98.82 74	94.36 177	99.16 32	99.29 43	96.05 36	99.81 75	97.00 101	99.71 56

ETH3D-3000-0.1		98.35 50	98.00 59	99.38 20	99.47 52	98.68 25	98.67 148	98.84 69	94.66 167	99.11 34	99.25 50	95.46 57	99.81 75	96.80 122	99.73 47	99.63 79

agg_prior1		97.95 67	97.51 79	99.28 38	99.30 79	98.38 40	97.81 259	98.72 114	93.16 233	97.57 138	98.66 139	96.14 30	99.81 75	96.63 127	99.56 86	99.66 71

agg_prior					99.30 79	98.38 40		98.72 114		97.57 138			99.81 75

UA-Net		97.96 64	97.62 70	98.98 68	98.86 133	97.47 89	98.89 97	99.08 22	96.67 71	98.72 65	99.54 1	93.15 112	99.81 75	94.87 183	98.83 134	99.65 73

PVSNet_BlendedMVS		96.73 127	96.60 122	97.12 188	99.25 90	95.35 188	98.26 209	99.26 9	94.28 178	97.94 114	97.46 251	92.74 116	99.81 75	96.88 114	93.32 263	96.20 327

PVSNet_Blended		97.38 101	97.12 95	98.14 123	99.25 90	95.35 188	97.28 296	99.26 9	93.13 234	97.94 114	98.21 190	92.74 116	99.81 75	96.88 114	99.40 108	99.27 134

F-COLMAP		97.09 116	96.80 109	97.97 135	99.45 59	94.95 206	98.55 168	98.62 145	93.02 238	96.17 190	98.58 149	94.01 102	99.81 75	93.95 215	98.90 128	99.14 152

PCF-MVS	93.45 11	94.68 232	93.43 276	98.42 106	98.62 155	96.77 117	95.48 352	98.20 224	84.63 357	93.34 277	98.32 180	88.55 211	99.81 75	84.80 347	98.96 126	98.68 187
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
xiu_mvs_v1_base_debu		97.60 83	97.56 74	97.72 152	98.35 171	95.98 152	97.86 255	98.51 167	97.13 50	99.01 41	98.40 168	91.56 141	99.80 84	98.53 14	98.68 138	97.37 232

xiu_mvs_v2_base		97.66 80	97.70 69	97.56 166	98.61 156	95.46 182	97.44 280	98.46 179	97.15 48	98.65 72	98.15 194	94.33 97	99.80 84	97.84 58	98.66 142	97.41 228

xiu_mvs_v1_base		97.60 83	97.56 74	97.72 152	98.35 171	95.98 152	97.86 255	98.51 167	97.13 50	99.01 41	98.40 168	91.56 141	99.80 84	98.53 14	98.68 138	97.37 232

xiu_mvs_v1_base_debi		97.60 83	97.56 74	97.72 152	98.35 171	95.98 152	97.86 255	98.51 167	97.13 50	99.01 41	98.40 168	91.56 141	99.80 84	98.53 14	98.68 138	97.37 232

TEST9					99.31 74	98.50 34	97.92 246	98.73 112	92.63 249	97.74 125	98.68 136	96.20 27	99.80 84

train_agg		97.97 63	97.52 77	99.33 30	99.31 74	98.50 34	97.92 246	98.73 112	92.98 239	97.74 125	98.68 136	96.20 27	99.80 84	96.59 128	99.57 81	99.68 63

test_8					99.29 82	98.44 36	97.89 252	98.72 114	92.98 239	97.70 128	98.66 139	96.20 27	99.80 84

Regformer-4		98.64 15	98.53 14	98.99 66	99.43 61	97.37 92	98.40 188	98.79 96	97.46 23	99.09 36	99.31 39	95.86 46	99.80 84	98.64 8	99.76 36	99.79 13

Regformer-2		98.69 12	98.52 15	99.19 46	99.35 64	98.01 67	98.37 190	98.81 80	97.48 20	99.21 25	99.21 55	96.13 31	99.80 84	98.40 30	99.73 47	99.75 32

旧先验2							97.57 277		91.30 296	98.67 67			99.80 84	95.70 164

APD-MVS_3200maxsize		98.53 35	98.33 37	99.15 56	99.50 45	97.92 72	99.15 45	98.81 80	96.24 88	99.20 26	99.37 26	95.30 68	99.80 84	97.73 64	99.67 60	99.72 46

APD-MVS		98.35 50	98.00 59	99.42 18	99.51 43	98.72 21	98.80 120	98.82 74	94.52 172	99.23 24	99.25 50	95.54 55	99.80 84	96.52 132	99.77 30	99.74 37
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSP-MVS		98.74 9	98.55 13	99.29 34	99.75 4	98.23 54	99.26 27	98.88 51	97.52 17	99.41 13	98.78 124	96.00 38	99.79 96	97.79 61	99.59 77	99.85 4
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
EI-MVSNet-UG-set		98.41 44	98.34 33	98.61 86	99.45 59	96.32 141	98.28 206	98.68 125	97.17 46	98.74 62	99.37 26	95.25 72	99.79 96	98.57 12	99.54 91	99.73 42

Regformer-1		98.66 13	98.51 16	99.12 60	99.35 64	97.81 79	98.37 190	98.76 103	97.49 19	99.20 26	99.21 55	96.08 33	99.79 96	98.42 28	99.73 47	99.75 32

COLMAP_ROB	93.27 12	95.33 198	94.87 199	96.71 215	99.29 82	93.24 273	98.58 161	98.11 243	89.92 323	93.57 268	99.10 78	86.37 257	99.79 96	90.78 291	98.10 164	97.09 237
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EI-MVSNet-Vis-set		98.47 40	98.39 24	98.69 81	99.46 55	96.49 132	98.30 203	98.69 122	97.21 43	98.84 55	99.36 30	95.41 60	99.78 100	98.62 10	99.65 64	99.80 12

VDD-MVS		95.82 170	95.23 182	97.61 163	98.84 136	93.98 244	98.68 145	97.40 308	95.02 151	97.95 112	99.34 35	74.37 359	99.78 100	98.64 8	96.80 194	99.08 161

CNVR-MVS		98.78 7	98.56 12	99.45 17	99.32 72	98.87 19	98.47 178	98.81 80	97.72 7	98.76 61	99.16 68	97.05 13	99.78 100	98.06 41	99.66 63	99.69 57

WTY-MVS		97.37 102	96.92 106	98.72 80	98.86 133	96.89 114	98.31 201	98.71 118	95.26 137	97.67 130	98.56 152	92.21 126	99.78 100	95.89 152	96.85 193	99.48 105

PLC	95.07 4	97.20 110	96.78 112	98.44 103	99.29 82	96.31 143	98.14 226	98.76 103	92.41 259	96.39 185	98.31 181	94.92 83	99.78 100	94.06 213	98.77 137	99.23 137
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Regformer-3		98.59 21	98.50 17	98.86 76	99.43 61	97.05 105	98.40 188	98.68 125	97.43 26	99.06 37	99.31 39	95.80 47	99.77 105	98.62 10	99.76 36	99.78 16

HPM-MVS		98.36 48	98.10 54	99.13 57	99.74 8	97.82 77	99.53 8	98.80 91	94.63 168	98.61 74	98.97 97	95.13 77	99.77 105	97.65 72	99.83 11	99.79 13
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HY-MVS	93.96 8	96.82 125	96.23 137	98.57 88	98.46 165	97.00 107	98.14 226	98.21 222	93.95 192	96.72 168	97.99 206	91.58 140	99.76 107	94.51 197	96.54 203	98.95 173

AdaColmap		97.15 113	96.70 117	98.48 99	99.16 107	96.69 121	98.01 239	98.89 48	94.44 176	96.83 162	98.68 136	90.69 164	99.76 107	94.36 200	99.29 114	98.98 169

ab-mvs		96.42 140	95.71 158	98.55 90	98.63 154	96.75 118	97.88 253	98.74 108	93.84 197	96.54 178	98.18 193	85.34 276	99.75 109	95.93 151	96.35 208	99.15 150

MAR-MVS		96.91 121	96.40 129	98.45 102	98.69 149	96.90 112	98.66 151	98.68 125	92.40 260	97.07 151	97.96 209	91.54 144	99.75 109	93.68 222	98.92 127	98.69 186
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
test_241102_ONE					99.71 21	99.24 5		98.87 58	97.62 12	99.73 1	99.39 18	97.53 7	99.74 111

HPM-MVS_fast		98.38 46	98.13 51	99.12 60	99.75 4	97.86 73	99.44 12	98.82 74	94.46 175	98.94 45	99.20 59	95.16 76	99.74 111	97.58 77	99.85 5	99.77 23

AllTest		95.24 202	94.65 207	96.99 195	99.25 90	93.21 274	98.59 159	98.18 228	91.36 291	93.52 270	98.77 126	84.67 286	99.72 113	89.70 309	97.87 171	98.02 214

TestCases				96.99 195	99.25 90	93.21 274		98.18 228	91.36 291	93.52 270	98.77 126	84.67 286	99.72 113	89.70 309	97.87 171	98.02 214

CDPH-MVS		97.94 68	97.49 80	99.28 38	99.47 52	98.44 36	97.91 248	98.67 133	92.57 253	98.77 60	98.85 115	95.93 42	99.72 113	95.56 167	99.69 58	99.68 63

test12				99.18 50	99.16 107	98.19 57		98.53 162		98.07 98		95.13 77	99.72 113		99.56 86	99.63 79

