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.

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 44	99.89 1	99.75 25	99.56 53	99.02 12	99.88 3	99.85 28	99.18 5	99.96 18	99.22 34	99.92 11	99.90 1

UA-Net		99.42 33	99.29 39	99.80 36	99.62 113	99.55 60	99.50 114	99.70 15	98.79 43	99.77 29	99.96 1	97.45 104	99.96 18	98.92 65	99.90 23	99.89 2

CHOSEN 1792x2688		99.19 62	99.10 62	99.45 105	99.89 8	98.52 181	99.39 171	99.94 1	98.73 47	99.11 173	99.89 10	95.50 162	99.94 47	99.50 8	99.97 3	99.89 2

test_0728_THIRD									98.99 21	99.81 18	99.80 70	99.09 9	99.96 18	98.85 75	99.90 23	99.88 4

test_0728_SECOND				99.91 2	99.84 31	99.89 1	99.57 81	99.51 93					99.96 18	98.93 63	99.86 50	99.88 4

DPE-MVS		99.46 22	99.32 27	99.91 2	99.78 39	99.88 5	99.36 182	99.51 93	98.73 47	99.88 3	99.84 37	98.72 53	99.96 18	98.16 152	99.87 39	99.88 4

MSP-MVS		99.42 33	99.27 44	99.88 6	99.89 8	99.80 19	99.67 42	99.50 110	98.70 49	99.77 29	99.49 201	98.21 84	99.95 38	98.46 131	99.77 81	99.88 4

DP-MVS		99.16 68	98.95 87	99.78 40	99.77 44	99.53 65	99.41 159	99.50 110	97.03 208	99.04 188	99.88 15	97.39 105	99.92 71	98.66 101	99.90 23	99.87 8

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 68	99.78 39	99.14 111	99.60 66	99.45 166	99.01 15	99.90 1	99.83 41	98.98 20	99.93 62	99.59 1	99.95 6	99.86 9

Test_1112_low_res		98.89 107	98.66 122	99.57 78	99.69 84	98.95 137	99.03 257	99.47 144	96.98 210	99.15 167	99.23 263	96.77 124	99.89 102	98.83 80	98.78 169	99.86 9

HyFIR lowres test		99.11 83	98.92 89	99.65 63	99.90 3	99.37 83	99.02 260	99.91 3	97.67 148	99.59 73	99.75 101	95.90 152	99.73 173	99.53 5	99.02 152	99.86 9

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 68	99.78 39	99.15 110	99.61 65	99.45 166	99.01 15	99.89 2	99.82 48	99.01 13	99.92 71	99.56 4	99.95 6	99.85 12

CVMVSNet		98.57 139	98.67 119	98.30 241	99.35 175	95.59 281	99.50 114	99.55 60	98.60 55	99.39 116	99.83 41	94.48 201	99.45 221	98.75 88	98.56 178	99.85 12

HPM-MVS_fast		99.51 13	99.40 15	99.85 23	99.91 1	99.79 23	99.76 24	99.56 53	97.72 142	99.76 33	99.75 101	99.13 7	99.92 71	99.07 49	99.92 11	99.85 12

MG-MVS		99.13 72	99.02 75	99.45 105	99.57 125	98.63 169	99.07 246	99.34 216	98.99 21	99.61 67	99.82 48	97.98 93	99.87 111	97.00 234	99.80 75	99.85 12

ACMMP_NAP		99.47 21	99.34 25	99.88 6	99.87 15	99.86 7	99.47 134	99.48 128	98.05 111	99.76 33	99.86 23	98.82 38	99.93 62	98.82 84	99.91 16	99.84 16

HFP-MVS		99.49 14	99.37 18	99.86 16	99.87 15	99.80 19	99.66 46	99.67 22	98.15 93	99.68 44	99.69 128	99.06 10	99.96 18	98.69 97	99.87 39	99.84 16

region2R		99.48 18	99.35 23	99.87 10	99.88 11	99.80 19	99.65 53	99.66 26	98.13 95	99.66 55	99.68 133	98.96 22	99.96 18	98.62 106	99.87 39	99.84 16

#test#		99.43 30	99.29 39	99.86 16	99.87 15	99.80 19	99.55 95	99.67 22	97.83 129	99.68 44	99.69 128	99.06 10	99.96 18	98.39 135	99.87 39	99.84 16

Regformer-4		99.59 2	99.54 4	99.73 51	99.76 47	99.41 80	99.58 76	99.49 118	99.02 12	99.88 3	99.80 70	99.00 19	99.94 47	99.45 15	99.92 11	99.84 16

XVS		99.53 10	99.42 12	99.87 10	99.85 24	99.83 11	99.69 35	99.68 19	98.98 23	99.37 120	99.74 106	98.81 39	99.94 47	98.79 85	99.86 50	99.84 16

X-MVStestdata		96.55 268	95.45 281	99.87 10	99.85 24	99.83 11	99.69 35	99.68 19	98.98 23	99.37 120	64.01 336	98.81 39	99.94 47	98.79 85	99.86 50	99.84 16

ACMMPR		99.49 14	99.36 20	99.86 16	99.87 15	99.79 23	99.66 46	99.67 22	98.15 93	99.67 50	99.69 128	98.95 25	99.96 18	98.69 97	99.87 39	99.84 16

HPM-MVS		99.42 33	99.28 42	99.83 29	99.90 3	99.72 34	99.81 12	99.54 67	97.59 153	99.68 44	99.63 156	98.91 31	99.94 47	98.58 114	99.91 16	99.84 16
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SteuartSystems-ACMMP		99.54 8	99.42 12	99.87 10	99.82 33	99.81 18	99.59 69	99.51 93	98.62 53	99.79 22	99.83 41	99.28 3	99.97 10	98.48 127	99.90 23	99.84 16
Skip Steuart: Steuart Systems R&D Blog.
1112_ss		98.98 101	98.77 109	99.59 74	99.68 87	99.02 123	99.25 216	99.48 128	97.23 190	99.13 169	99.58 173	96.93 119	99.90 94	98.87 71	98.78 169	99.84 16

MP-MVS-pluss		99.37 42	99.20 52	99.88 6	99.90 3	99.87 6	99.30 196	99.52 83	97.18 193	99.60 70	99.79 80	98.79 41	99.95 38	98.83 80	99.91 16	99.83 27
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
zzz-MVS		99.49 14	99.36 20	99.89 4	99.90 3	99.86 7	99.36 182	99.47 144	98.79 43	99.68 44	99.81 59	98.43 70	99.97 10	98.88 67	99.90 23	99.83 27

MTAPA		99.52 12	99.39 16	99.89 4	99.90 3	99.86 7	99.66 46	99.47 144	98.79 43	99.68 44	99.81 59	98.43 70	99.97 10	98.88 67	99.90 23	99.83 27

Regformer-3		99.57 6	99.53 5	99.68 56	99.76 47	99.29 92	99.58 76	99.44 173	99.01 15	99.87 7	99.80 70	98.97 21	99.91 81	99.44 17	99.92 11	99.83 27

PGM-MVS		99.45 24	99.31 33	99.86 16	99.87 15	99.78 29	99.58 76	99.65 31	97.84 128	99.71 39	99.80 70	99.12 8	99.97 10	98.33 143	99.87 39	99.83 27

mPP-MVS		99.44 27	99.30 35	99.86 16	99.88 11	99.79 23	99.69 35	99.48 128	98.12 97	99.50 88	99.75 101	98.78 42	99.97 10	98.57 116	99.89 32	99.83 27

CP-MVS		99.45 24	99.32 27	99.85 23	99.83 32	99.75 30	99.69 35	99.52 83	98.07 106	99.53 83	99.63 156	98.93 30	99.97 10	98.74 89	99.91 16	99.83 27

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 34	99.91 1	99.66 43	99.63 56	99.39 194	98.91 32	99.78 27	99.85 28	99.36 2	99.94 47	98.84 77	99.88 35	99.82 34
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MP-MVS		99.33 46	99.15 56	99.87 10	99.88 11	99.82 17	99.66 46	99.46 154	98.09 102	99.48 92	99.74 106	98.29 80	99.96 18	97.93 170	99.87 39	99.82 34
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		99.43 30	99.30 35	99.82 31	99.79 38	99.74 33	99.29 200	99.40 190	98.79 43	99.52 85	99.62 161	98.91 31	99.90 94	98.64 103	99.75 85	99.82 34

DeepC-MVS_fast	98.69 1	99.49 14	99.39 16	99.77 42	99.63 107	99.59 54	99.36 182	99.46 154	99.07 11	99.79 22	99.82 48	98.85 35	99.92 71	98.68 99	99.87 39	99.82 34
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		99.12 78	98.87 96	99.86 16	99.72 70	99.79 23	99.44 142	99.51 93	97.29 183	99.59 73	99.74 106	98.15 88	99.96 18	96.74 248	99.69 97	99.81 38

DVP-MVS		99.57 6	99.47 8	99.88 6	99.85 24	99.89 1	99.57 81	99.37 207	99.10 8	99.81 18	99.80 70	98.94 27	99.96 18	98.93 63	99.86 50	99.81 38

GST-MVS		99.40 40	99.24 49	99.85 23	99.86 20	99.79 23	99.60 66	99.67 22	97.97 117	99.63 61	99.68 133	98.52 64	99.95 38	98.38 137	99.86 50	99.81 38

SMA-MVS		99.44 27	99.30 35	99.85 23	99.73 66	99.83 11	99.56 87	99.47 144	97.45 169	99.78 27	99.82 48	99.18 5	99.91 81	98.79 85	99.89 32	99.81 38

CPTT-MVS		99.11 83	98.90 92	99.74 49	99.80 37	99.46 75	99.59 69	99.49 118	97.03 208	99.63 61	99.69 128	97.27 110	99.96 18	97.82 178	99.84 63	99.81 38

ACMMP		99.45 24	99.32 27	99.82 31	99.89 8	99.67 41	99.62 59	99.69 18	98.12 97	99.63 61	99.84 37	98.73 52	99.96 18	98.55 122	99.83 67	99.81 38
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
DeepPCF-MVS	98.18 3	98.81 122	99.37 18	97.12 290	99.60 120	91.75 319	98.61 306	99.44 173	99.35 1	99.83 13	99.85 28	98.70 55	99.81 146	99.02 53	99.91 16	99.81 38

