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 41	99.89 1	99.75 34	99.56 48	99.02 10	99.88 3	99.85 26	99.18 5	99.96 19	99.22 31	99.92 12	99.90 1

UA-Net		99.42 29	99.29 36	99.80 30	99.62 112	99.55 53	99.50 133	99.70 15	98.79 40	99.77 23	99.96 1	97.45 93	99.96 19	98.92 55	99.90 24	99.89 2

CHOSEN 1792x2688		99.19 56	99.10 56	99.45 95	99.89 8	98.52 191	99.39 181	99.94 1	98.73 44	99.11 173	99.89 10	95.50 145	99.94 42	99.50 8	99.97 3	99.89 2

DP-MVS		99.16 61	98.95 76	99.78 34	99.77 41	99.53 57	99.41 174	99.50 99	97.03 202	99.04 188	99.88 14	97.39 94	99.92 65	98.66 85	99.90 24	99.87 4

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 63	99.78 36	99.14 99	99.60 90	99.45 149	99.01 13	99.90 1	99.83 37	98.98 18	99.93 57	99.59 2	99.95 6	99.86 5

Test_1112_low_res		98.89 97	98.66 111	99.57 73	99.69 88	98.95 130	99.03 274	99.47 129	96.98 204	99.15 168	99.23 257	96.77 112	99.89 94	98.83 68	98.78 154	99.86 5

HyFIR lowres test		99.11 72	98.92 78	99.65 58	99.90 3	99.37 75	99.02 277	99.91 3	97.67 141	99.59 63	99.75 92	95.90 135	99.73 168	99.53 6	99.02 134	99.86 5

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 63	99.78 36	99.15 98	99.61 88	99.45 149	99.01 13	99.89 2	99.82 44	99.01 11	99.92 65	99.56 5	99.95 6	99.85 8

CVMVSNet		98.57 128	98.67 108	98.30 245	99.35 165	95.59 290	99.50 133	99.55 55	98.60 51	99.39 105	99.83 37	94.48 199	99.45 214	98.75 74	98.56 163	99.85 8

HPM-MVS_fast		99.51 12	99.40 14	99.85 18	99.91 1	99.79 18	99.76 27	99.56 48	97.72 135	99.76 28	99.75 92	99.13 6	99.92 65	99.07 44	99.92 12	99.85 8

MG-MVS		99.13 63	99.02 67	99.45 95	99.57 123	98.63 179	99.07 262	99.34 205	98.99 18	99.61 58	99.82 44	97.98 82	99.87 103	97.00 221	99.80 70	99.85 8

ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 150	99.48 113	98.05 98	99.76 28	99.86 22	98.82 34	99.93 57	98.82 71	99.91 17	99.84 12

HFP-MVS		99.49 13	99.37 17	99.86 13	99.87 15	99.80 14	99.66 65	99.67 22	98.15 80	99.68 37	99.69 114	99.06 8	99.96 19	98.69 82	99.87 38	99.84 12

region2R		99.48 17	99.35 22	99.87 6	99.88 11	99.80 14	99.65 75	99.66 25	98.13 82	99.66 48	99.68 119	98.96 20	99.96 19	98.62 90	99.87 38	99.84 12

#test#		99.43 27	99.29 36	99.86 13	99.87 15	99.80 14	99.55 117	99.67 22	97.83 122	99.68 37	99.69 114	99.06 8	99.96 19	98.39 114	99.87 38	99.84 12

Regformer-4		99.59 2	99.54 4	99.73 46	99.76 44	99.41 72	99.58 99	99.49 104	99.02 10	99.88 3	99.80 64	99.00 17	99.94 42	99.45 15	99.92 12	99.84 12

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 7	99.69 45	99.68 19	98.98 19	99.37 109	99.74 97	98.81 35	99.94 42	98.79 72	99.86 48	99.84 12

X-MVStestdata		96.55 269	95.45 290	99.87 6	99.85 23	99.83 7	99.69 45	99.68 19	98.98 19	99.37 109	64.01 355	98.81 35	99.94 42	98.79 72	99.86 48	99.84 12