CNLPA		97.45 95	97.03 100	98.73 79	99.05 114	97.44 91	98.07 233	98.53 162	95.32 134	96.80 166	98.53 153	93.32 110	99.72 113	94.31 204	99.31 113	99.02 165

DPM-MVS		97.55 90	96.99 103	99.23 45	99.04 116	98.55 31	97.17 304	98.35 199	94.85 159	97.93 116	98.58 149	95.07 79	99.71 118	92.60 253	99.34 111	99.43 115

test_yl		97.22 107	96.78 112	98.54 92	98.73 142	96.60 125	98.45 179	98.31 205	94.70 161	98.02 104	98.42 166	90.80 161	99.70 119	96.81 120	96.79 195	99.34 121

DCV-MVSNet		97.22 107	96.78 112	98.54 92	98.73 142	96.60 125	98.45 179	98.31 205	94.70 161	98.02 104	98.42 166	90.80 161	99.70 119	96.81 120	96.79 195	99.34 121

TSAR-MVS + MP.		98.78 7	98.62 9	99.24 43	99.69 26	98.28 53	99.14 47	98.66 136	96.84 62	99.56 6	99.31 39	96.34 23	99.70 119	98.32 33	99.73 47	99.73 42
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test_prior3		98.22 59	97.90 64	99.19 46	99.31 74	98.22 55	97.80 260	98.84 69	96.12 94	97.89 119	98.69 134	95.96 40	99.70 119	96.89 111	99.60 74	99.65 73

test_prior				99.19 46	99.31 74	98.22 55		98.84 69					99.70 119			99.65 73

PVSNet	91.96 18	96.35 143	96.15 138	96.96 200	99.17 103	92.05 288	96.08 341	98.68 125	93.69 209	97.75 124	97.80 227	88.86 204	99.69 124	94.26 206	99.01 124	99.15 150

MG-MVS		97.81 73	97.60 71	98.44 103	99.12 111	95.97 157	97.75 264	98.78 99	96.89 61	98.46 79	99.22 54	93.90 105	99.68 125	94.81 187	99.52 94	99.67 67

TSAR-MVS + GP.		98.38 46	98.24 46	98.81 77	99.22 98	97.25 100	98.11 231	98.29 213	97.19 45	98.99 44	99.02 90	96.22 24	99.67 126	98.52 20	98.56 146	99.51 98

114514_t		96.93 120	96.27 134	98.92 72	99.50 45	97.63 83	98.85 106	98.90 46	84.80 356	97.77 122	99.11 76	92.84 114	99.66 127	94.85 184	99.77 30	99.47 107

DP-MVS Recon		97.86 71	97.46 82	99.06 64	99.53 39	98.35 48	98.33 195	98.89 48	92.62 250	98.05 99	98.94 106	95.34 65	99.65 128	96.04 148	99.42 105	99.19 143

PatchMatch-RL		96.59 132	96.03 144	98.27 114	99.31 74	96.51 131	97.91 248	99.06 23	93.72 205	96.92 159	98.06 200	88.50 213	99.65 128	91.77 277	99.00 125	98.66 190

VDDNet		95.36 195	94.53 212	97.86 140	98.10 198	95.13 196	98.85 106	97.75 279	90.46 312	98.36 87	99.39 18	73.27 361	99.64 130	97.98 44	96.58 201	98.81 180

MVS_111021_HR		98.47 40	98.34 33	98.88 75	99.22 98	97.32 93	97.91 248	99.58 3	97.20 44	98.33 90	99.00 95	95.99 39	99.64 130	98.05 43	99.76 36	99.69 57

DeepPCF-MVS	96.37 2	97.93 69	98.48 22	96.30 259	99.00 121	89.54 330	97.43 282	98.87 58	98.16 2	99.26 22	99.38 25	96.12 32	99.64 130	98.30 34	99.77 30	99.72 46

LFMVS		95.86 167	94.98 194	98.47 100	98.87 132	96.32 141	98.84 109	96.02 346	93.40 223	98.62 73	99.20 59	74.99 355	99.63 133	97.72 65	97.20 188	99.46 111

MVS		94.67 235	93.54 272	98.08 129	96.88 279	96.56 128	98.19 219	98.50 172	78.05 365	92.69 297	98.02 202	91.07 156	99.63 133	90.09 299	98.36 157	98.04 213

MVS_111021_LR		98.34 52	98.23 47	98.67 83	99.27 87	96.90 112	97.95 244	99.58 3	97.14 49	98.44 83	99.01 94	95.03 80	99.62 135	97.91 49	99.75 42	99.50 100

MSDG		95.93 163	95.30 180	97.83 142	98.90 129	95.36 186	96.83 328	98.37 196	91.32 295	94.43 229	98.73 130	90.27 171	99.60 136	90.05 302	98.82 135	98.52 197

thres600view7		95.49 184	94.77 201	97.67 158	98.98 125	95.02 199	98.85 106	96.90 332	95.38 129	96.63 171	96.90 299	84.29 291	99.59 137	88.65 322	96.33 209	98.40 201

1112_ss		96.63 129	96.00 145	98.50 96	98.56 158	96.37 138	98.18 223	98.10 245	92.92 242	94.84 212	98.43 164	92.14 128	99.58 138	94.35 201	96.51 204	99.56 93

dcpmvs_2		98.08 61	98.59 10	96.56 234	99.57 35	90.34 320	99.15 45	98.38 195	96.82 64	99.29 20	99.49 7	95.78 48	99.57 139	98.94 2	99.86 1	99.77 23

PAPM_NR		97.46 92	97.11 96	98.50 96	99.50 45	96.41 137	98.63 155	98.60 146	95.18 141	97.06 152	98.06 200	94.26 99	99.57 139	93.80 220	98.87 132	99.52 94

API-MVS		97.41 99	97.25 91	97.91 138	98.70 147	96.80 115	98.82 113	98.69 122	94.53 170	98.11 95	98.28 183	94.50 94	99.57 139	94.12 210	99.49 96	97.37 232

thres100view900		95.38 192	94.70 205	97.41 171	98.98 125	94.92 207	98.87 103	96.90 332	95.38 129	96.61 172	96.88 300	84.29 291	99.56 142	88.11 323	96.29 211	97.76 219

tfpn200view9		95.32 199	94.62 208	97.43 170	98.94 127	94.98 203	98.68 145	96.93 330	95.33 132	96.55 176	96.53 316	84.23 294	99.56 142	88.11 323	96.29 211	97.76 219

thres400		95.38 192	94.62 208	97.65 161	98.94 127	94.98 203	98.68 145	96.93 330	95.33 132	96.55 176	96.53 316	84.23 294	99.56 142	88.11 323	96.29 211	98.40 201

Test_1112_low_res		96.34 144	95.66 163	98.36 109	98.56 158	95.94 160	97.71 266	98.07 253	92.10 271	94.79 216	97.29 262	91.75 137	99.56 142	94.17 208	96.50 205	99.58 91

PAPR		96.84 124	96.24 136	98.65 84	98.72 146	96.92 111	97.36 289	98.57 154	93.33 225	96.67 169	97.57 245	94.30 98	99.56 142	91.05 288	98.59 144	99.47 107

XVG-OURS-SEG-HR		96.51 137	96.34 130	97.02 194	98.77 140	93.76 250	97.79 262	98.50 172	95.45 125	96.94 156	99.09 84	87.87 228	99.55 147	96.76 125	95.83 224	97.74 221

thres200		95.25 201	94.57 210	97.28 178	98.81 138	94.92 207	98.20 215	97.11 319	95.24 140	96.54 178	96.22 327	84.58 288	99.53 148	87.93 327	96.50 205	97.39 230

XVG-OURS		96.55 136	96.41 128	96.99 195	98.75 141	93.76 250	97.50 279	98.52 164	95.67 115	96.83 162	99.30 42	88.95 202	99.53 148	95.88 153	96.26 215	97.69 224

IB-MVS	91.98 17	93.27 289	91.97 300	97.19 182	97.47 239	93.41 266	97.09 308	95.99 347	93.32 226	92.47 306	95.73 336	78.06 339	99.53 148	94.59 195	82.98 350	98.62 193
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
test2506		94.44 251	93.91 248	96.04 267	99.02 118	88.99 340	99.06 63	79.47 383	96.96 58	98.36 87	99.26 47	77.21 346	99.52 151	96.78 124	99.04 121	99.59 87

ECVR-MVS		95.95 160	95.71 158	96.65 221	99.02 118	90.86 309	99.03 69	91.80 373	96.96 58	98.10 96	99.26 47	81.31 316	99.51 152	96.90 110	99.04 121	99.59 87

canonicalmvs		97.67 79	97.23 92	98.98 68	98.70 147	98.38 40	99.34 19	98.39 192	96.76 67	97.67 130	97.40 257	92.26 123	99.49 153	98.28 35	96.28 214	99.08 161

1314		96.25 149	95.73 154	97.79 146	97.13 265	95.55 179	98.19 219	98.59 148	93.47 220	92.03 315	97.82 225	91.33 149	99.49 153	94.62 192	98.44 152	98.32 206

RPSCF		94.87 224	95.40 168	93.26 335	98.89 130	82.06 369	98.33 195	98.06 258	90.30 317	96.56 174	99.26 47	87.09 242	99.49 153	93.82 219	96.32 210	98.24 207