3Dnovator+	97.12 13	99.18 64	98.97 83	99.82 31	99.17 219	99.68 39	99.81 12	99.51 93	99.20 4	98.72 229	99.89 10	95.68 159	99.97 10	98.86 74	99.86 50	99.81 38

Regformer-1		99.53 10	99.47 8	99.72 53	99.71 76	99.44 77	99.49 124	99.46 154	98.95 28	99.83 13	99.76 96	99.01 13	99.93 62	99.17 39	99.87 39	99.80 46

Regformer-2		99.54 8	99.47 8	99.75 45	99.71 76	99.52 68	99.49 124	99.49 118	98.94 29	99.83 13	99.76 96	99.01 13	99.94 47	99.15 42	99.87 39	99.80 46

APD-MVS		99.27 54	99.08 65	99.84 28	99.75 54	99.79 23	99.50 114	99.50 110	97.16 195	99.77 29	99.82 48	98.78 42	99.94 47	97.56 202	99.86 50	99.80 46
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		99.34 45	99.19 53	99.79 39	99.61 117	99.65 46	99.30 196	99.48 128	98.86 34	99.21 155	99.63 156	98.72 53	99.90 94	98.25 147	99.63 109	99.80 46

SR-MVS		99.43 30	99.29 39	99.86 16	99.75 54	99.83 11	99.59 69	99.62 32	98.21 88	99.73 36	99.79 80	98.68 56	99.96 18	98.44 133	99.77 81	99.79 50

HPM-MVS++		99.39 41	99.23 50	99.87 10	99.75 54	99.84 10	99.43 148	99.51 93	98.68 51	99.27 140	99.53 189	98.64 61	99.96 18	98.44 133	99.80 75	99.79 50

abl_6		99.44 27	99.31 33	99.83 29	99.85 24	99.75 30	99.66 46	99.59 41	98.13 95	99.82 16	99.81 59	98.60 62	99.96 18	98.46 131	99.88 35	99.79 50

PVSNet_Blended_VisFu		99.36 43	99.28 42	99.61 72	99.86 20	99.07 118	99.47 134	99.93 2	97.66 149	99.71 39	99.86 23	97.73 99	99.96 18	99.47 13	99.82 71	99.79 50

3Dnovator	97.25 9	99.24 59	99.05 67	99.81 34	99.12 227	99.66 43	99.84 6	99.74 10	99.09 10	98.92 206	99.90 7	95.94 149	99.98 5	98.95 60	99.92 11	99.79 50

APD-MVS_3200maxsize		99.48 18	99.35 23	99.85 23	99.76 47	99.83 11	99.63 56	99.54 67	98.36 72	99.79 22	99.82 48	98.86 34	99.95 38	98.62 106	99.81 73	99.78 55

CDPH-MVS		99.13 72	98.91 91	99.80 36	99.75 54	99.71 35	99.15 233	99.41 184	96.60 236	99.60 70	99.55 183	98.83 37	99.90 94	97.48 209	99.83 67	99.78 55

SD-MVS		99.41 37	99.52 6	99.05 153	99.74 61	99.68 39	99.46 137	99.52 83	99.11 7	99.88 3	99.91 5	99.43 1	97.70 320	98.72 93	99.93 10	99.77 57
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
CNVR-MVS		99.42 33	99.30 35	99.78 40	99.62 113	99.71 35	99.26 214	99.52 83	98.82 38	99.39 116	99.71 117	98.96 22	99.85 120	98.59 112	99.80 75	99.77 57

MVS_111021_HR		99.41 37	99.32 27	99.66 59	99.72 70	99.47 74	98.95 279	99.85 6	98.82 38	99.54 81	99.73 112	98.51 65	99.74 166	98.91 66	99.88 35	99.77 57

QAPM		98.67 134	98.30 149	99.80 36	99.20 208	99.67 41	99.77 21	99.72 11	94.74 290	98.73 228	99.90 7	95.78 156	99.98 5	96.96 238	99.88 35	99.76 60

test9_res														97.49 208	99.72 91	99.75 61

train_agg		99.02 96	98.77 109	99.77 42	99.67 88	99.65 46	99.05 251	99.41 184	96.28 255	98.95 202	99.49 201	98.76 47	99.91 81	97.63 195	99.72 91	99.75 61

agg_prior1		99.01 99	98.76 111	99.76 44	99.67 88	99.62 49	98.99 267	99.40 190	96.26 258	98.87 212	99.49 201	98.77 45	99.91 81	97.69 192	99.72 91	99.75 61

agg_prior2														97.21 222	99.73 90	99.75 61

save filter2										99.48 92	99.70 121	98.95 25	99.95 38	98.59 112	99.85 57	99.74 65

test_prior3		99.21 60	99.05 67	99.68 56	99.67 88	99.48 72	98.96 275	99.56 53	98.34 74	99.01 191	99.52 192	98.68 56	99.83 134	97.96 167	99.74 87	99.74 65

test_prior				99.68 56	99.67 88	99.48 72		99.56 53					99.83 134			99.74 65

test12				99.75 45	99.64 104	99.61 51		99.29 236		99.21 155		98.38 75	99.89 102		99.74 87	99.74 65

114514_t		98.93 105	98.67 119	99.72 53	99.85 24	99.53 65	99.62 59	99.59 41	92.65 308	99.71 39	99.78 86	98.06 91	99.90 94	98.84 77	99.91 16	99.74 65

Vis-MVSNet		99.12 78	98.97 83	99.56 80	99.78 39	99.10 115	99.68 40	99.66 26	98.49 61	99.86 8	99.87 20	94.77 187	99.84 125	99.19 36	99.41 122	99.74 65
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
旧先验1					99.74 61	99.59 54		99.54 67			99.69 128	98.47 67			99.68 101	99.73 71

1121		99.09 87	98.87 96	99.75 45	99.74 61	99.60 53	99.27 206	99.48 128	96.82 223	99.25 146	99.65 145	98.38 75	99.93 62	97.53 205	99.67 103	99.73 71

EPNet		98.86 111	98.71 115	99.30 128	97.20 318	98.18 199	99.62 59	98.91 279	99.28 2	98.63 247	99.81 59	95.96 146	99.99 1	99.24 33	99.72 91	99.73 71
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		99.05 92	98.87 96	99.57 78	99.73 66	99.32 87	99.75 25	99.20 246	98.02 115	99.56 78	99.86 23	96.54 130	99.67 194	98.09 156	99.13 141	99.73 71

F-COLMAP		99.19 62	99.04 70	99.64 68	99.78 39	99.27 95	99.42 155	99.54 67	97.29 183	99.41 109	99.59 170	98.42 73	99.93 62	98.19 149	99.69 97	99.73 71

DeepC-MVS	98.35 2	99.30 49	99.19 53	99.64 68	99.82 33	99.23 99	99.62 59	99.55 60	98.94 29	99.63 61	99.95 2	95.82 155	99.94 47	99.37 18	99.97 3	99.73 71
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
新几何1				99.75 45	99.75 54	99.59 54		99.54 67	96.76 224	99.29 136	99.64 152	98.43 70	99.94 47	96.92 242	99.66 104	99.72 77

无先验							98.99 267	99.51 93	96.89 217				99.93 62	97.53 205		99.72 77

test222					99.75 54	99.49 71	98.91 284	99.49 118	96.42 249	99.34 129	99.65 145	98.28 81			99.69 97	99.72 77

testdata				99.54 82	99.75 54	98.95 137		99.51 93	97.07 204	99.43 102	99.70 121	98.87 33	99.94 47	97.76 183	99.64 107	99.72 77

VNet		99.11 83	98.90 92	99.73 51	99.52 134	99.56 58	99.41 159	99.39 194	99.01 15	99.74 35	99.78 86	95.56 160	99.92 71	99.52 6	98.18 195	99.72 77

WTY-MVS		99.06 91	98.88 95	99.61 72	99.62 113	99.16 106	99.37 178	99.56 53	98.04 112	99.53 83	99.62 161	96.84 120	99.94 47	98.85 75	98.49 182	99.72 77

CSCG		99.32 47	99.32 27	99.32 123	99.85 24	98.29 195	99.71 31	99.66 26	98.11 99	99.41 109	99.80 70	98.37 77	99.96 18	98.99 55	99.96 5	99.72 77

原ACMM1				99.65 63	99.73 66	99.33 86		99.47 144	97.46 166	99.12 171	99.66 144	98.67 59	99.91 81	97.70 191	99.69 97	99.71 84

Anonymous202405211		98.30 156	97.98 171	99.26 135	99.57 125	98.16 200	99.41 159	98.55 306	96.03 277	99.19 161	99.74 106	91.87 262	99.92 71	99.16 41	98.29 190	99.70 85

casdiffmvs		99.13 72	98.98 82	99.56 80	99.65 102	99.16 106	99.56 87	99.50 110	98.33 77	99.41 109	99.86 23	95.92 150	99.83 134	99.45 15	99.16 137	99.70 85

LFMVS		97.90 198	97.35 240	99.54 82	99.52 134	99.01 125	99.39 171	98.24 310	97.10 203	99.65 58	99.79 80	84.79 318	99.91 81	99.28 29	98.38 184	99.69 87

EPNet_dtu		98.03 180	97.96 173	98.23 247	98.27 303	95.54 284	99.23 219	98.75 290	99.02 12	97.82 285	99.71 117	96.11 142	99.48 218	93.04 307	99.65 106	99.69 87
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PAPM_NR		99.04 93	98.84 102	99.66 59	99.74 61	99.44 77	99.39 171	99.38 200	97.70 144	99.28 137	99.28 255	98.34 78	99.85 120	96.96 238	99.45 119	99.69 87

EPP-MVSNet		99.13 72	98.99 79	99.53 88	99.65 102	99.06 119	99.81 12	99.33 222	97.43 171	99.60 70	99.88 15	97.14 112	99.84 125	99.13 43	98.94 156	99.69 87

sss		99.17 66	99.05 67	99.53 88	99.62 113	98.97 131	99.36 182	99.62 32	97.83 129	99.67 50	99.65 145	97.37 108	99.95 38	99.19 36	99.19 136	99.68 91