ACMMPR		99.49 13	99.36 19	99.86 13	99.87 15	99.79 18	99.66 65	99.67 22	98.15 80	99.67 43	99.69 114	98.95 25	99.96 19	98.69 82	99.87 38	99.84 12

HPM-MVS		99.42 29	99.28 38	99.83 23	99.90 3	99.72 27	99.81 15	99.54 62	97.59 144	99.68 37	99.63 141	98.91 28	99.94 42	98.58 95	99.91 17	99.84 12

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 13	99.59 92	99.51 85	98.62 49	99.79 18	99.83 37	99.28 3	99.97 11	98.48 108	99.90 24	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
1112_ss		98.98 91	98.77 98	99.59 69	99.68 91	99.02 116	99.25 228	99.48 113	97.23 178	99.13 169	99.58 157	96.93 107	99.90 86	98.87 61	98.78 154	99.84 12

test_part1								99.48 113				98.96 20			99.84 57	99.83 23

ESAPD		99.31 44	99.13 52	99.87 6	99.81 32	99.83 7	99.37 188	99.48 113	97.97 108	99.77 23	99.78 77	98.96 20	99.95 33	97.15 212	99.84 57	99.83 23

MP-MVS-pluss		99.37 37	99.20 46	99.88 4	99.90 3	99.87 2	99.30 208	99.52 76	97.18 181	99.60 60	99.79 72	98.79 37	99.95 33	98.83 68	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MPTG		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 194	99.47 129	98.79 40	99.68 37	99.81 53	98.43 63	99.97 11	98.88 57	99.90 24	99.83 23

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 65	99.47 129	98.79 40	99.68 37	99.81 53	98.43 63	99.97 11	98.88 57	99.90 24	99.83 23

Regformer-3		99.57 6	99.53 5	99.68 51	99.76 44	99.29 83	99.58 99	99.44 157	99.01 13	99.87 6	99.80 64	98.97 19	99.91 74	99.44 16	99.92 12	99.83 23

PGM-MVS		99.45 22	99.31 31	99.86 13	99.87 15	99.78 22	99.58 99	99.65 30	97.84 121	99.71 31	99.80 64	99.12 7	99.97 11	98.33 121	99.87 38	99.83 23

mPP-MVS		99.44 25	99.30 33	99.86 13	99.88 11	99.79 18	99.69 45	99.48 113	98.12 84	99.50 83	99.75 92	98.78 38	99.97 11	98.57 97	99.89 32	99.83 23

CP-MVS		99.45 22	99.32 26	99.85 18	99.83 28	99.75 23	99.69 45	99.52 76	98.07 93	99.53 78	99.63 141	98.93 27	99.97 11	98.74 75	99.91 17	99.83 23

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 28	99.91 1	99.66 36	99.63 79	99.39 179	98.91 29	99.78 22	99.85 26	99.36 2	99.94 42	98.84 66	99.88 34	99.82 32

MP-MVS		99.33 41	99.15 50	99.87 6	99.88 11	99.82 12	99.66 65	99.46 138	98.09 89	99.48 87	99.74 97	98.29 72	99.96 19	97.93 148	99.87 38	99.82 32

MCST-MVS		99.43 27	99.30 33	99.82 25	99.79 35	99.74 26	99.29 212	99.40 176	98.79 40	99.52 80	99.62 146	98.91 28	99.90 86	98.64 87	99.75 79	99.82 32

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 36	99.63 108	99.59 48	99.36 194	99.46 138	99.07 9	99.79 18	99.82 44	98.85 32	99.92 65	98.68 84	99.87 38	99.82 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HSP-MVS		99.41 32	99.26 43	99.85 18	99.89 8	99.80 14	99.67 56	99.37 192	98.70 45	99.77 23	99.49 189	98.21 75	99.95 33	98.46 111	99.77 76	99.81 36

CPTT-MVS		99.11 72	98.90 81	99.74 44	99.80 34	99.46 67	99.59 92	99.49 104	97.03 202	99.63 53	99.69 114	97.27 99	99.96 19	97.82 156	99.84 57	99.81 36