OMC-MVS		97.55 90	97.34 88	98.20 120	99.33 69	95.92 164	98.28 206	98.59 148	95.52 122	97.97 111	99.10 78	93.28 111	99.49 153	95.09 180	98.88 130	99.19 143

test1111		95.94 162	95.78 152	96.41 251	98.99 124	90.12 322	99.04 66	92.45 372	96.99 57	98.03 102	99.27 46	81.40 315	99.48 157	96.87 117	99.04 121	99.63 79

alignmvs		97.56 89	97.07 99	99.01 65	98.66 151	98.37 46	98.83 110	98.06 258	96.74 68	98.00 110	97.65 237	90.80 161	99.48 157	98.37 31	96.56 202	99.19 143

tttt0517		96.07 153	95.51 167	97.78 147	98.41 168	94.84 209	99.28 25	94.33 364	94.26 180	97.64 134	98.64 141	84.05 298	99.47 159	95.34 171	97.60 182	99.03 164

thisisatest0530		96.01 156	95.36 173	97.97 135	98.38 169	95.52 180	98.88 100	94.19 366	94.04 185	97.64 134	98.31 181	83.82 305	99.46 160	95.29 175	97.70 179	98.93 174

thisisatest0515		95.61 183	94.89 198	97.76 149	98.15 195	95.15 195	96.77 329	94.41 362	92.95 241	97.18 146	97.43 255	84.78 284	99.45 161	94.63 190	97.73 178	98.68 187

mvs-test1		96.60 130	96.68 120	96.37 254	97.89 211	91.81 291	98.56 166	98.10 245	96.57 75	96.52 180	97.94 211	90.81 159	99.45 161	95.72 160	98.01 166	97.86 218

MSLP-MVS++		98.56 29	98.57 11	98.55 90	99.26 89	96.80 115	98.71 138	99.05 25	97.28 36	98.84 55	99.28 44	96.47 22	99.40 163	98.52 20	99.70 57	99.47 107

PVSNet_0	88.72 19	91.28 309	90.03 315	95.00 302	97.99 205	87.29 358	94.84 357	98.50 172	92.06 272	89.86 334	95.19 344	79.81 328	99.39 164	92.27 264	69.79 369	98.33 205

OPU-MVS				99.37 23	99.24 96	99.05 14	99.02 73				99.16 68	97.81 3	99.37 165	97.24 93	99.73 47	99.70 54

ETV-MVS		97.96 64	97.81 65	98.40 107	98.42 167	97.27 95	98.73 133	98.55 158	96.84 62	98.38 86	97.44 254	95.39 61	99.35 166	97.62 74	98.89 129	98.58 196

Vis-MVSNet		97.42 98	97.11 96	98.34 110	98.66 151	96.23 144	99.22 34	99.00 28	96.63 73	98.04 101	99.21 55	88.05 223	99.35 166	96.01 150	99.21 115	99.45 113
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EIA-MVS		97.75 75	97.58 72	98.27 114	98.38 169	96.44 134	99.01 75	98.60 146	95.88 105	97.26 143	97.53 248	94.97 81	99.33 168	97.38 90	99.20 116	99.05 163

lupinMVS		97.44 96	97.22 93	98.12 127	98.07 199	95.76 171	97.68 268	97.76 278	94.50 173	98.79 58	98.61 144	92.34 120	99.30 169	97.58 77	99.59 77	99.31 127

TAPA-MVS	93.98 7	95.35 196	94.56 211	97.74 151	99.13 110	94.83 211	98.33 195	98.64 141	86.62 345	96.29 187	98.61 144	94.00 103	99.29 170	80.00 360	99.41 106	99.09 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_Test		97.28 105	97.00 102	98.13 125	98.33 178	95.97 157	98.74 129	98.07 253	94.27 179	98.44 83	98.07 199	92.48 118	99.26 171	96.43 136	98.19 161	99.16 149

Effi-MVS+		97.12 114	96.69 118	98.39 108	98.19 189	96.72 120	97.37 287	98.43 186	93.71 206	97.65 133	98.02 202	92.20 127	99.25 172	96.87 117	97.79 174	99.19 143

diffmvs		97.58 87	97.40 86	98.13 125	98.32 180	95.81 170	98.06 234	98.37 196	96.20 90	98.74 62	98.89 111	91.31 150	99.25 172	98.16 37	98.52 147	99.34 121

tpmvs		94.60 238	94.36 224	95.33 293	97.46 240	88.60 345	96.88 324	97.68 281	91.29 297	93.80 262	96.42 320	88.58 208	99.24 174	91.06 286	96.04 222	98.17 210

casdiffmvs		97.63 82	97.41 85	98.28 113	98.33 178	96.14 148	98.82 113	98.32 203	96.38 85	97.95 112	99.21 55	91.23 152	99.23 175	98.12 38	98.37 155	99.48 105

jason		97.32 104	97.08 98	98.06 131	97.45 244	95.59 175	97.87 254	97.91 273	94.79 160	98.55 77	98.83 119	91.12 153	99.23 175	97.58 77	99.60 74	99.34 121
jason: jason.
EPP-MVSNet		97.46 92	97.28 90	97.99 134	98.64 153	95.38 185	99.33 22	98.31 205	93.61 216	97.19 145	99.07 87	94.05 101	99.23 175	96.89 111	98.43 154	99.37 120

PMMVS		96.60 130	96.33 131	97.41 171	97.90 210	93.93 245	97.35 290	98.41 188	92.84 245	97.76 123	97.45 253	91.10 155	99.20 178	96.26 141	97.91 169	99.11 155

gm-plane-assit					95.88 327	87.47 356			89.74 327		96.94 297		99.19 179	93.32 234

baseline2		95.11 209	94.52 213	96.87 207	96.65 292	93.56 258	98.27 208	94.10 368	93.45 221	92.02 316	97.43 255	87.45 239	99.19 179	93.88 217	97.41 186	97.87 217

baseline1		95.84 168	95.12 187	98.01 133	98.49 164	95.98 152	98.73 133	97.03 324	95.37 131	96.22 188	98.19 192	89.96 175	99.16 181	94.60 193	87.48 332	98.90 176

baseline		97.64 81	97.44 84	98.25 117	98.35 171	96.20 145	99.00 77	98.32 203	96.33 87	98.03 102	99.17 63	91.35 148	99.16 181	98.10 39	98.29 160	99.39 118

tpmrst		95.63 180	95.69 161	95.44 290	97.54 234	88.54 346	96.97 313	97.56 289	93.50 219	97.52 140	96.93 298	89.49 181	99.16 181	95.25 177	96.42 207	98.64 192

CS-MVS-test		98.49 37	98.50 17	98.46 101	99.20 101	97.05 105	99.64 4	98.50 172	97.45 25	98.88 53	99.14 72	95.25 72	99.15 184	98.83 5	99.56 86	99.20 139

Fast-Effi-MVS+		96.28 147	95.70 160	98.03 132	98.29 182	95.97 157	98.58 161	98.25 220	91.74 279	95.29 204	97.23 266	91.03 157	99.15 184	92.90 247	97.96 168	98.97 170

ACMP	93.49 10	95.34 197	94.98 194	96.43 250	97.67 223	93.48 263	98.73 133	98.44 183	94.94 157	92.53 302	98.53 153	84.50 290	99.14 186	95.48 170	94.00 241	96.66 289
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CS-MVS		98.44 42	98.49 20	98.31 112	99.08 113	96.73 119	99.67 3	98.47 178	97.17 46	98.94 45	99.10 78	95.73 49	99.13 187	98.71 7	99.49 96	99.09 157

tpm cat1		93.36 285	92.80 287	95.07 301	97.58 229	87.97 353	96.76 330	97.86 275	82.17 361	93.53 269	96.04 331	86.13 260	99.13 187	89.24 317	95.87 223	98.10 212

BH-RMVSNet		95.92 164	95.32 177	97.69 156	98.32 180	94.64 217	98.19 219	97.45 303	94.56 169	96.03 193	98.61 144	85.02 279	99.12 189	90.68 293	99.06 120	99.30 130

ACMM	93.85 9	95.69 178	95.38 172	96.61 227	97.61 227	93.84 248	98.91 92	98.44 183	95.25 138	94.28 236	98.47 160	86.04 264	99.12 189	95.50 169	93.95 243	96.87 262
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-ACMP-BASELINE		94.54 243	94.14 233	95.75 282	96.55 296	91.65 297	98.11 231	98.44 183	94.96 154	94.22 240	97.90 214	79.18 332	99.11 191	94.05 214	93.85 245	96.48 315

LPG-MVS_test		95.62 181	95.34 174	96.47 245	97.46 240	93.54 259	98.99 79	98.54 160	94.67 165	94.36 232	98.77 126	85.39 273	99.11 191	95.71 162	94.15 236	96.76 274

LGP-MVS_train				96.47 245	97.46 240	93.54 259		98.54 160	94.67 165	94.36 232	98.77 126	85.39 273	99.11 191	95.71 162	94.15 236	96.76 274

HyFIR lowres test		96.90 122	96.49 127	98.14 123	99.33 69	95.56 177	97.38 285	99.65 2	92.34 261	97.61 136	98.20 191	89.29 187	99.10 194	96.97 103	97.60 182	99.77 23

TDRefinement		91.06 312	89.68 317	95.21 295	85.35 374	91.49 300	98.51 174	97.07 321	91.47 287	88.83 344	97.84 221	77.31 345	99.09 195	92.79 250	77.98 362	95.04 350