PHI-MVS		99.30 49	99.17 55	99.70 55	99.56 129	99.52 68	99.58 76	99.80 8	97.12 199	99.62 65	99.73 112	98.58 63	99.90 94	98.61 109	99.91 16	99.68 91

PVSNet_0	94.43 19	96.09 278	95.47 280	97.94 263	99.31 188	94.34 305	97.81 324	99.70 15	97.12 199	97.46 289	98.75 298	89.71 291	99.79 155	97.69 192	81.69 326	99.68 91

diffmvs		99.14 70	99.02 75	99.51 95	99.61 117	98.96 135	99.28 202	99.49 118	98.46 64	99.72 38	99.71 117	96.50 131	99.88 107	99.31 26	99.11 142	99.67 94

baseline		99.15 69	99.02 75	99.53 88	99.66 97	99.14 111	99.72 29	99.48 128	98.35 73	99.42 105	99.84 37	96.07 143	99.79 155	99.51 7	99.14 140	99.67 94

TAMVS		99.12 78	99.08 65	99.24 138	99.46 151	98.55 175	99.51 108	99.46 154	98.09 102	99.45 97	99.82 48	98.34 78	99.51 217	98.70 94	98.93 157	99.67 94

Anonymous20240529		98.09 172	97.68 201	99.34 118	99.66 97	98.44 189	99.40 167	99.43 180	93.67 300	99.22 152	99.89 10	90.23 287	99.93 62	99.26 32	98.33 185	99.66 97

CHOSEN 280x420		99.12 78	99.13 58	99.08 149	99.66 97	97.89 213	98.43 313	99.71 13	98.88 33	99.62 65	99.76 96	96.63 128	99.70 189	99.46 14	99.99 1	99.66 97

CDS-MVSNet		99.09 87	99.03 72	99.25 136	99.42 158	98.73 161	99.45 138	99.46 154	98.11 99	99.46 96	99.77 92	98.01 92	99.37 237	98.70 94	98.92 159	99.66 97
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		98.63 138	98.34 145	99.51 95	99.40 166	99.03 122	98.80 293	99.36 208	96.33 252	99.00 196	99.12 275	98.46 68	99.84 125	95.23 283	99.37 127	99.66 97

CANet		99.25 58	99.14 57	99.59 74	99.41 161	99.16 106	99.35 187	99.57 48	98.82 38	99.51 87	99.61 165	96.46 132	99.95 38	99.59 1	99.98 2	99.65 101

TSAR-MVS + GP.		99.36 43	99.36 20	99.36 117	99.67 88	98.61 172	99.07 246	99.33 222	99.00 19	99.82 16	99.81 59	99.06 10	99.84 125	99.09 47	99.42 121	99.65 101

MVSFormer		99.17 66	99.12 60	99.29 131	99.51 136	98.94 140	99.88 1	99.46 154	97.55 158	99.80 20	99.65 145	97.39 105	99.28 253	99.03 51	99.85 57	99.65 101

jason		99.13 72	99.03 72	99.45 105	99.46 151	98.87 147	99.12 236	99.26 239	98.03 114	99.79 22	99.65 145	97.02 115	99.85 120	99.02 53	99.90 23	99.65 101
jason: jason.
PLC	97.94 4	99.02 96	98.85 101	99.53 88	99.66 97	99.01 125	99.24 218	99.52 83	96.85 219	99.27 140	99.48 207	98.25 82	99.91 81	97.76 183	99.62 111	99.65 101
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	97.07 15	97.74 224	97.34 243	98.94 167	99.70 82	97.53 222	99.25 216	99.51 93	91.90 310	99.30 133	99.63 156	98.78 42	99.64 202	88.09 321	99.87 39	99.65 101
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LCM-MVSNet-Re		97.83 208	98.15 155	96.87 295	99.30 189	92.25 318	99.59 69	98.26 309	97.43 171	96.20 304	99.13 272	96.27 139	98.73 307	98.17 151	98.99 154	99.64 107

BH-RMVSNet		98.41 147	98.08 162	99.40 113	99.41 161	98.83 154	99.30 196	98.77 289	97.70 144	98.94 204	99.65 145	92.91 238	99.74 166	96.52 256	99.55 116	99.64 107

MVS_111021_LR		99.41 37	99.33 26	99.65 63	99.77 44	99.51 70	98.94 281	99.85 6	98.82 38	99.65 58	99.74 106	98.51 65	99.80 151	98.83 80	99.89 32	99.64 107

MVS		97.28 257	96.55 264	99.48 99	98.78 273	98.95 137	99.27 206	99.39 194	83.53 323	98.08 275	99.54 188	96.97 117	99.87 111	94.23 295	99.16 137	99.63 110

MSLP-MVS++		99.46 22	99.47 8	99.44 110	99.60 120	99.16 106	99.41 159	99.71 13	98.98 23	99.45 97	99.78 86	99.19 4	99.54 216	99.28 29	99.84 63	99.63 110

GA-MVS		97.85 204	97.47 220	99.00 159	99.38 170	97.99 208	98.57 308	99.15 250	97.04 207	98.90 209	99.30 252	89.83 290	99.38 234	96.70 251	98.33 185	99.62 112

Vis-MVSNet (Re-imp)		98.87 108	98.72 113	99.31 124	99.71 76	98.88 146	99.80 16	99.44 173	97.91 122	99.36 123	99.78 86	95.49 163	99.43 230	97.91 171	99.11 142	99.62 112

DPM-MVS		98.95 104	98.71 115	99.66 59	99.63 107	99.55 60	98.64 305	99.10 255	97.93 120	99.42 105	99.55 183	98.67 59	99.80 151	95.80 270	99.68 101	99.61 114

baseline1		98.31 154	97.95 174	99.38 116	99.50 142	98.74 160	99.59 69	98.93 273	98.41 69	99.14 168	99.60 168	94.59 196	99.79 155	98.48 127	93.29 301	99.61 114

VDD-MVS		97.73 225	97.35 240	98.88 183	99.47 150	97.12 233	99.34 190	98.85 285	98.19 89	99.67 50	99.85 28	82.98 320	99.92 71	99.49 12	98.32 189	99.60 116

DELS-MVS		99.48 18	99.42 12	99.65 63	99.72 70	99.40 82	99.05 251	99.66 26	99.14 6	99.57 77	99.80 70	98.46 68	99.94 47	99.57 3	99.84 63	99.60 116
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
PVSNet_Blended		99.08 89	98.97 83	99.42 112	99.76 47	98.79 158	98.78 295	99.91 3	96.74 225	99.67 50	99.49 201	97.53 102	99.88 107	98.98 56	99.85 57	99.60 116

OMC-MVS		99.08 89	99.04 70	99.20 141	99.67 88	98.22 198	99.28 202	99.52 83	98.07 106	99.66 55	99.81 59	97.79 97	99.78 159	97.79 180	99.81 73	99.60 116

test_yl		98.86 111	98.63 124	99.54 82	99.49 144	99.18 103	99.50 114	99.07 261	98.22 86	99.61 67	99.51 195	95.37 165	99.84 125	98.60 110	98.33 185	99.59 120

DCV-MVSNet		98.86 111	98.63 124	99.54 82	99.49 144	99.18 103	99.50 114	99.07 261	98.22 86	99.61 67	99.51 195	95.37 165	99.84 125	98.60 110	98.33 185	99.59 120

AllTest		98.87 108	98.72 113	99.31 124	99.86 20	98.48 187	99.56 87	99.61 34	97.85 126	99.36 123	99.85 28	95.95 147	99.85 120	96.66 254	99.83 67	99.59 120

TestCases				99.31 124	99.86 20	98.48 187		99.61 34	97.85 126	99.36 123	99.85 28	95.95 147	99.85 120	96.66 254	99.83 67	99.59 120

lupinMVS		99.13 72	99.01 78	99.46 104	99.51 136	98.94 140	99.05 251	99.16 249	97.86 124	99.80 20	99.56 180	97.39 105	99.86 114	98.94 61	99.85 57	99.58 124

tttt0517		98.42 145	98.14 156	99.28 133	99.66 97	98.38 193	99.74 28	96.85 324	97.68 146	99.79 22	99.74 106	91.39 274	99.89 102	98.83 80	99.56 114	99.57 125

RPSCF		98.22 160	98.62 129	96.99 291	99.82 33	91.58 320	99.72 29	99.44 173	96.61 234	99.66 55	99.89 10	95.92 150	99.82 142	97.46 212	99.10 145	99.57 125

DSMNet-mixed		97.25 258	97.35 240	96.95 293	97.84 308	93.61 312	99.57 81	96.63 327	96.13 271	98.87 212	98.61 303	94.59 196	97.70 320	95.08 285	98.86 164	99.55 127

AdaColmap		99.01 99	98.80 106	99.66 59	99.56 129	99.54 62	99.18 228	99.70 15	98.18 92	99.35 126	99.63 156	96.32 137	99.90 94	97.48 209	99.77 81	99.55 127

alignmvs		98.81 122	98.56 136	99.58 77	99.43 157	99.42 79	99.51 108	98.96 271	98.61 54	99.35 126	98.92 290	94.78 184	99.77 161	99.35 19	98.11 202	99.54 129

PatchmatchNet		98.31 154	98.36 143	98.19 249	99.16 221	95.32 289	99.27 206	98.92 276	97.37 177	99.37 120	99.58 173	94.90 178	99.70 189	97.43 215	99.21 134	99.54 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PVSNet	96.02 17	98.85 119	98.84 102	98.89 180	99.73 66	97.28 227	98.32 316	99.60 38	97.86 124	99.50 88	99.57 177	96.75 125	99.86 114	98.56 119	99.70 96	99.54 129

MSDG		98.98 101	98.80 106	99.53 88	99.76 47	99.19 101	98.75 298	99.55 60	97.25 187	99.47 94	99.77 92	97.82 96	99.87 111	96.93 241	99.90 23	99.54 129

UGNet		98.87 108	98.69 117	99.40 113	99.22 204	98.72 162	99.44 142	99.68 19	99.24 3	99.18 164	99.42 221	92.74 242	99.96 18	99.34 23	99.94 9	99.53 133
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
GSMVS																99.52 134

sam_mvs1												94.86 180				99.52 134

SCA		98.19 164	98.16 154	98.27 246	99.30 189	95.55 282	99.07 246	98.97 269	97.57 156	99.43 102	99.57 177	92.72 243	99.74 166	97.58 198	99.20 135	99.52 134