ACMH	92.88 16	94.55 242	93.95 245	96.34 257	97.63 226	93.26 272	98.81 119	98.49 177	93.43 222	89.74 335	98.53 153	81.91 312	99.08 196	93.69 221	93.30 264	96.70 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		97.18 111	97.18 94	97.20 181	98.81 138	93.27 271	95.78 348	99.15 19	95.25 138	96.79 167	98.11 197	92.29 122	99.07 197	98.56 13	99.85 5	99.25 136

OPM-MVS		95.69 178	95.33 176	96.76 213	96.16 318	94.63 218	98.43 184	98.39 192	96.64 72	95.02 209	98.78 124	85.15 278	99.05 198	95.21 179	94.20 233	96.60 294
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MDTV_nov1_ep13			95.40 168		97.48 238	88.34 349	96.85 326	97.29 313	93.74 203	97.48 141	97.26 263	89.18 191	99.05 198	91.92 274	97.43 185

ACMH+	92.99 14	94.30 258	93.77 259	95.88 277	97.81 215	92.04 289	98.71 138	98.37 196	93.99 190	90.60 329	98.47 160	80.86 322	99.05 198	92.75 251	92.40 273	96.55 302

LTVRE_ROB	92.95 15	94.60 238	93.90 249	96.68 220	97.41 248	94.42 230	98.52 170	98.59 148	91.69 282	91.21 322	98.35 174	84.87 282	99.04 201	91.06 286	93.44 261	96.60 294
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
AUN-MVS		94.53 244	93.73 263	96.92 205	98.50 162	93.52 262	98.34 194	98.10 245	93.83 199	95.94 197	97.98 208	85.59 270	99.03 202	94.35 201	80.94 358	98.22 208

HQP_MVS		96.14 151	95.90 147	96.85 208	97.42 245	94.60 223	98.80 120	98.56 156	97.28 36	95.34 202	98.28 183	87.09 242	99.03 202	96.07 144	94.27 230	96.92 251

plane_prior5								98.56 156					99.03 202	96.07 144	94.27 230	96.92 251

hse-mvs2		95.71 175	95.30 180	96.93 202	98.50 162	93.53 261	98.36 192	98.10 245	97.48 20	98.67 67	97.99 206	89.76 177	99.02 205	97.95 46	80.91 359	98.22 208

dp		94.15 268	93.90 249	94.90 305	97.31 251	86.82 360	96.97 313	97.19 318	91.22 301	96.02 194	96.61 315	85.51 272	99.02 205	90.00 304	94.30 229	98.85 177

DROMVSNet		98.21 60	98.11 53	98.49 98	98.34 176	97.26 99	99.61 5	98.43 186	96.78 65	98.87 54	98.84 117	93.72 106	99.01 207	98.91 3	99.50 95	99.19 143

BH-untuned		95.95 160	95.72 155	96.65 221	98.55 160	92.26 284	98.23 210	97.79 277	93.73 204	94.62 219	98.01 204	88.97 201	99.00 208	93.04 242	98.51 148	98.68 187

GeoE		96.58 134	96.07 141	98.10 128	98.35 171	95.89 167	99.34 19	98.12 240	93.12 235	96.09 191	98.87 113	89.71 179	98.97 209	92.95 245	98.08 165	99.43 115

test-LLR		95.10 210	94.87 199	95.80 279	96.77 283	89.70 326	96.91 318	95.21 354	95.11 145	94.83 214	95.72 338	87.71 231	98.97 209	93.06 240	98.50 149	98.72 184

test-mter		94.08 274	93.51 273	95.80 279	96.77 283	89.70 326	96.91 318	95.21 354	92.89 243	94.83 214	95.72 338	77.69 341	98.97 209	93.06 240	98.50 149	98.72 184

CLD-MVS		95.62 181	95.34 174	96.46 248	97.52 237	93.75 252	97.27 297	98.46 179	95.53 121	94.42 230	98.00 205	86.21 259	98.97 209	96.25 142	94.37 228	96.66 289
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ADS-MVSNet		95.00 215	94.45 219	96.63 225	98.00 203	91.91 290	96.04 342	97.74 280	90.15 318	96.47 182	96.64 313	87.89 226	98.96 213	90.08 300	97.06 189	99.02 165

HQP4-MVS										94.45 225			98.96 213			96.87 262

TR-MVS		94.94 222	94.20 228	97.17 184	97.75 217	94.14 241	97.59 275	97.02 326	92.28 266	95.75 198	97.64 239	83.88 302	98.96 213	89.77 306	96.15 219	98.40 201

HQP-MVS		95.72 174	95.40 168	96.69 218	97.20 258	94.25 238	98.05 235	98.46 179	96.43 81	94.45 225	97.73 230	86.75 248	98.96 213	95.30 173	94.18 234	96.86 265

CostFormer		94.95 220	94.73 204	95.60 285	97.28 252	89.06 337	97.53 278	96.89 334	89.66 328	96.82 164	96.72 308	86.05 262	98.95 217	95.53 168	96.13 220	98.79 181

IS-MVSNet		97.22 107	96.88 107	98.25 117	98.85 135	96.36 139	99.19 41	97.97 266	95.39 128	97.23 144	98.99 96	91.11 154	98.93 218	94.60 193	98.59 144	99.47 107

TESTMET0.1,1		94.18 267	93.69 266	95.63 284	96.92 275	89.12 336	96.91 318	94.78 359	93.17 232	94.88 211	96.45 319	78.52 334	98.92 219	93.09 239	98.50 149	98.85 177

Effi-MVS+-dtu		96.29 145	96.56 123	95.51 286	97.89 211	90.22 321	98.80 120	98.10 245	96.57 75	96.45 184	96.66 310	90.81 159	98.91 220	95.72 160	97.99 167	97.40 229

test_post											31.83 379	88.83 205	98.91 220

VPA-MVSNet		95.75 172	95.11 188	97.69 156	97.24 254	97.27 95	98.94 89	99.23 13	95.13 143	95.51 201	97.32 260	85.73 267	98.91 220	97.33 92	89.55 307	96.89 259

PatchmatchNet		95.71 175	95.52 166	96.29 260	97.58 229	90.72 313	96.84 327	97.52 296	94.06 184	97.08 149	96.96 294	89.24 190	98.90 223	92.03 271	98.37 155	99.26 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
patchmatchnet-post											95.10 346	89.42 184	98.89 224

SCA		95.46 185	95.13 186	96.46 248	97.67 223	91.29 304	97.33 292	97.60 287	94.68 164	96.92 159	97.10 272	83.97 300	98.89 224	92.59 255	98.32 159	99.20 139

ITE_SJBPF				95.44 290	97.42 245	91.32 303		97.50 298	95.09 148	93.59 266	98.35 174	81.70 313	98.88 226	89.71 308	93.39 262	96.12 329

cascas		94.63 237	93.86 252	96.93 202	96.91 277	94.27 236	96.00 345	98.51 167	85.55 354	94.54 221	96.23 325	84.20 296	98.87 227	95.80 157	96.98 192	97.66 225

XXY-MVS		95.20 205	94.45 219	97.46 168	96.75 286	96.56 128	98.86 105	98.65 140	93.30 228	93.27 279	98.27 186	84.85 283	98.87 227	94.82 186	91.26 287	96.96 247

PAPM		94.95 220	94.00 241	97.78 147	97.04 269	95.65 174	96.03 344	98.25 220	91.23 300	94.19 242	97.80 227	91.27 151	98.86 229	82.61 354	97.61 181	98.84 179

BH-w/o		95.38 192	95.08 189	96.26 261	98.34 176	91.79 292	97.70 267	97.43 305	92.87 244	94.24 239	97.22 267	88.66 207	98.84 230	91.55 281	97.70 179	98.16 211

EPMVS		94.99 216	94.48 215	96.52 241	97.22 256	91.75 294	97.23 298	91.66 374	94.11 182	97.28 142	96.81 305	85.70 268	98.84 230	93.04 242	97.28 187	98.97 170

Patchmatch-test		94.42 252	93.68 267	96.63 225	97.60 228	91.76 293	94.83 358	97.49 300	89.45 331	94.14 244	97.10 272	88.99 197	98.83 232	85.37 343	98.13 163	99.29 132

USDC		93.33 288	92.71 289	95.21 295	96.83 282	90.83 311	96.91 318	97.50 298	93.84 197	90.72 327	98.14 195	77.69 341	98.82 233	89.51 313	93.21 266	95.97 333

TinyColmap		92.31 302	91.53 303	94.65 314	96.92 275	89.75 325	96.92 316	96.68 342	90.45 313	89.62 336	97.85 220	76.06 351	98.81 234	86.74 332	92.51 272	95.41 342

LF4IMVS		93.14 294	92.79 288	94.20 324	95.88 327	88.67 344	97.66 270	97.07 321	93.81 200	91.71 318	97.65 237	77.96 340	98.81 234	91.47 282	91.92 278	95.12 347

Fast-Effi-MVS+-dtu		95.87 166	95.85 149	95.91 274	97.74 220	91.74 295	98.69 144	98.15 236	95.56 120	94.92 210	97.68 236	88.98 200	98.79 236	93.19 237	97.78 175	97.20 236

JIA-IIPM		93.35 286	92.49 293	95.92 273	96.48 302	90.65 315	95.01 353	96.96 328	85.93 351	96.08 192	87.33 367	87.70 233	98.78 237	91.35 283	95.58 225	98.34 204

UniMVSNet_ETH3D		94.24 262	93.33 278	96.97 199	97.19 261	93.38 268	98.74 129	98.57 154	91.21 302	93.81 261	98.58 149	72.85 362	98.77 238	95.05 181	93.93 244	98.77 183

tpm2		94.19 265	93.76 261	95.46 289	97.23 255	89.04 338	97.31 294	96.85 338	87.08 344	96.21 189	96.79 306	83.75 306	98.74 239	92.43 263	96.23 217	98.59 194

D2MVS		95.18 206	95.08 189	95.48 287	97.10 267	92.07 287	98.30 203	99.13 20	94.02 187	92.90 290	96.73 307	89.48 182	98.73 240	94.48 198	93.60 255	95.65 340

test_post1							96.68 333				30.43 380	87.85 229	98.69 241	92.59 255

MS-PatchMatch		93.84 280	93.63 268	94.46 321	96.18 315	89.45 331	97.76 263	98.27 214	92.23 267	92.13 313	97.49 249	79.50 329	98.69 241	89.75 307	99.38 109	95.25 344

nrg030		96.28 147	95.72 155	97.96 137	96.90 278	98.15 61	99.39 13	98.31 205	95.47 124	94.42 230	98.35 174	92.09 130	98.69 241	97.50 86	89.05 315	97.04 239

Anonymous20231211		94.10 272	93.26 281	96.61 227	99.11 112	94.28 235	99.01 75	98.88 51	86.43 347	92.81 292	97.57 245	81.66 314	98.68 244	94.83 185	89.02 317	96.88 260

test_part1		94.82 225	93.82 254	97.82 144	98.84 136	97.82 77	99.03 69	98.81 80	92.31 265	92.51 304	97.89 216	81.96 311	98.67 245	94.80 188	88.24 324	96.98 244

VPNet		94.99 216	94.19 229	97.40 173	97.16 263	96.57 127	98.71 138	98.97 31	95.67 115	94.84 212	98.24 189	80.36 325	98.67 245	96.46 133	87.32 335	96.96 247

jajsoiax		95.45 187	95.03 191	96.73 214	95.42 342	94.63 218	99.14 47	98.52 164	95.74 110	93.22 280	98.36 173	83.87 303	98.65 247	96.95 106	94.04 239	96.91 256

mvs_tets		95.41 191	95.00 192	96.65 221	95.58 335	94.42 230	99.00 77	98.55 158	95.73 111	93.21 281	98.38 171	83.45 307	98.63 248	97.09 99	94.00 241	96.91 256

tfpnnormal		93.66 281	92.70 290	96.55 239	96.94 274	95.94 160	98.97 83	99.19 16	91.04 305	91.38 321	97.34 258	84.94 281	98.61 249	85.45 342	89.02 317	95.11 348

PS-MVSNAJss		96.43 139	96.26 135	96.92 205	95.84 329	95.08 198	99.16 44	98.50 172	95.87 106	93.84 260	98.34 178	94.51 91	98.61 249	96.88 114	93.45 260	97.06 238

CMPMVS	66.06 21	89.70 322	89.67 318	89.78 346	93.19 361	76.56 371	97.00 312	98.35 199	80.97 362	81.57 363	97.75 229	74.75 356	98.61 249	89.85 305	93.63 252	94.17 357
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
iter_conf_final		96.42 140	96.12 139	97.34 176	98.46 165	96.55 130	99.08 61	98.06 258	96.03 98	95.63 199	98.46 162	87.72 230	98.59 252	97.84 58	93.80 247	96.87 262

iter_conf05		96.13 152	95.79 151	97.15 185	98.16 194	95.99 151	98.88 100	97.98 264	95.91 102	95.58 200	98.46 162	85.53 271	98.59 252	97.88 53	93.75 248	96.86 265

OurMVSNet-221017-0		94.21 263	94.00 241	94.85 307	95.60 334	89.22 335	98.89 97	97.43 305	95.29 135	92.18 312	98.52 156	82.86 308	98.59 252	93.46 229	91.76 279	96.74 276

Vis-MVSNet (Re-imp)		96.87 123	96.55 124	97.83 142	98.73 142	95.46 182	99.20 39	98.30 211	94.96 154	96.60 173	98.87 113	90.05 173	98.59 252	93.67 224	98.60 143	99.46 111

V42		94.78 228	94.14 233	96.70 217	96.33 311	95.22 192	98.97 83	98.09 250	92.32 263	94.31 235	97.06 282	88.39 214	98.55 256	92.90 247	88.87 319	96.34 321

mvsmamba		96.57 135	96.32 132	97.32 177	96.60 293	96.43 135	99.54 7	97.98 264	96.49 78	95.20 206	98.64 141	90.82 158	98.55 256	97.97 45	93.65 251	96.98 244

EI-MVSNet		95.96 159	95.83 150	96.36 255	97.93 208	93.70 256	98.12 229	98.27 214	93.70 208	95.07 207	99.02 90	92.23 125	98.54 258	94.68 189	93.46 258	96.84 267

bld_raw_conf005		95.91 165	95.56 165	96.99 195	96.51 299	95.46 182	99.21 37	97.42 307	96.41 84	94.10 246	98.63 143	86.59 251	98.54 258	97.56 81	93.59 256	96.96 247

MVSTER		96.06 154	95.72 155	97.08 191	98.23 184	95.93 163	98.73 133	98.27 214	94.86 158	95.07 207	98.09 198	88.21 217	98.54 258	96.59 128	93.46 258	96.79 271

v7n		94.19 265	93.43 276	96.47 245	95.90 326	94.38 233	99.26 27	98.34 201	91.99 273	92.76 294	97.13 271	88.31 215	98.52 261	89.48 314	87.70 330	96.52 308

TAMVS		97.02 117	96.79 111	97.70 155	98.06 201	95.31 190	98.52 170	98.31 205	93.95 192	97.05 153	98.61 144	93.49 108	98.52 261	95.33 172	97.81 173	99.29 132

bld_raw_dy_0_64		95.74 173	95.31 179	97.03 193	96.35 309	95.76 171	99.12 52	97.37 310	95.97 100	94.70 218	98.48 158	85.80 266	98.49 263	96.55 130	93.48 257	96.84 267

v8		94.47 249	93.77 259	96.57 233	96.36 308	94.83 211	99.05 65	98.19 225	91.92 275	93.16 282	96.97 292	88.82 206	98.48 264	91.69 279	87.79 329	96.39 319

GA-MVS		94.81 226	94.03 237	97.14 186	97.15 264	93.86 247	96.76 330	97.58 288	94.00 189	94.76 217	97.04 285	80.91 320	98.48 264	91.79 276	96.25 216	99.09 157

UniMVSNet (Re)		95.78 171	95.19 184	97.58 164	96.99 272	97.47 89	98.79 124	99.18 17	95.60 118	93.92 255	97.04 285	91.68 138	98.48 264	95.80 157	87.66 331	96.79 271

test_low_dy_conf_001		96.06 154	95.86 148	96.69 218	96.39 306	94.58 225	99.47 9	98.26 218	95.68 113	95.23 205	98.73 130	88.90 203	98.47 267	96.43 136	93.62 253	97.02 241

PC_three_1452									95.08 149	99.60 5	99.16 68	97.86 2	98.47 267	97.52 85	99.72 54	99.74 37

mvs_anonymous		96.70 128	96.53 126	97.18 183	98.19 189	93.78 249	98.31 201	98.19 225	94.01 188	94.47 224	98.27 186	92.08 131	98.46 269	97.39 89	97.91 169	99.31 127

v144192		94.39 254	93.70 265	96.48 244	96.06 321	94.35 234	98.58 161	98.16 235	91.45 288	94.33 234	97.02 287	87.50 237	98.45 270	91.08 285	89.11 314	96.63 291

v2v482		94.69 230	94.03 237	96.65 221	96.17 316	94.79 214	98.67 148	98.08 251	92.72 247	94.00 252	97.16 270	87.69 234	98.45 270	92.91 246	88.87 319	96.72 279

FIs		96.51 137	96.12 139	97.67 158	97.13 265	97.54 87	99.36 16	99.22 15	95.89 103	94.03 251	98.35 174	91.98 133	98.44 272	96.40 138	92.76 270	97.01 242

v1192		94.32 257	93.58 270	96.53 240	96.10 319	94.45 228	98.50 175	98.17 233	91.54 286	94.19 242	97.06 282	86.95 246	98.43 273	90.14 298	89.57 305	96.70 283

MVP-Stereo		94.28 261	93.92 246	95.35 292	94.95 346	92.60 282	97.97 243	97.65 283	91.61 285	90.68 328	97.09 276	86.32 258	98.42 274	89.70 309	99.34 111	95.02 351
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1921920		94.20 264	93.47 275	96.40 253	95.98 324	94.08 242	98.52 170	98.15 236	91.33 294	94.25 238	97.20 269	86.41 256	98.42 274	90.04 303	89.39 311	96.69 288

RRT_MVS		95.98 158	95.78 152	96.56 234	96.48 302	94.22 240	99.57 6	97.92 271	95.89 103	93.95 253	98.70 133	89.27 188	98.42 274	97.23 94	93.02 267	97.04 239

v1240		94.06 276	93.29 280	96.34 257	96.03 323	93.90 246	98.44 182	98.17 233	91.18 303	94.13 245	97.01 289	86.05 262	98.42 274	89.13 319	89.50 309	96.70 283

lessismore_v0				94.45 322	94.93 347	88.44 348		91.03 375		86.77 352	97.64 239	76.23 350	98.42 274	90.31 297	85.64 348	96.51 311