Patchmatch-test		97.93 193	97.65 204	98.77 204	99.18 213	97.07 238	99.03 257	99.14 252	96.16 267	98.74 227	99.57 177	94.56 198	99.72 177	93.36 303	99.11 142	99.52 134

PMMVS		98.80 125	98.62 129	99.34 118	99.27 197	98.70 163	98.76 297	99.31 229	97.34 178	99.21 155	99.07 277	97.20 111	99.82 142	98.56 119	98.87 163	99.52 134

LS3D		99.27 54	99.12 60	99.74 49	99.18 213	99.75 30	99.56 87	99.57 48	98.45 65	99.49 91	99.85 28	97.77 98	99.94 47	98.33 143	99.84 63	99.52 134

Effi-MVS+		98.81 122	98.59 134	99.48 99	99.46 151	99.12 114	98.08 322	99.50 110	97.50 165	99.38 118	99.41 224	96.37 136	99.81 146	99.11 45	98.54 179	99.51 140

Patchmatch-RL test		95.84 280	95.81 277	95.95 302	95.61 319	90.57 321	98.24 318	98.39 308	95.10 286	95.20 309	98.67 300	94.78 184	97.77 318	96.28 262	90.02 317	99.51 140

mvs_anonymous		99.03 95	98.99 79	99.16 144	99.38 170	98.52 181	99.51 108	99.38 200	97.79 134	99.38 118	99.81 59	97.30 109	99.45 221	99.35 19	98.99 154	99.51 140

UniMVSNet_ETH3D		97.32 256	96.81 260	98.87 187	99.40 166	97.46 224	99.51 108	99.53 78	95.86 279	98.54 253	99.77 92	82.44 323	99.66 197	98.68 99	97.52 221	99.50 143

ab-mvs		98.86 111	98.63 124	99.54 82	99.64 104	99.19 101	99.44 142	99.54 67	97.77 136	99.30 133	99.81 59	94.20 209	99.93 62	99.17 39	98.82 166	99.49 144

thisisatest0530		98.35 152	98.03 166	99.31 124	99.63 107	98.56 174	99.54 98	96.75 326	97.53 162	99.73 36	99.65 145	91.25 277	99.89 102	98.62 106	99.56 114	99.48 145

ADS-MVSNet2		98.02 182	98.07 164	97.87 268	99.33 180	95.19 292	99.23 219	99.08 258	96.24 260	99.10 176	99.67 139	94.11 213	98.93 300	96.81 245	99.05 149	99.48 145

ADS-MVSNet		98.20 163	98.08 162	98.56 218	99.33 180	96.48 265	99.23 219	99.15 250	96.24 260	99.10 176	99.67 139	94.11 213	99.71 183	96.81 245	99.05 149	99.48 145

tpm		97.67 236	97.55 210	98.03 256	99.02 245	95.01 295	99.43 148	98.54 307	96.44 247	99.12 171	99.34 243	91.83 263	99.60 210	97.75 185	96.46 251	99.48 145

CNLPA		99.14 70	98.99 79	99.59 74	99.58 123	99.41 80	99.16 230	99.44 173	98.45 65	99.19 161	99.49 201	98.08 90	99.89 102	97.73 187	99.75 85	99.48 145

canonicalmvs		99.02 96	98.86 100	99.51 95	99.42 158	99.32 87	99.80 16	99.48 128	98.63 52	99.31 132	98.81 294	97.09 113	99.75 165	99.27 31	97.90 206	99.47 150

MIMVSNet		97.73 225	97.45 223	98.57 216	99.45 156	97.50 223	99.02 260	98.98 268	96.11 272	99.41 109	99.14 271	90.28 283	98.74 306	95.74 271	98.93 157	99.47 150

MVS_Test		99.10 86	98.97 83	99.48 99	99.49 144	99.14 111	99.67 42	99.34 216	97.31 181	99.58 75	99.76 96	97.65 101	99.82 142	98.87 71	99.07 148	99.46 152

MDTV_nov1_ep13_2view						95.18 293	99.35 187		96.84 220	99.58 75		95.19 173		97.82 178		99.46 152

MVS-HIRNet		95.75 281	95.16 284	97.51 284	99.30 189	93.69 311	98.88 286	95.78 329	85.09 322	98.78 224	92.65 325	91.29 276	99.37 237	94.85 288	99.85 57	99.46 152

DI_MVS_plusplus_test		97.45 252	96.79 261	99.44 110	97.76 310	99.04 121	99.21 225	98.61 304	97.74 140	94.01 314	98.83 293	87.38 311	99.83 134	98.63 104	98.90 161	99.44 155

DP-MVS Recon		99.12 78	98.95 87	99.65 63	99.74 61	99.70 37	99.27 206	99.57 48	96.40 251	99.42 105	99.68 133	98.75 50	99.80 151	97.98 166	99.72 91	99.44 155

PatchMatch-RL		98.84 121	98.62 129	99.52 93	99.71 76	99.28 93	99.06 249	99.77 9	97.74 140	99.50 88	99.53 189	95.41 164	99.84 125	97.17 227	99.64 107	99.44 155

VDDNet		97.55 242	97.02 257	99.16 144	99.49 144	98.12 204	99.38 176	99.30 231	95.35 283	99.68 44	99.90 7	82.62 322	99.93 62	99.31 26	98.13 201	99.42 158

PCF-MVS	97.08 14	97.66 237	97.06 256	99.47 102	99.61 117	99.09 116	98.04 323	99.25 241	91.24 313	98.51 254	99.70 121	94.55 199	99.91 81	92.76 309	99.85 57	99.42 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ET-MVSNet_ETH3D		96.49 270	95.64 279	99.05 153	99.53 132	98.82 155	98.84 290	97.51 321	97.63 151	84.77 323	99.21 266	92.09 260	98.91 301	98.98 56	92.21 312	99.41 160

HY-MVS	97.30 7	98.85 119	98.64 123	99.47 102	99.42 158	99.08 117	99.62 59	99.36 208	97.39 176	99.28 137	99.68 133	96.44 134	99.92 71	98.37 139	98.22 191	99.40 161

Fast-Effi-MVS+		98.70 131	98.43 140	99.51 95	99.51 136	99.28 93	99.52 104	99.47 144	96.11 272	99.01 191	99.34 243	96.20 141	99.84 125	97.88 173	98.82 166	99.39 162

CANet_DTU		98.97 103	98.87 96	99.25 136	99.33 180	98.42 192	99.08 245	99.30 231	99.16 5	99.43 102	99.75 101	95.27 169	99.97 10	98.56 119	99.95 6	99.36 163

ETV-MVS		99.18 64	99.09 64	99.45 105	99.49 144	99.18 103	99.67 42	99.53 78	97.66 149	99.40 114	99.44 216	98.10 89	99.81 146	98.94 61	99.62 111	99.35 164

EPMVS		97.82 211	97.65 204	98.35 237	98.88 259	95.98 276	99.49 124	94.71 332	97.57 156	99.26 144	99.48 207	92.46 256	99.71 183	97.87 174	99.08 147	99.35 164

CostFormer		97.72 227	97.73 197	97.71 278	99.15 224	94.02 307	99.54 98	99.02 265	94.67 291	99.04 188	99.35 240	92.35 258	99.77 161	98.50 126	97.94 205	99.34 166

BH-untuned		98.42 145	98.36 143	98.59 214	99.49 144	96.70 257	99.27 206	99.13 253	97.24 189	98.80 221	99.38 231	95.75 157	99.74 166	97.07 232	99.16 137	99.33 167

PAPM		97.59 241	97.09 255	99.07 150	99.06 238	98.26 197	98.30 317	99.10 255	94.88 287	98.08 275	99.34 243	96.27 139	99.64 202	89.87 316	98.92 159	99.31 168

tpm2		97.44 253	97.34 243	97.74 277	99.15 224	94.36 304	99.45 138	98.94 272	93.45 305	98.90 209	99.44 216	91.35 275	99.59 211	97.31 217	98.07 203	99.29 169

JIA-IIPM		97.50 248	97.02 257	98.93 169	98.73 279	97.80 217	99.30 196	98.97 269	91.73 311	98.91 207	94.86 323	95.10 174	99.71 183	97.58 198	97.98 204	99.28 170

dp		97.75 222	97.80 185	97.59 281	99.10 232	93.71 310	99.32 192	98.88 283	96.48 245	99.08 181	99.55 183	92.67 247	99.82 142	96.52 256	98.58 175	99.24 171

thisisatest0515		98.14 168	97.79 186	99.19 142	99.50 142	98.50 184	98.61 306	96.82 325	96.95 214	99.54 81	99.43 218	91.66 270	99.86 114	98.08 160	99.51 118	99.22 172

TESTMET0.1,1		97.55 242	97.27 251	98.40 234	98.93 255	96.53 263	98.67 301	97.61 320	96.96 212	98.64 246	99.28 255	88.63 302	99.45 221	97.30 218	99.38 123	99.21 173

DWT-MVSNet_test		97.53 244	97.40 234	97.93 264	99.03 244	94.86 298	99.57 81	98.63 302	96.59 238	98.36 263	98.79 295	89.32 294	99.74 166	98.14 154	98.16 200	99.20 174

CR-MVSNet		98.17 166	97.93 177	98.87 187	99.18 213	98.49 185	99.22 223	99.33 222	96.96 212	99.56 78	99.38 231	94.33 205	99.00 289	94.83 289	98.58 175	99.14 175

RPMNet		96.61 267	95.85 275	98.87 187	99.18 213	98.49 185	99.22 223	99.08 258	88.72 319	99.56 78	97.38 315	94.08 215	99.00 289	86.87 325	98.58 175	99.14 175

testgi		97.65 238	97.50 217	98.13 253	99.36 174	96.45 266	99.42 155	99.48 128	97.76 137	97.87 283	99.45 215	91.09 278	98.81 305	94.53 291	98.52 180	99.13 177