EPNet_dtu		95.21 204	94.95 196	95.99 269	96.17 316	90.45 318	98.16 225	97.27 315	96.77 66	93.14 285	98.33 179	90.34 169	98.42 274	85.57 340	98.81 136	99.09 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EG-PatchMatch MVS		91.13 311	90.12 314	94.17 326	94.73 351	89.00 339	98.13 228	97.81 276	89.22 334	85.32 358	96.46 318	67.71 366	98.42 274	87.89 328	93.82 246	95.08 349

CDS-MVSNet		96.99 118	96.69 118	97.90 139	98.05 202	95.98 152	98.20 215	98.33 202	93.67 213	96.95 155	98.49 157	93.54 107	98.42 274	95.24 178	97.74 177	99.31 127
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
anonymousdsp		95.42 189	94.91 197	96.94 201	95.10 344	95.90 166	99.14 47	98.41 188	93.75 201	93.16 282	97.46 251	87.50 237	98.41 282	95.63 166	94.03 240	96.50 313

v1144		94.59 240	93.92 246	96.60 229	96.21 313	94.78 215	98.59 159	98.14 238	91.86 278	94.21 241	97.02 287	87.97 224	98.41 282	91.72 278	89.57 305	96.61 293

pm-mvs1		93.94 279	93.06 283	96.59 230	96.49 301	95.16 193	98.95 87	98.03 261	92.32 263	91.08 324	97.84 221	84.54 289	98.41 282	92.16 265	86.13 347	96.19 328

v10		94.29 259	93.55 271	96.51 242	96.39 306	94.80 213	98.99 79	98.19 225	91.35 293	93.02 288	96.99 290	88.09 221	98.41 282	90.50 295	88.41 323	96.33 323

MVSFormer		97.57 88	97.49 80	97.84 141	98.07 199	95.76 171	99.47 9	98.40 190	94.98 152	98.79 58	98.83 119	92.34 120	98.41 282	96.91 107	99.59 77	99.34 121

test_djsdf		96.00 157	95.69 161	96.93 202	95.72 331	95.49 181	99.47 9	98.40 190	94.98 152	94.58 220	97.86 218	89.16 192	98.41 282	96.91 107	94.12 238	96.88 260

gg-mvs-nofinetune		92.21 303	90.58 310	97.13 187	96.75 286	95.09 197	95.85 346	89.40 377	85.43 355	94.50 223	81.98 370	80.80 323	98.40 288	92.16 265	98.33 158	97.88 216

pmmvs6		91.77 305	90.63 309	95.17 297	94.69 352	91.24 305	98.67 148	97.92 271	86.14 349	89.62 336	97.56 247	75.79 352	98.34 289	90.75 292	84.56 349	95.94 334

MVS-HIRNet		89.46 326	88.40 325	92.64 338	97.58 229	82.15 368	94.16 363	93.05 371	75.73 367	90.90 325	82.52 369	79.42 330	98.33 290	83.53 352	98.68 138	97.43 227

FC-MVSNet-test		96.42 140	96.05 142	97.53 167	96.95 273	97.27 95	99.36 16	99.23 13	95.83 107	93.93 254	98.37 172	92.00 132	98.32 291	96.02 149	92.72 271	97.00 243

v148		94.29 259	93.76 261	95.91 274	96.10 319	92.93 279	98.58 161	97.97 266	92.59 252	93.47 274	96.95 296	88.53 212	98.32 291	92.56 257	87.06 338	96.49 314

UniMVSNet_NR-MVSNet		95.71 175	95.15 185	97.40 173	96.84 281	96.97 108	98.74 129	99.24 11	95.16 142	93.88 257	97.72 232	91.68 138	98.31 293	95.81 155	87.25 336	96.92 251

DU-MVS		95.42 189	94.76 202	97.40 173	96.53 297	96.97 108	98.66 151	98.99 30	95.43 126	93.88 257	97.69 233	88.57 209	98.31 293	95.81 155	87.25 336	96.92 251

miper_enhance_ethall		95.10 210	94.75 203	96.12 266	97.53 236	93.73 254	96.61 335	98.08 251	92.20 270	93.89 256	96.65 312	92.44 119	98.30 295	94.21 207	91.16 288	96.34 321

WR-MVS		95.15 207	94.46 217	97.22 180	96.67 291	96.45 133	98.21 212	98.81 80	94.15 181	93.16 282	97.69 233	87.51 235	98.30 295	95.29 175	88.62 321	96.90 258

tpm		94.13 269	93.80 256	95.12 298	96.50 300	87.91 354	97.44 280	95.89 351	92.62 250	96.37 186	96.30 322	84.13 297	98.30 295	93.24 235	91.66 282	99.14 152

OpenMVS_ROB	86.42 20	89.00 327	87.43 332	93.69 328	93.08 362	89.42 332	97.91 248	96.89 334	78.58 364	85.86 355	94.69 349	69.48 364	98.29 298	77.13 367	93.29 265	93.36 364

cl22		94.68 232	94.19 229	96.13 265	98.11 197	93.60 257	96.94 315	98.31 205	92.43 258	93.32 278	96.87 302	86.51 252	98.28 299	94.10 212	91.16 288	96.51 311

SixPastTwentyTwo		93.34 287	92.86 286	94.75 311	95.67 332	89.41 333	98.75 126	96.67 343	93.89 194	90.15 333	98.25 188	80.87 321	98.27 300	90.90 289	90.64 293	96.57 298

WR-MVS_H		95.05 213	94.46 217	96.81 211	96.86 280	95.82 169	99.24 29	99.24 11	93.87 196	92.53 302	96.84 304	90.37 168	98.24 301	93.24 235	87.93 328	96.38 320

pmmvs4		94.69 230	93.99 243	96.81 211	95.74 330	95.94 160	97.40 283	97.67 282	90.42 314	93.37 276	97.59 243	89.08 195	98.20 302	92.97 244	91.67 281	96.30 325

NR-MVSNet		94.98 218	94.16 231	97.44 169	96.53 297	97.22 101	98.74 129	98.95 35	94.96 154	89.25 340	97.69 233	89.32 186	98.18 303	94.59 195	87.40 334	96.92 251

eth_miper_zixun_eth		94.68 232	94.41 222	95.47 288	97.64 225	91.71 296	96.73 332	98.07 253	92.71 248	93.64 265	97.21 268	90.54 166	98.17 304	93.38 230	89.76 302	96.54 303

miper_ehance_all_eth		95.01 214	94.69 206	95.97 271	97.70 222	93.31 270	97.02 311	98.07 253	92.23 267	93.51 272	96.96 294	91.85 135	98.15 305	93.68 222	91.16 288	96.44 318

Baseline_NR-MVSNet		94.35 255	93.81 255	95.96 272	96.20 314	94.05 243	98.61 158	96.67 343	91.44 289	93.85 259	97.60 242	88.57 209	98.14 306	94.39 199	86.93 339	95.68 339

cl____		94.51 246	94.01 240	96.02 268	97.58 229	93.40 267	97.05 309	97.96 268	91.73 281	92.76 294	97.08 278	89.06 196	98.13 307	92.61 252	90.29 297	96.52 308

CP-MVSNet		94.94 222	94.30 225	96.83 209	96.72 288	95.56 177	99.11 54	98.95 35	93.89 194	92.42 308	97.90 214	87.19 241	98.12 308	94.32 203	88.21 325	96.82 270

PS-CasMVS		94.67 235	93.99 243	96.71 215	96.68 290	95.26 191	99.13 50	99.03 26	93.68 211	92.33 309	97.95 210	85.35 275	98.10 309	93.59 226	88.16 327	96.79 271

IterMVS-LS		95.46 185	95.21 183	96.22 262	98.12 196	93.72 255	98.32 200	98.13 239	93.71 206	94.26 237	97.31 261	92.24 124	98.10 309	94.63 190	90.12 298	96.84 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs5		93.65 283	92.97 285	95.68 283	95.49 338	92.37 283	98.20 215	97.28 314	89.66 328	92.58 300	97.26 263	82.14 310	98.09 311	93.18 238	90.95 291	96.58 296

TransMVSNet (Re)		92.67 299	91.51 304	96.15 263	96.58 295	94.65 216	98.90 93	96.73 339	90.86 307	89.46 339	97.86 218	85.62 269	98.09 311	86.45 334	81.12 356	95.71 338

DIV-MVS_self_test		94.52 245	94.03 237	95.99 269	97.57 233	93.38 268	97.05 309	97.94 269	91.74 279	92.81 292	97.10 272	89.12 193	98.07 313	92.60 253	90.30 296	96.53 305

MVS_0304		92.81 297	92.01 299	95.23 294	97.46 240	91.33 302	98.17 224	98.81 80	91.13 304	93.80 262	95.68 341	66.08 369	98.06 314	90.79 290	96.13 220	96.32 324

GG-mvs-BLEND				96.59 230	96.34 310	94.98 203	96.51 338	88.58 378		93.10 287	94.34 354	80.34 326	98.05 315	89.53 312	96.99 191	96.74 276

TranMVSNet+NR-MVSNet		95.14 208	94.48 215	97.11 189	96.45 304	96.36 139	99.03 69	99.03 26	95.04 150	93.58 267	97.93 212	88.27 216	98.03 316	94.13 209	86.90 341	96.95 250

c3_l		94.79 227	94.43 221	95.89 276	97.75 217	93.12 277	97.16 305	98.03 261	92.23 267	93.46 275	97.05 284	91.39 146	98.01 317	93.58 227	89.21 313	96.53 305

FMVSNet3		94.97 219	94.26 226	97.11 189	98.18 191	96.62 122	98.56 166	98.26 218	93.67 213	94.09 247	97.10 272	84.25 293	98.01 317	92.08 267	92.14 274	96.70 283

FMVSNet2		94.47 249	93.61 269	97.04 192	98.21 186	96.43 135	98.79 124	98.27 214	92.46 254	93.50 273	97.09 276	81.16 317	98.00 319	91.09 284	91.93 277	96.70 283

test_0402		91.32 308	90.27 313	94.48 319	96.60 293	91.12 306	98.50 175	97.22 317	86.10 350	88.30 346	96.98 291	77.65 343	97.99 320	78.13 366	92.94 269	94.34 355

GBi-Net		94.49 247	93.80 256	96.56 234	98.21 186	95.00 200	98.82 113	98.18 228	92.46 254	94.09 247	97.07 279	81.16 317	97.95 321	92.08 267	92.14 274	96.72 279

test1		94.49 247	93.80 256	96.56 234	98.21 186	95.00 200	98.82 113	98.18 228	92.46 254	94.09 247	97.07 279	81.16 317	97.95 321	92.08 267	92.14 274	96.72 279

FMVSNet1		93.19 293	92.07 298	96.56 234	97.54 234	95.00 200	98.82 113	98.18 228	90.38 315	92.27 310	97.07 279	73.68 360	97.95 321	89.36 316	91.30 285	96.72 279

our_test_3		93.65 283	93.30 279	94.69 312	95.45 340	89.68 328	96.91 318	97.65 283	91.97 274	91.66 319	96.88 300	89.67 180	97.93 324	88.02 326	91.49 283	96.48 315

ambc				89.49 347	86.66 372	75.78 372	92.66 365	96.72 340		86.55 353	92.50 361	46.01 374	97.90 325	90.32 296	82.09 351	94.80 354

PEN-MVS		94.42 252	93.73 263	96.49 243	96.28 312	94.84 209	99.17 43	99.00 28	93.51 218	92.23 311	97.83 224	86.10 261	97.90 325	92.55 258	86.92 340	96.74 276

Patchmtry		93.22 291	92.35 295	95.84 278	96.77 283	93.09 278	94.66 359	97.56 289	87.37 343	92.90 290	96.24 323	88.15 219	97.90 325	87.37 330	90.10 299	96.53 305

PatchT		93.06 295	91.97 300	96.35 256	96.69 289	92.67 281	94.48 360	97.08 320	86.62 345	97.08 149	92.23 362	87.94 225	97.90 325	78.89 364	96.69 197	98.49 198

CR-MVSNet		94.76 229	94.15 232	96.59 230	97.00 270	93.43 264	94.96 354	97.56 289	92.46 254	96.93 157	96.24 323	88.15 219	97.88 329	87.38 329	96.65 199	98.46 199

ppachtmachnet_test		93.22 291	92.63 291	94.97 303	95.45 340	90.84 310	96.88 324	97.88 274	90.60 309	92.08 314	97.26 263	88.08 222	97.86 330	85.12 344	90.33 295	96.22 326

miper_lstm_enhance		94.33 256	94.07 236	95.11 299	97.75 217	90.97 308	97.22 299	98.03 261	91.67 283	92.76 294	96.97 292	90.03 174	97.78 331	92.51 260	89.64 304	96.56 300

N_pmnet		87.12 331	87.77 330	85.17 351	95.46 339	61.92 378	97.37 287	70.66 384	85.83 352	88.73 345	96.04 331	85.33 277	97.76 332	80.02 359	90.48 294	95.84 335

LCM-MVSNet-Re		95.22 203	95.32 177	94.91 304	98.18 191	87.85 355	98.75 126	95.66 352	95.11 145	88.96 341	96.85 303	90.26 172	97.65 333	95.65 165	98.44 152	99.22 138

K. test v3		92.55 300	91.91 302	94.48 319	95.64 333	89.24 334	99.07 62	94.88 358	94.04 185	86.78 351	97.59 243	77.64 344	97.64 334	92.08 267	89.43 310	96.57 298

SD-MVS		98.64 15	98.68 7	98.53 94	99.33 69	98.36 47	98.90 93	98.85 68	97.28 36	99.72 3	99.39 18	96.63 19	97.60 335	98.17 36	99.85 5	99.64 76
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
DTE-MVSNet		93.98 278	93.26 281	96.14 264	96.06 321	94.39 232	99.20 39	98.86 64	93.06 236	91.78 317	97.81 226	85.87 265	97.58 336	90.53 294	86.17 345	96.46 317

ADS-MVSNet2		94.58 241	94.40 223	95.11 299	98.00 203	88.74 343	96.04 342	97.30 312	90.15 318	96.47 182	96.64 313	87.89 226	97.56 337	90.08 300	97.06 189	99.02 165

ET-MVSNet_ETH3D		94.13 269	92.98 284	97.58 164	98.22 185	96.20 145	97.31 294	95.37 353	94.53 170	79.56 365	97.63 241	86.51 252	97.53 338	96.91 107	90.74 292	99.02 165

CVMVSNet		95.43 188	96.04 143	93.57 329	97.93 208	83.62 364	98.12 229	98.59 148	95.68 113	96.56 174	99.02 90	87.51 235	97.51 339	93.56 228	97.44 184	99.60 85

IterMVS-SCA-FT		94.11 271	93.87 251	94.85 307	97.98 207	90.56 317	97.18 302	98.11 243	93.75 201	92.58 300	97.48 250	83.97 300	97.41 340	92.48 262	91.30 285	96.58 296

IterMVS		94.09 273	93.85 253	94.80 310	97.99 205	90.35 319	97.18 302	98.12 240	93.68 211	92.46 307	97.34 258	84.05 298	97.41 340	92.51 260	91.33 284	96.62 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UnsupCasMVSNet_bld		87.17 330	85.12 334	93.31 334	91.94 365	88.77 342	94.92 356	98.30 211	84.30 358	82.30 362	90.04 364	63.96 371	97.25 342	85.85 339	74.47 368	93.93 362

MIMVSNet		93.26 290	92.21 297	96.41 251	97.73 221	93.13 276	95.65 349	97.03 324	91.27 299	94.04 250	96.06 330	75.33 353	97.19 343	86.56 333	96.23 217	98.92 175

new_pmnet		90.06 320	89.00 324	93.22 336	94.18 353	88.32 350	96.42 340	96.89 334	86.19 348	85.67 357	93.62 356	77.18 347	97.10 344	81.61 356	89.29 312	94.23 356

testgi		93.06 295	92.45 294	94.88 306	96.43 305	89.90 323	98.75 126	97.54 295	95.60 118	91.63 320	97.91 213	74.46 358	97.02 345	86.10 336	93.67 249	97.72 223

Anonymous20240521		91.18 310	90.44 311	93.42 330	93.70 359	88.47 347	98.94 89	97.56 289	88.46 338	89.56 338	95.08 347	77.15 348	96.97 346	83.92 350	89.55 307	94.82 353

test0.0.03 1		94.08 274	93.51 273	95.80 279	95.53 337	92.89 280	97.38 285	95.97 348	95.11 145	92.51 304	96.66 310	87.71 231	96.94 347	87.03 331	93.67 249	97.57 226

KD-MVS_2432*1600		89.61 324	87.96 328	94.54 316	94.06 356	91.59 298	95.59 350	97.63 285	89.87 324	88.95 342	94.38 352	78.28 336	96.82 348	84.83 345	68.05 370	95.21 345

miper_refine_blended		89.61 324	87.96 328	94.54 316	94.06 356	91.59 298	95.59 350	97.63 285	89.87 324	88.95 342	94.38 352	78.28 336	96.82 348	84.83 345	68.05 370	95.21 345

pmmvs-eth3d		90.36 318	89.05 323	94.32 323	91.10 368	92.12 285	97.63 274	96.95 329	88.86 336	84.91 359	93.13 358	78.32 335	96.74 350	88.70 321	81.81 354	94.09 359

PM-MVS		87.77 329	86.55 333	91.40 345	91.03 369	83.36 366	96.92 316	95.18 356	91.28 298	86.48 354	93.42 357	53.27 373	96.74 350	89.43 315	81.97 353	94.11 358

UnsupCasMVSNet_eth		90.99 313	89.92 316	94.19 325	94.08 355	89.83 324	97.13 307	98.67 133	93.69 209	85.83 356	96.19 328	75.15 354	96.74 350	89.14 318	79.41 360	96.00 332

MDA-MVSNet_test_wron		90.71 315	89.38 320	94.68 313	94.83 348	90.78 312	97.19 301	97.46 301	87.60 341	72.41 370	95.72 338	86.51 252	96.71 353	85.92 338	86.80 342	96.56 300

YYNet1		90.70 316	89.39 319	94.62 315	94.79 350	90.65 315	97.20 300	97.46 301	87.54 342	72.54 369	95.74 334	86.51 252	96.66 354	86.00 337	86.76 343	96.54 303

MDA-MVSNet-bldmvs		89.97 321	88.35 326	94.83 309	95.21 343	91.34 301	97.64 271	97.51 297	88.36 339	71.17 371	96.13 329	79.22 331	96.63 355	83.65 351	86.27 344	96.52 308

Anonymous20231206		91.66 306	91.10 306	93.33 333	94.02 358	87.35 357	98.58 161	97.26 316	90.48 311	90.16 332	96.31 321	83.83 304	96.53 356	79.36 362	89.90 301	96.12 329