CS-MVS		99.21 60	99.13 58	99.45 105	99.54 131	99.34 85	99.71 31	99.54 67	98.26 81	98.99 198	99.24 261	98.25 82	99.88 107	98.98 56	99.63 109	99.12 178

test-LLR		98.06 175	97.90 179	98.55 220	98.79 270	97.10 234	98.67 301	97.75 317	97.34 178	98.61 250	98.85 291	94.45 202	99.45 221	97.25 220	99.38 123	99.10 179

test-mter		97.49 250	97.13 254	98.55 220	98.79 270	97.10 234	98.67 301	97.75 317	96.65 231	98.61 250	98.85 291	88.23 306	99.45 221	97.25 220	99.38 123	99.10 179

IB-MVS	95.67 18	96.22 274	95.44 282	98.57 216	99.21 206	96.70 257	98.65 304	97.74 319	96.71 227	97.27 292	98.54 305	86.03 314	99.92 71	98.47 130	86.30 323	99.10 179
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
MAR-MVS		98.86 111	98.63 124	99.54 82	99.37 172	99.66 43	99.45 138	99.54 67	96.61 234	99.01 191	99.40 226	97.09 113	99.86 114	97.68 194	99.53 117	99.10 179
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
tpmrst		98.33 153	98.48 139	97.90 267	99.16 221	94.78 299	99.31 194	99.11 254	97.27 185	99.45 97	99.59 170	95.33 167	99.84 125	98.48 127	98.61 172	99.09 183

xiu_mvs_v1_base_debu		99.29 51	99.27 44	99.34 118	99.63 107	98.97 131	99.12 236	99.51 93	98.86 34	99.84 10	99.47 210	98.18 85	99.99 1	99.50 8	99.31 128	99.08 184

xiu_mvs_v1_base		99.29 51	99.27 44	99.34 118	99.63 107	98.97 131	99.12 236	99.51 93	98.86 34	99.84 10	99.47 210	98.18 85	99.99 1	99.50 8	99.31 128	99.08 184

xiu_mvs_v1_base_debi		99.29 51	99.27 44	99.34 118	99.63 107	98.97 131	99.12 236	99.51 93	98.86 34	99.84 10	99.47 210	98.18 85	99.99 1	99.50 8	99.31 128	99.08 184

COLMAP_ROB	97.56 6	98.86 111	98.75 112	99.17 143	99.88 11	98.53 177	99.34 190	99.59 41	97.55 158	98.70 236	99.89 10	95.83 154	99.90 94	98.10 155	99.90 23	99.08 184
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchFormer-LS_test		98.01 185	98.05 165	97.87 268	99.15 224	94.76 300	99.42 155	98.93 273	97.12 199	98.84 217	98.59 304	93.74 226	99.80 151	98.55 122	98.17 199	99.06 188

OpenMVS	96.50 16	98.47 141	98.12 158	99.52 93	99.04 242	99.53 65	99.82 10	99.72 11	94.56 293	98.08 275	99.88 15	94.73 190	99.98 5	97.47 211	99.76 84	99.06 188

EIA-MVS		99.26 56	99.21 51	99.40 113	99.46 151	99.30 91	99.56 87	99.52 83	98.52 59	99.44 101	99.27 258	98.41 74	99.86 114	99.10 46	99.59 113	99.04 190

PatchT		97.03 263	96.44 266	98.79 202	98.99 248	98.34 194	99.16 230	99.07 261	92.13 309	99.52 85	97.31 317	94.54 200	98.98 291	88.54 319	98.73 171	99.03 191

BH-w/o		98.00 186	97.89 183	98.32 239	99.35 175	96.20 274	99.01 265	98.90 281	96.42 249	98.38 261	99.00 283	95.26 171	99.72 177	96.06 264	98.61 172	99.03 191

Fast-Effi-MVS+-dtu		98.77 128	98.83 105	98.60 213	99.41 161	96.99 245	99.52 104	99.49 118	98.11 99	99.24 147	99.34 243	96.96 118	99.79 155	97.95 169	99.45 119	99.02 193

XVG-OURS-SEG-HR		98.69 132	98.62 129	98.89 180	99.71 76	97.74 219	99.12 236	99.54 67	98.44 68	99.42 105	99.71 117	94.20 209	99.92 71	98.54 124	98.90 161	99.00 194

XVG-OURS		98.73 130	98.68 118	98.88 183	99.70 82	97.73 220	98.92 282	99.55 60	98.52 59	99.45 97	99.84 37	95.27 169	99.91 81	98.08 160	98.84 165	99.00 194

tpm cat1		97.39 254	97.36 238	97.50 285	99.17 219	93.73 309	99.43 148	99.31 229	91.27 312	98.71 230	99.08 276	94.31 207	99.77 161	96.41 260	98.50 181	99.00 194

xiu_mvs_v2_base		99.26 56	99.25 48	99.29 131	99.53 132	98.91 144	99.02 260	99.45 166	98.80 42	99.71 39	99.26 259	98.94 27	99.98 5	99.34 23	99.23 133	98.98 197

PS-MVSNAJ		99.32 47	99.32 27	99.30 128	99.57 125	98.94 140	98.97 274	99.46 154	98.92 31	99.71 39	99.24 261	99.01 13	99.98 5	99.35 19	99.66 104	98.97 198

tpmvs		97.98 188	98.02 168	97.84 271	99.04 242	94.73 301	99.31 194	99.20 246	96.10 276	98.76 226	99.42 221	94.94 176	99.81 146	96.97 237	98.45 183	98.97 198

mvs-test1		98.86 111	98.84 102	98.89 180	99.33 180	97.77 218	99.44 142	99.30 231	98.47 62	99.10 176	99.43 218	96.78 122	99.95 38	98.73 91	99.02 152	98.96 200

thres600view7		97.86 203	97.51 216	98.92 171	99.72 70	97.95 212	99.59 69	98.74 293	97.94 119	99.27 140	98.62 301	91.75 264	99.86 114	93.73 300	98.19 194	98.96 200

thres400		97.77 217	97.38 236	98.92 171	99.69 84	97.96 210	99.50 114	98.73 298	97.83 129	99.17 165	98.45 307	91.67 268	99.83 134	93.22 304	98.18 195	98.96 200

TR-MVS		97.76 218	97.41 233	98.82 197	99.06 238	97.87 214	98.87 288	98.56 305	96.63 233	98.68 238	99.22 264	92.49 252	99.65 200	95.40 280	97.79 208	98.95 203

test0.0.03 1		97.71 230	97.42 232	98.56 218	98.41 302	97.82 216	98.78 295	98.63 302	97.34 178	98.05 279	98.98 287	94.45 202	98.98 291	95.04 286	97.15 242	98.89 204

baseline2		97.87 201	97.55 210	98.82 197	99.18 213	98.02 206	99.41 159	96.58 328	96.97 211	96.51 301	99.17 268	93.43 227	99.57 212	97.71 190	99.03 151	98.86 205

cascas		97.69 231	97.43 231	98.48 225	98.60 294	97.30 226	98.18 321	99.39 194	92.96 307	98.41 259	98.78 297	93.77 223	99.27 256	98.16 152	98.61 172	98.86 205

1314		98.68 133	98.54 137	99.11 148	98.89 258	98.65 167	99.27 206	99.49 118	96.89 217	97.99 280	99.56 180	97.72 100	99.83 134	97.74 186	99.27 131	98.84 207

PS-MVSNAJss		98.92 106	98.92 89	98.90 177	98.78 273	98.53 177	99.78 19	99.54 67	98.07 106	99.00 196	99.76 96	99.01 13	99.37 237	99.13 43	97.23 238	98.81 208

FC-MVSNet-test		98.75 129	98.62 129	99.15 146	99.08 235	99.45 76	99.86 5	99.60 38	98.23 85	98.70 236	99.82 48	96.80 121	99.22 264	99.07 49	96.38 253	98.79 209

nrg030		98.64 137	98.42 141	99.28 133	99.05 241	99.69 38	99.81 12	99.46 154	98.04 112	99.01 191	99.82 48	96.69 127	99.38 234	99.34 23	94.59 285	98.78 210

FIs		98.78 126	98.63 124	99.23 140	99.18 213	99.54 62	99.83 9	99.59 41	98.28 79	98.79 223	99.81 59	96.75 125	99.37 237	99.08 48	96.38 253	98.78 210

EU-MVSNet		97.98 188	98.03 166	97.81 274	98.72 281	96.65 260	99.66 46	99.66 26	98.09 102	98.35 264	99.82 48	95.25 172	98.01 313	97.41 216	95.30 273	98.78 210

jajsoiax		98.43 144	98.28 150	98.88 183	98.60 294	98.43 190	99.82 10	99.53 78	98.19 89	98.63 247	99.80 70	93.22 232	99.44 226	99.22 34	97.50 224	98.77 213

mvs_tets		98.40 149	98.23 152	98.91 175	98.67 287	98.51 183	99.66 46	99.53 78	98.19 89	98.65 245	99.81 59	92.75 240	99.44 226	99.31 26	97.48 228	98.77 213

Anonymous20231211		97.88 199	97.54 213	98.90 177	99.71 76	98.53 177	99.48 129	99.57 48	94.16 296	98.81 219	99.68 133	93.23 230	99.42 231	98.84 77	94.42 288	98.76 215

XXY-MVS		98.38 150	98.09 161	99.24 138	99.26 199	99.32 87	99.56 87	99.55 60	97.45 169	98.71 230	99.83 41	93.23 230	99.63 207	98.88 67	96.32 255	98.76 215

v7n		97.87 201	97.52 214	98.92 171	98.76 277	98.58 173	99.84 6	99.46 154	96.20 263	98.91 207	99.70 121	94.89 179	99.44 226	96.03 265	93.89 296	98.75 217

PS-CasMVS		97.93 193	97.59 209	98.95 166	98.99 248	99.06 119	99.68 40	99.52 83	97.13 197	98.31 266	99.68 133	92.44 257	99.05 282	98.51 125	94.08 294	98.75 217

test_djsdf		98.67 134	98.57 135	98.98 161	98.70 284	98.91 144	99.88 1	99.46 154	97.55 158	99.22 152	99.88 15	95.73 158	99.28 253	99.03 51	97.62 213	98.75 217

Effi-MVS+-dtu		98.78 126	98.89 94	98.47 227	99.33 180	96.91 251	99.57 81	99.30 231	98.47 62	99.41 109	98.99 284	96.78 122	99.74 166	98.73 91	99.38 123	98.74 220

CP-MVSNet		98.09 172	97.78 189	99.01 157	98.97 253	99.24 98	99.67 42	99.46 154	97.25 187	98.48 256	99.64 152	93.79 222	99.06 281	98.63 104	94.10 293	98.74 220

VPA-MVSNet		98.29 157	97.95 174	99.30 128	99.16 221	99.54 62	99.50 114	99.58 47	98.27 80	99.35 126	99.37 234	92.53 251	99.65 200	99.35 19	94.46 286	98.72 222

PEN-MVS		97.76 218	97.44 228	98.72 207	98.77 276	98.54 176	99.78 19	99.51 93	97.06 206	98.29 268	99.64 152	92.63 248	98.89 304	98.09 156	93.16 303	98.72 222

VPNet		97.84 206	97.44 228	99.01 157	99.21 206	98.94 140	99.48 129	99.57 48	98.38 71	99.28 137	99.73 112	88.89 298	99.39 232	99.19 36	93.27 302	98.71 224

EI-MVSNet		98.67 134	98.67 119	98.68 210	99.35 175	97.97 209	99.50 114	99.38 200	96.93 216	99.20 158	99.83 41	97.87 94	99.36 241	98.38 137	97.56 218	98.71 224

WR-MVS		98.06 175	97.73 197	99.06 151	98.86 265	99.25 97	99.19 227	99.35 213	97.30 182	98.66 239	99.43 218	93.94 218	99.21 268	98.58 114	94.28 290	98.71 224

IterMVS-LS		98.46 142	98.42 141	98.58 215	99.59 122	98.00 207	99.37 178	99.43 180	96.94 215	99.07 182	99.59 170	97.87 94	99.03 285	98.32 145	95.62 268	98.71 224
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		97.92 196	97.60 208	98.87 187	98.83 268	98.65 167	99.55 95	99.34 216	96.20 263	99.32 131	99.40 226	94.36 204	99.26 258	96.37 261	95.03 279	98.70 228

v1240		97.69 231	97.32 246	98.79 202	98.85 266	98.43 190	99.48 129	99.36 208	96.11 272	99.27 140	99.36 237	93.76 224	99.24 260	94.46 292	95.23 274	98.70 228

DTE-MVSNet		97.51 247	97.19 253	98.46 228	98.63 290	98.13 203	99.84 6	99.48 128	96.68 229	97.97 281	99.67 139	92.92 236	98.56 308	96.88 244	92.60 311	98.70 228

TranMVSNet+NR-MVSNet		97.93 193	97.66 203	98.76 205	98.78 273	98.62 170	99.65 53	99.49 118	97.76 137	98.49 255	99.60 168	94.23 208	98.97 298	98.00 165	92.90 305	98.70 228

v1921920		97.80 215	97.45 223	98.84 195	98.80 269	98.53 177	99.52 104	99.34 216	96.15 269	99.24 147	99.47 210	93.98 217	99.29 252	95.40 280	95.13 277	98.69 232

v1192		97.81 213	97.44 228	98.91 175	98.88 259	98.68 164	99.51 108	99.34 216	96.18 265	99.20 158	99.34 243	94.03 216	99.36 241	95.32 282	95.18 275	98.69 232

v2v482		98.06 175	97.77 191	98.92 171	98.90 257	98.82 155	99.57 81	99.36 208	96.65 231	99.19 161	99.35 240	94.20 209	99.25 259	97.72 189	94.97 280	98.69 232

UniMVSNet_NR-MVSNet		98.22 160	97.97 172	98.96 164	98.92 256	98.98 128	99.48 129	99.53 78	97.76 137	98.71 230	99.46 214	96.43 135	99.22 264	98.57 116	92.87 307	98.69 232

OurMVSNet-221017-0		97.88 199	97.77 191	98.19 249	98.71 283	96.53 263	99.88 1	99.00 266	97.79 134	98.78 224	99.94 3	91.68 267	99.35 244	97.21 222	96.99 244	98.69 232

gg-mvs-nofinetune		96.17 276	95.32 283	98.73 206	98.79 270	98.14 202	99.38 176	94.09 333	91.07 315	98.07 278	91.04 328	89.62 293	99.35 244	96.75 247	99.09 146	98.68 237

v1144		97.98 188	97.69 200	98.85 194	98.87 262	98.66 166	99.54 98	99.35 213	96.27 257	99.23 151	99.35 240	94.67 193	99.23 261	96.73 249	95.16 276	98.68 237

testing_2		94.44 290	92.93 295	98.98 161	94.16 324	99.00 127	99.42 155	99.28 237	96.60 236	84.86 322	96.84 318	70.91 327	99.27 256	98.23 148	96.08 259	98.68 237

DU-MVS		98.08 174	97.79 186	98.96 164	98.87 262	98.98 128	99.41 159	99.45 166	97.87 123	98.71 230	99.50 198	94.82 181	99.22 264	98.57 116	92.87 307	98.68 237

NR-MVSNet		97.97 191	97.61 207	99.02 156	98.87 262	99.26 96	99.47 134	99.42 182	97.63 151	97.08 296	99.50 198	95.07 175	99.13 274	97.86 175	93.59 298	98.68 237

LPG-MVS_test		98.22 160	98.13 157	98.49 223	99.33 180	97.05 240	99.58 76	99.55 60	97.46 166	99.24 147	99.83 41	92.58 249	99.72 177	98.09 156	97.51 222	98.68 237

LGP-MVS_train				98.49 223	99.33 180	97.05 240		99.55 60	97.46 166	99.24 147	99.83 41	92.58 249	99.72 177	98.09 156	97.51 222	98.68 237

LTVRE_ROB	97.16 12	98.02 182	97.90 179	98.40 234	99.23 202	96.80 255	99.70 33	99.60 38	97.12 199	98.18 272	99.70 121	91.73 266	99.72 177	98.39 135	97.45 229	98.68 237
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
IterMVS-SCA-FT		97.82 211	97.75 195	98.06 255	99.57 125	96.36 269	99.02 260	99.49 118	97.18 193	98.71 230	99.72 116	92.72 243	99.14 271	97.44 214	95.86 264	98.67 245

pm-mvs1		97.68 233	97.28 249	98.88 183	99.06 238	98.62 170	99.50 114	99.45 166	96.32 253	97.87 283	99.79 80	92.47 253	99.35 244	97.54 204	93.54 299	98.67 245

v10		97.85 204	97.52 214	98.86 191	98.99 248	98.67 165	99.75 25	99.41 184	95.70 280	98.98 199	99.41 224	94.75 189	99.23 261	96.01 266	94.63 284	98.67 245

HQP_MVS		98.27 159	98.22 153	98.44 232	99.29 192	96.97 247	99.39 171	99.47 144	98.97 26	99.11 173	99.61 165	92.71 245	99.69 192	97.78 181	97.63 211	98.67 245

plane_prior5								99.47 144					99.69 192	97.78 181	97.63 211	98.67 245

SixPastTwentyTwo		97.50 248	97.33 245	98.03 256	98.65 288	96.23 273	99.77 21	98.68 301	97.14 196	97.90 282	99.93 4	90.45 282	99.18 270	97.00 234	96.43 252	98.67 245

IterMVS		97.83 208	97.77 191	98.02 258	99.58 123	96.27 272	99.02 260	99.48 128	97.22 191	98.71 230	99.70 121	92.75 240	99.13 274	97.46 212	96.00 261	98.67 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	97.28 8	98.10 171	97.99 170	98.44 232	99.41 161	96.96 249	99.60 66	99.56 53	98.09 102	98.15 273	99.91 5	90.87 281	99.70 189	98.88 67	97.45 229	98.67 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v8		97.95 192	97.63 206	98.93 169	98.95 254	98.81 157	99.80 16	99.41 184	96.03 277	99.10 176	99.42 221	94.92 177	99.30 251	96.94 240	94.08 294	98.66 253

UniMVSNet (Re)		98.29 157	98.00 169	99.13 147	99.00 247	99.36 84	99.49 124	99.51 93	97.95 118	98.97 201	99.13 272	96.30 138	99.38 234	98.36 141	93.34 300	98.66 253

pmmvs6		96.53 269	96.09 271	97.82 273	98.69 285	95.47 286	99.37 178	99.47 144	93.46 304	97.41 290	99.78 86	87.06 312	99.33 247	96.92 242	92.70 310	98.65 255

K. test v3		97.10 262	96.79 261	98.01 259	98.72 281	96.33 270	99.87 4	97.05 323	97.59 153	96.16 305	99.80 70	88.71 299	99.04 283	96.69 252	96.55 250	98.65 255

our_test_3		97.65 238	97.68 201	97.55 283	98.62 291	94.97 296	98.84 290	99.30 231	96.83 222	98.19 271	99.34 243	97.01 116	99.02 286	95.00 287	96.01 260	98.64 257

YYNet1		95.36 285	94.51 290	97.92 265	97.89 307	97.10 234	99.10 244	99.23 243	93.26 306	80.77 327	99.04 281	92.81 239	98.02 312	94.30 293	94.18 292	98.64 257

MDA-MVSNet_test_wron		95.45 283	94.60 288	98.01 259	98.16 305	97.21 232	99.11 242	99.24 242	93.49 303	80.73 328	98.98 287	93.02 233	98.18 310	94.22 296	94.45 287	98.64 257

Baseline_NR-MVSNet		97.76 218	97.45 223	98.68 210	99.09 234	98.29 195	99.41 159	98.85 285	95.65 281	98.63 247	99.67 139	94.82 181	99.10 279	98.07 163	92.89 306	98.64 257

HQP4-MVS										98.66 239			99.64 202			98.64 257

HQP-MVS		98.02 182	97.90 179	98.37 236	99.19 210	96.83 252	98.98 271	99.39 194	98.24 82	98.66 239	99.40 226	92.47 253	99.64 202	97.19 224	97.58 216	98.64 257

ACMM	97.58 5	98.37 151	98.34 145	98.48 225	99.41 161	97.10 234	99.56 87	99.45 166	98.53 58	99.04 188	99.85 28	93.00 234	99.71 183	98.74 89	97.45 229	98.64 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs5		97.52 245	97.30 248	98.16 251	98.57 296	96.73 256	99.27 206	98.90 281	96.14 270	98.37 262	99.53 189	91.54 273	99.14 271	97.51 207	95.87 263	98.63 264

v148		97.79 216	97.55 210	98.50 222	98.74 278	97.72 221	99.54 98	99.33 222	96.26 258	98.90 209	99.51 195	94.68 192	99.14 271	97.83 177	93.15 304	98.63 264