Patchmatch-RL test		91.49 307	90.85 308	93.41 331	91.37 367	84.40 362	92.81 364	95.93 350	91.87 277	87.25 349	94.87 348	88.99 197	96.53 356	92.54 259	82.00 352	99.30 130

EU-MVSNet		93.66 281	94.14 233	92.25 342	95.96 325	83.38 365	98.52 170	98.12 240	94.69 163	92.61 299	98.13 196	87.36 240	96.39 358	91.82 275	90.00 300	96.98 244

EGC-MVSNET		75.22 338	69.54 341	92.28 341	94.81 349	89.58 329	97.64 271	96.50 345	1.82 381	5.57 382	95.74 334	68.21 365	96.26 359	73.80 370	91.71 280	90.99 366

KD-MVS_self_test		90.38 317	89.38 320	93.40 332	92.85 363	88.94 341	97.95 244	97.94 269	90.35 316	90.25 331	93.96 355	79.82 327	95.94 360	84.62 349	76.69 364	95.33 343

DSMNet-mixed		92.52 301	92.58 292	92.33 340	94.15 354	82.65 367	98.30 203	94.26 365	89.08 335	92.65 298	95.73 336	85.01 280	95.76 361	86.24 335	97.76 176	98.59 194

DeepMVS_CX				86.78 348	97.09 268	72.30 374		95.17 357	75.92 366	84.34 360	95.19 344	70.58 363	95.35 362	79.98 361	89.04 316	92.68 365

CL-MVSNet_self_test		90.11 319	89.14 322	93.02 337	91.86 366	88.23 351	96.51 338	98.07 253	90.49 310	90.49 330	94.41 350	84.75 285	95.34 363	80.79 358	74.95 366	95.50 341

FMVSNet5		91.81 304	90.92 307	94.49 318	97.21 257	92.09 286	98.00 241	97.55 294	89.31 333	90.86 326	95.61 342	74.48 357	95.32 364	85.57 340	89.70 303	96.07 331

pmmvs3		86.67 332	84.86 335	92.11 343	88.16 371	87.19 359	96.63 334	94.75 360	79.88 363	87.22 350	92.75 360	66.56 368	95.20 365	81.24 357	76.56 365	93.96 361

new-patchmatchnet		88.50 328	87.45 331	91.67 344	90.31 370	85.89 361	97.16 305	97.33 311	89.47 330	83.63 361	92.77 359	76.38 349	95.06 366	82.70 353	77.29 363	94.06 360

test_method		79.03 333	78.17 336	81.63 353	86.06 373	54.40 383	82.75 372	96.89 334	39.54 376	80.98 364	95.57 343	58.37 372	94.73 367	84.74 348	78.61 361	95.75 337

MIMVSNet1		89.67 323	88.28 327	93.82 327	92.81 364	91.08 307	98.01 239	97.45 303	87.95 340	87.90 348	95.87 333	67.63 367	94.56 368	78.73 365	88.18 326	95.83 336

test20.03		90.89 314	90.38 312	92.43 339	93.48 360	88.14 352	98.33 195	97.56 289	93.40 223	87.96 347	96.71 309	80.69 324	94.13 369	79.15 363	86.17 345	95.01 352

Gipuma		78.40 335	76.75 338	83.38 352	95.54 336	80.43 370	79.42 373	97.40 308	64.67 370	73.46 368	80.82 371	45.65 375	93.14 370	66.32 372	87.43 333	76.56 373
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		78.70 334	76.24 339	86.08 349	77.26 380	71.99 375	94.34 361	96.72 340	61.62 371	76.53 366	89.33 365	33.91 380	92.78 371	81.85 355	74.60 367	93.46 363

PMMVS2		77.95 336	75.44 340	85.46 350	82.54 375	74.95 373	94.23 362	93.08 370	72.80 368	74.68 367	87.38 366	36.36 379	91.56 372	73.95 369	63.94 372	89.87 367

PMVS	61.03 23	65.95 341	63.57 345	73.09 358	57.90 383	51.22 384	85.05 371	93.93 369	54.45 372	44.32 378	83.57 368	13.22 382	89.15 373	58.68 374	81.00 357	78.91 372
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		77.62 337	77.14 337	79.05 355	79.25 378	60.97 379	95.79 347	95.94 349	65.96 369	67.93 372	94.40 351	37.73 378	88.88 374	68.83 371	88.46 322	87.29 368

ANet_high		69.08 339	65.37 343	80.22 354	65.99 382	71.96 376	90.91 368	90.09 376	82.62 359	49.93 377	78.39 372	29.36 381	81.75 375	62.49 373	38.52 376	86.95 370

MVE	62.14 22	63.28 344	59.38 347	74.99 356	74.33 381	65.47 377	85.55 370	80.50 382	52.02 374	51.10 376	75.00 375	10.91 385	80.50 376	51.60 375	53.40 373	78.99 371
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		64.94 342	64.25 344	67.02 359	82.28 376	59.36 381	91.83 367	85.63 379	52.69 373	60.22 374	77.28 373	41.06 377	80.12 377	46.15 376	41.14 374	61.57 375

EMVS		64.07 343	63.26 346	66.53 360	81.73 377	58.81 382	91.85 366	84.75 380	51.93 375	59.09 375	75.13 374	43.32 376	79.09 378	42.03 377	39.47 375	61.69 374

tmp_tt		68.90 340	66.97 342	74.68 357	50.78 384	59.95 380	87.13 369	83.47 381	38.80 377	62.21 373	96.23 325	64.70 370	76.91 379	88.91 320	30.49 377	87.19 369

wuyk23d		30.17 345	30.18 349	30.16 361	78.61 379	43.29 385	66.79 374	14.21 385	17.31 378	14.82 381	11.93 381	11.55 384	41.43 380	37.08 378	19.30 378	5.76 378

test123		20.95 348	23.72 351	12.64 362	13.54 386	8.19 386	96.55 337	6.13 387	7.48 380	16.74 380	37.98 378	12.97 383	6.05 381	16.69 379	5.43 380	23.68 376

testmvs		21.48 347	24.95 350	11.09 363	14.89 385	6.47 387	96.56 336	9.87 386	7.55 379	17.93 379	39.02 377	9.43 386	5.90 382	16.56 380	12.72 379	20.91 377

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

cdsmvs_eth3d_5k		23.98 346	31.98 348	0.00 364	0.00 387	0.00 388	0.00 375	98.59 148	0.00 382	0.00 383	98.61 144	90.60 165	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		7.88 350	10.50 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	94.51 91	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		8.20 349	10.94 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	98.43 164	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

FOURS1					99.82 1	98.66 26	99.69 1	98.95 35	97.46 23	99.39 15

test_one_0601					99.66 28	99.25 2		98.86 64	97.55 16	99.20 26	99.47 10	97.57 6

eth-test2					0.00 387

eth-test					0.00 387

RE-MVS-def			98.34 33		99.49 49	97.86 73	99.11 54	98.80 91	96.49 78	99.17 30	99.35 32	95.29 69		97.72 65	99.65 64	99.71 50

IU-MVS					99.71 21	99.23 7		98.64 141	95.28 136	99.63 4				98.35 32	99.81 12	99.83 7

save fliter					99.46 55	98.38 40	98.21 212	98.71 118	97.95 3

test0726					99.72 13	99.25 2	99.06 63	98.88 51	97.62 12	99.56 6	99.50 5	97.42 9

GSMVS																99.20 139

test_part2					99.63 31	99.18 10				99.27 21

sam_mvs1												89.45 183				99.20 139

sam_mvs												88.99 197

MTGPA								98.74 108

MTMP							98.89 97	94.14 367

test9_res														96.39 139	99.57 81	99.69 57

agg_prior2														95.87 154	99.57 81	99.68 63

test_prior4						98.01 67	97.86 255

test_prior2							97.80 260		96.12 94	97.89 119	98.69 134	95.96 40		96.89 111	99.60 74

新几何2							97.64 271

旧先验1					99.29 82	97.48 88		98.70 121			99.09 84	95.56 53			99.47 99	99.61 82

原ACMM2							97.67 269

test222					99.23 97	97.17 103	97.40 283	98.66 136	88.68 337	98.05 99	98.96 103	94.14 100			99.53 92	99.61 82

segment_acmp												96.85 14

testdata1							97.32 293		96.34 86

plane_prior7					97.42 245	94.63 218

plane_prior6					97.35 250	94.61 221						87.09 242

plane_prior4											98.28 183

plane_prior3						94.61 221			97.02 55	95.34 202

plane_prior2							98.80 120		97.28 36

plane_prior1					97.37 249

plane_prior						94.60 223	98.44 182		96.74 68						94.22 232

n2								0.00 388

nn								0.00 388

door-mid								94.37 363

test11								98.66 136

door								94.64 361

HQP5-MVS						94.25 238

HQP-NCC					97.20 258		98.05 235		96.43 81	94.45 225

ACMP_Plane					97.20 258		98.05 235		96.43 81	94.45 225

BP-MVS														95.30 173

HQP3-MVS								98.46 179							94.18 234

HQP2-MVS												86.75 248

NP-MVS					97.28 252	94.51 227					97.73 230

MDTV_nov1_ep13_2view						84.26 363	96.89 323		90.97 306	97.90 118		89.89 176		93.91 216		99.18 148

ACMMP++_ref															92.97 268

ACMMP++															93.61 254

Test By Simon												94.64 87

High-resolution multi-view benchmark