MDA-MVSNet-bldmvs		94.96 287	93.98 292	97.92 265	98.24 304	97.27 228	99.15 233	99.33 222	93.80 299	80.09 329	99.03 282	88.31 305	97.86 317	93.49 302	94.36 289	98.62 266

TransMVSNet (Re)		97.15 260	96.58 263	98.86 191	99.12 227	98.85 150	99.49 124	98.91 279	95.48 282	97.16 295	99.80 70	93.38 228	99.11 277	94.16 297	91.73 313	98.62 266

lessismore_v0				97.79 275	98.69 285	95.44 288		94.75 331		95.71 308	99.87 20	88.69 300	99.32 248	95.89 267	94.93 282	98.62 266

MVSTER		98.49 140	98.32 147	99.00 159	99.35 175	99.02 123	99.54 98	99.38 200	97.41 174	99.20 158	99.73 112	93.86 221	99.36 241	98.87 71	97.56 218	98.62 266

GBi-Net		97.68 233	97.48 218	98.29 242	99.51 136	97.26 229	99.43 148	99.48 128	96.49 241	99.07 182	99.32 249	90.26 284	98.98 291	97.10 229	96.65 246	98.62 266

test1		97.68 233	97.48 218	98.29 242	99.51 136	97.26 229	99.43 148	99.48 128	96.49 241	99.07 182	99.32 249	90.26 284	98.98 291	97.10 229	96.65 246	98.62 266

FMVSNet1		96.84 264	96.36 267	98.29 242	99.32 187	97.26 229	99.43 148	99.48 128	95.11 285	98.55 252	99.32 249	83.95 319	98.98 291	95.81 269	96.26 256	98.62 266

ACMP	97.20 11	98.06 175	97.94 176	98.45 229	99.37 172	97.01 243	99.44 142	99.49 118	97.54 161	98.45 257	99.79 80	91.95 261	99.72 177	97.91 171	97.49 227	98.62 266
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	97.24 10	97.92 196	97.78 189	98.32 239	99.46 151	96.68 259	99.56 87	99.54 67	98.41 69	97.79 287	99.87 20	90.18 288	99.66 197	98.05 164	97.18 241	98.62 266

ppachtmachnet_test		97.49 250	97.45 223	97.61 280	98.62 291	95.24 290	98.80 293	99.46 154	96.11 272	98.22 270	99.62 161	96.45 133	98.97 298	93.77 299	95.97 262	98.61 275

OPM-MVS		98.19 164	98.10 159	98.45 229	98.88 259	97.07 238	99.28 202	99.38 200	98.57 56	99.22 152	99.81 59	92.12 259	99.66 197	98.08 160	97.54 220	98.61 275

WR-MVS_H		98.13 169	97.87 184	98.90 177	99.02 245	98.84 151	99.70 33	99.59 41	97.27 185	98.40 260	99.19 267	95.53 161	99.23 261	98.34 142	93.78 297	98.61 275

MIMVSNet1		95.51 282	95.04 285	96.92 294	97.38 313	95.60 280	99.52 104	99.50 110	93.65 301	96.97 299	99.17 268	85.28 317	96.56 326	88.36 320	95.55 270	98.60 278

N_pmnet		94.95 288	95.83 276	92.31 309	98.47 300	79.33 330	99.12 236	92.81 337	93.87 298	97.68 288	99.13 272	93.87 220	99.01 288	91.38 313	96.19 257	98.59 279

FMVSNet2		97.72 227	97.36 238	98.80 201	99.51 136	98.84 151	99.45 138	99.42 182	96.49 241	98.86 216	99.29 254	90.26 284	98.98 291	96.44 258	96.56 249	98.58 280

anonymousdsp		98.44 143	98.28 150	98.94 167	98.50 299	98.96 135	99.77 21	99.50 110	97.07 204	98.87 212	99.77 92	94.76 188	99.28 253	98.66 101	97.60 214	98.57 281

FMVSNet3		98.03 180	97.76 194	98.84 195	99.39 169	98.98 128	99.40 167	99.38 200	96.67 230	99.07 182	99.28 255	92.93 235	98.98 291	97.10 229	96.65 246	98.56 282

XVG-ACMP-BASELINE		97.83 208	97.71 199	98.20 248	99.11 229	96.33 270	99.41 159	99.52 83	98.06 110	99.05 187	99.50 198	89.64 292	99.73 173	97.73 187	97.38 235	98.53 283

Patchmtry		97.75 222	97.40 234	98.81 199	99.10 232	98.87 147	99.11 242	99.33 222	94.83 288	98.81 219	99.38 231	94.33 205	99.02 286	96.10 263	95.57 269	98.53 283

miper_lstm_enhance		98.00 186	97.91 178	98.28 245	99.34 179	97.43 225	98.88 286	99.36 208	96.48 245	98.80 221	99.55 183	95.98 145	98.91 301	97.27 219	95.50 271	98.51 285

USDC		97.34 255	97.20 252	97.75 276	99.07 236	95.20 291	98.51 311	99.04 264	97.99 116	98.31 266	99.86 23	89.02 296	99.55 215	95.67 274	97.36 236	98.49 286

CLD-MVS		98.16 167	98.10 159	98.33 238	99.29 192	96.82 254	98.75 298	99.44 173	97.83 129	99.13 169	99.55 183	92.92 236	99.67 194	98.32 145	97.69 210	98.48 287
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_0304		96.79 265	96.52 265	97.59 281	99.22 204	94.92 297	99.04 256	99.59 41	96.49 241	98.43 258	98.99 284	80.48 325	99.39 232	97.15 228	99.27 131	98.47 288

Anonymous20231206		96.22 274	96.03 272	96.79 297	97.31 316	94.14 306	99.63 56	99.08 258	96.17 266	97.04 297	99.06 279	93.94 218	97.76 319	86.96 324	95.06 278	98.47 288

FMVSNet5		96.43 272	96.19 269	97.15 288	99.11 229	95.89 278	99.32 192	99.52 83	94.47 295	98.34 265	99.07 277	87.54 310	97.07 323	92.61 310	95.72 266	98.47 288

pmmvs4		98.13 169	97.90 179	98.81 199	98.61 293	98.87 147	98.99 267	99.21 245	96.44 247	99.06 186	99.58 173	95.90 152	99.11 277	97.18 226	96.11 258	98.46 291

V42		98.06 175	97.79 186	98.86 191	98.98 251	98.84 151	99.69 35	99.34 216	96.53 240	99.30 133	99.37 234	94.67 193	99.32 248	97.57 201	94.66 283	98.42 292

PVSNet_BlendedMVS		98.86 111	98.80 106	99.03 155	99.76 47	98.79 158	99.28 202	99.91 3	97.42 173	99.67 50	99.37 234	97.53 102	99.88 107	98.98 56	97.29 237	98.42 292

UnsupCasMVSNet_eth		96.44 271	96.12 270	97.40 287	98.65 288	95.65 279	99.36 182	99.51 93	97.13 197	96.04 307	98.99 284	88.40 304	98.17 311	96.71 250	90.27 316	98.40 294

TinyColmap		97.12 261	96.89 259	97.83 272	99.07 236	95.52 285	98.57 308	98.74 293	97.58 155	97.81 286	99.79 80	88.16 307	99.56 213	95.10 284	97.21 239	98.39 295

thres100view900		97.76 218	97.45 223	98.69 209	99.72 70	97.86 215	99.59 69	98.74 293	97.93 120	99.26 144	98.62 301	91.75 264	99.83 134	93.22 304	98.18 195	98.37 296

tfpn200view9		97.72 227	97.38 236	98.72 207	99.69 84	97.96 210	99.50 114	98.73 298	97.83 129	99.17 165	98.45 307	91.67 268	99.83 134	93.22 304	98.18 195	98.37 296

tfpnnormal		97.84 206	97.47 220	98.98 161	99.20 208	99.22 100	99.64 55	99.61 34	96.32 253	98.27 269	99.70 121	93.35 229	99.44 226	95.69 272	95.40 272	98.27 298

test20.03		96.12 277	95.96 274	96.63 298	97.44 312	95.45 287	99.51 108	99.38 200	96.55 239	96.16 305	99.25 260	93.76 224	96.17 327	87.35 323	94.22 291	98.27 298

ITE_SJBPF				98.08 254	99.29 192	96.37 268		98.92 276	98.34 74	98.83 218	99.75 101	91.09 278	99.62 208	95.82 268	97.40 234	98.25 300

EG-PatchMatch MVS		95.97 279	95.69 278	96.81 296	97.78 309	92.79 316	99.16 230	98.93 273	96.16 267	94.08 313	99.22 264	82.72 321	99.47 219	95.67 274	97.50 224	98.17 301

D2MVS		98.41 147	98.50 138	98.15 252	99.26 199	96.62 261	99.40 167	99.61 34	97.71 143	98.98 199	99.36 237	96.04 144	99.67 194	98.70 94	97.41 233	98.15 302

TDRefinement		95.42 284	94.57 289	97.97 262	89.83 330	96.11 275	99.48 129	98.75 290	96.74 225	96.68 300	99.88 15	88.65 301	99.71 183	98.37 139	82.74 325	98.09 303

API-MVS		99.04 93	99.03 72	99.06 151	99.40 166	99.31 90	99.55 95	99.56 53	98.54 57	99.33 130	99.39 230	98.76 47	99.78 159	96.98 236	99.78 79	98.07 304

new_pmnet		96.38 273	96.03 272	97.41 286	98.13 306	95.16 294	99.05 251	99.20 246	93.94 297	97.39 291	98.79 295	91.61 272	99.04 283	90.43 315	95.77 265	98.05 305

thres200		97.61 240	97.28 249	98.62 212	99.64 104	98.03 205	99.26 214	98.74 293	97.68 146	99.09 180	98.32 309	91.66 270	99.81 146	92.88 308	98.22 191	98.03 306

DeepMVS_CX				93.34 307	99.29 192	82.27 327		99.22 244	85.15 321	96.33 303	99.05 280	90.97 280	99.73 173	93.57 301	97.77 209	98.01 307

GG-mvs-BLEND				98.45 229	98.55 297	98.16 200	99.43 148	93.68 334		97.23 293	98.46 306	89.30 295	99.22 264	95.43 279	98.22 191	97.98 308

pmmvs3		94.09 293	93.25 294	96.60 299	94.76 323	94.49 302	98.92 282	98.18 313	89.66 316	96.48 302	98.06 311	86.28 313	97.33 322	89.68 317	87.20 322	97.97 309

LF4IMVS		97.52 245	97.46 222	97.70 279	98.98 251	95.55 282	99.29 200	98.82 288	98.07 106	98.66 239	99.64 152	89.97 289	99.61 209	97.01 233	96.68 245	97.94 310

test_0402		96.64 266	96.24 268	97.85 270	98.85 266	96.43 267	99.44 142	99.26 239	93.52 302	96.98 298	99.52 192	88.52 303	99.20 269	92.58 311	97.50 224	97.93 311

MVP-Stereo		97.81 213	97.75 195	97.99 261	97.53 311	96.60 262	98.96 275	98.85 285	97.22 191	97.23 293	99.36 237	95.28 168	99.46 220	95.51 277	99.78 79	97.92 312
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MS-PatchMatch		97.24 259	97.32 246	96.99 291	98.45 301	93.51 313	98.82 292	99.32 228	97.41 174	98.13 274	99.30 252	88.99 297	99.56 213	95.68 273	99.80 75	97.90 313

test_normal		88.78 297	86.73 299	94.92 305	93.21 325	87.97 324	85.00 332	99.44 173	96.84 220	71.82 331	87.84 331	58.02 332	98.90 303	95.63 276	92.78 309	97.88 314

ambc				93.06 308	92.68 326	82.36 326	98.47 312	98.73 298		95.09 310	97.41 314	55.55 333	99.10 279	96.42 259	91.32 314	97.71 315

new-patchmatchnet		94.48 289	94.08 291	95.67 303	95.08 322	92.41 317	99.18 228	99.28 237	94.55 294	93.49 316	97.37 316	87.86 309	97.01 324	91.57 312	88.36 320	97.61 316

pmmvs-eth3d		95.34 286	94.73 287	97.15 288	95.53 321	95.94 277	99.35 187	99.10 255	95.13 284	93.55 315	97.54 313	88.15 308	97.91 315	94.58 290	89.69 319	97.61 316

UnsupCasMVSNet_bld		93.53 294	92.51 296	96.58 300	97.38 313	93.82 308	98.24 318	99.48 128	91.10 314	93.10 317	96.66 319	74.89 326	98.37 309	94.03 298	87.71 321	97.56 318

PM-MVS		92.96 295	92.23 297	95.14 304	95.61 319	89.98 323	99.37 178	98.21 311	94.80 289	95.04 311	97.69 312	65.06 329	97.90 316	94.30 293	89.98 318	97.54 319

LCM-MVSNet		86.80 299	85.22 302	91.53 311	87.81 331	80.96 328	98.23 320	98.99 267	71.05 327	90.13 321	96.51 320	48.45 336	96.88 325	90.51 314	85.30 324	96.76 320

OpenMVS_ROB	92.34 20	94.38 291	93.70 293	96.41 301	97.38 313	93.17 314	99.06 249	98.75 290	86.58 320	94.84 312	98.26 310	81.53 324	99.32 248	89.01 318	97.87 207	96.76 320

CMPMVS	69.68 23	94.13 292	94.90 286	91.84 310	97.24 317	80.01 329	98.52 310	99.48 128	89.01 317	91.99 319	99.67 139	85.67 316	99.13 274	95.44 278	97.03 243	96.39 322
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PMMVS2		86.87 298	85.37 301	91.35 312	90.21 329	83.80 325	98.89 285	97.45 322	83.13 324	91.67 320	95.03 321	48.49 335	94.70 329	85.86 326	77.62 327	95.54 323

tmp_tt		82.80 301	81.52 303	86.66 313	66.61 337	68.44 335	92.79 331	97.92 315	68.96 328	80.04 330	99.85 28	85.77 315	96.15 328	97.86 175	43.89 332	95.39 324

FPMVS		84.93 300	85.65 300	82.75 317	86.77 332	63.39 336	98.35 315	98.92 276	74.11 326	83.39 325	98.98 287	50.85 334	92.40 331	84.54 327	94.97 280	92.46 325

Gipuma		90.99 296	90.15 298	93.51 306	98.73 279	90.12 322	93.98 329	99.45 166	79.32 325	92.28 318	94.91 322	69.61 328	97.98 314	87.42 322	95.67 267	92.45 326
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		77.30 304	74.86 307	84.62 315	75.88 335	77.61 331	97.63 326	93.15 336	88.81 318	64.27 333	89.29 329	36.51 337	83.93 335	75.89 329	52.31 331	92.33 327

MVE	76.82 21	76.91 305	74.31 308	84.70 314	85.38 334	76.05 334	96.88 328	93.17 335	67.39 329	71.28 332	89.01 330	21.66 342	87.69 332	71.74 330	72.29 328	90.35 328
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	70.75 22	75.98 306	74.97 306	79.01 319	70.98 336	55.18 337	93.37 330	98.21 311	65.08 332	61.78 334	93.83 324	21.74 341	92.53 330	78.59 328	91.12 315	89.34 329
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		80.02 303	79.22 305	82.43 318	91.19 327	76.40 332	97.55 327	92.49 338	66.36 331	83.01 326	91.27 327	64.63 330	85.79 334	65.82 332	60.65 330	85.08 330

E-PMN		80.61 302	79.88 304	82.81 316	90.75 328	76.38 333	97.69 325	95.76 330	66.44 330	83.52 324	92.25 326	62.54 331	87.16 333	68.53 331	61.40 329	84.89 331

test123		39.01 309	42.50 310	28.53 321	39.17 338	20.91 339	98.75 298	19.17 341	19.83 335	38.57 335	66.67 333	33.16 338	15.42 337	37.50 335	29.66 334	49.26 332

testmvs		39.17 308	43.78 309	25.37 322	36.04 339	16.84 340	98.36 314	26.56 339	20.06 334	38.51 336	67.32 332	29.64 339	15.30 338	37.59 334	39.90 333	43.98 333

wuyk23d		40.18 307	41.29 311	36.84 320	86.18 333	49.12 338	79.73 333	22.81 340	27.64 333	25.46 337	28.45 337	21.98 340	48.89 336	55.80 333	23.56 335	12.51 334

test_part1				0.00 323		0.00 341	0.00 334	99.48 128				0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

cdsmvs_eth3d_5k		24.64 310	32.85 312	0.00 323	0.00 340	0.00 341	0.00 334	99.51 93	0.00 336	0.00 338	99.56 180	96.58 129	0.00 339	0.00 336	0.00 336	0.00 335

pcd_1.5k_mvsjas		8.27 312	11.03 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.27 338	99.01 13	0.00 339	0.00 336	0.00 336	0.00 335

sosnet-low-res		0.02 313	0.03 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.27 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sosnet		0.02 313	0.03 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.27 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uncertanet		0.02 313	0.03 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.27 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

Regformer		0.02 313	0.03 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.27 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

ab-mvs-re		8.30 311	11.06 313	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	99.58 173	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uanet		0.02 313	0.03 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.27 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

9.14			99.10 62		99.72 70		99.40 167	99.51 93	97.53 162	99.64 60	99.78 86	98.84 36	99.91 81	97.63 195	99.82 71

save fliter					99.76 47	99.59 54	99.14 235	99.40 190	99.00 19

test0726					99.85 24	99.89 1	99.62 59	99.50 110	99.10 8	99.86 8	99.82 48	98.94 27

test_part2					99.81 36	99.83 11				99.77 29

sam_mvs												94.72 191

MTGPA								99.47 144

test_post1							99.23 219				65.14 335	94.18 212	99.71 183	97.58 198

test_post											65.99 334	94.65 195	99.73 173

patchmatchnet-post											98.70 299	94.79 183	99.74 166

MTMP							99.54 98	98.88 283

gm-plane-assit					98.54 298	92.96 315			94.65 292		99.15 270		99.64 202	97.56 202

TEST9					99.67 88	99.65 46	99.05 251	99.41 184	96.22 262	98.95 202	99.49 201	98.77 45	99.91 81

test_8					99.67 88	99.61 51	99.03 257	99.41 184	96.28 255	98.93 205	99.48 207	98.76 47	99.91 81

agg_prior					99.67 88	99.62 49		99.40 190		98.87 212			99.91 81

test_prior4						99.56 58	98.99 267

test_prior2							98.96 275		98.34 74	99.01 191	99.52 192	98.68 56		97.96 167	99.74 87

旧先验2							98.96 275		96.70 228	99.47 94			99.94 47	98.19 149

新几何2							99.01 265

原ACMM2							98.95 279

testdata2													99.95 38	96.67 253

segment_acmp												98.96 22

testdata1							98.85 289		98.32 78

plane_prior7					99.29 192	97.03 242

plane_prior6					99.27 197	96.98 246						92.71 245

plane_prior4											99.61 165

plane_prior3						97.00 244			98.69 50	99.11 173

plane_prior2							99.39 171		98.97 26

plane_prior1					99.26 199

plane_prior						96.97 247	99.21 225		98.45 65						97.60 214

n2								0.00 342

nn								0.00 342

door-mid								98.05 314

test11								99.35 213

door								97.92 315

HQP5-MVS						96.83 252

HQP-NCC					99.19 210		98.98 271		98.24 82	98.66 239

ACMP_Plane					99.19 210		98.98 271		98.24 82	98.66 239

BP-MVS														97.19 224

HQP3-MVS								99.39 194							97.58 216

HQP2-MVS												92.47 253

NP-MVS					99.23 202	96.92 250					99.40 226

MDTV_nov1_ep13			98.32 147		99.11 229	94.44 303	99.27 206	98.74 293	97.51 164	99.40 114	99.62 161	94.78 184	99.76 164	97.59 197	98.81 168

ACMMP++_ref															97.19 240

ACMMP++															97.43 232

Test By Simon												98.75 50

High-resolution multi-view benchmark