ACMMP		99.45 22	99.32 26	99.82 25	99.89 8	99.67 34	99.62 82	99.69 18	98.12 84	99.63 53	99.84 35	98.73 48	99.96 19	98.55 103	99.83 63	99.81 36
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	98.81 110	99.37 17	97.12 300	99.60 118	91.75 328	98.61 319	99.44 157	99.35 1	99.83 11	99.85 26	98.70 50	99.81 138	99.02 48	99.91 17	99.81 36

3Dnovator+	97.12 13	99.18 58	98.97 72	99.82 25	99.17 206	99.68 32	99.81 15	99.51 85	99.20 4	98.72 228	99.89 10	95.68 142	99.97 11	98.86 64	99.86 48	99.81 36

Regformer-1		99.53 9	99.47 8	99.72 48	99.71 81	99.44 69	99.49 141	99.46 138	98.95 24	99.83 11	99.76 87	99.01 11	99.93 57	99.17 36	99.87 38	99.80 41

Regformer-2		99.54 7	99.47 8	99.75 39	99.71 81	99.52 60	99.49 141	99.49 104	98.94 26	99.83 11	99.76 87	99.01 11	99.94 42	99.15 38	99.87 38	99.80 41

APD-MVS		99.27 50	99.08 57	99.84 22	99.75 56	99.79 18	99.50 133	99.50 99	97.16 183	99.77 23	99.82 44	98.78 38	99.94 42	97.56 183	99.86 48	99.80 41
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		99.34 40	99.19 47	99.79 33	99.61 116	99.65 39	99.30 208	99.48 113	98.86 31	99.21 157	99.63 141	98.72 49	99.90 86	98.25 125	99.63 102	99.80 41

HPM-MVS++		99.39 36	99.23 45	99.87 6	99.75 56	99.84 6	99.43 163	99.51 85	98.68 47	99.27 135	99.53 176	98.64 54	99.96 19	98.44 113	99.80 70	99.79 45

abl_6		99.44 25	99.31 31	99.83 23	99.85 23	99.75 23	99.66 65	99.59 38	98.13 82	99.82 14	99.81 53	98.60 56	99.96 19	98.46 111	99.88 34	99.79 45

PVSNet_Blended_VisFu		99.36 38	99.28 38	99.61 67	99.86 20	99.07 105	99.47 150	99.93 2	97.66 142	99.71 31	99.86 22	97.73 88	99.96 19	99.47 13	99.82 67	99.79 45

3Dnovator	97.25 9	99.24 54	99.05 59	99.81 28	99.12 214	99.66 36	99.84 9	99.74 10	99.09 8	98.92 206	99.90 7	95.94 133	99.98 5	98.95 53	99.92 12	99.79 45

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 18	99.76 44	99.83 7	99.63 79	99.54 62	98.36 65	99.79 18	99.82 44	98.86 31	99.95 33	98.62 90	99.81 68	99.78 49

CDPH-MVS		99.13 63	98.91 80	99.80 30	99.75 56	99.71 28	99.15 247	99.41 169	96.60 227	99.60 60	99.55 166	98.83 33	99.90 86	97.48 191	99.83 63	99.78 49

SD-MVS		99.41 32	99.52 6	99.05 145	99.74 67	99.68 32	99.46 153	99.52 76	99.11 7	99.88 3	99.91 5	99.43 1	97.70 329	98.72 79	99.93 11	99.77 51

CNVR-MVS		99.42 29	99.30 33	99.78 34	99.62 112	99.71 28	99.26 226	99.52 76	98.82 35	99.39 105	99.71 105	98.96 20	99.85 112	98.59 94	99.80 70	99.77 51

MVS_111021_HR		99.41 32	99.32 26	99.66 54	99.72 75	99.47 66	98.95 296	99.85 6	98.82 35	99.54 77	99.73 100	98.51 58	99.74 160	98.91 56	99.88 34	99.77 51

QAPM		98.67 123	98.30 136	99.80 30	99.20 196	99.67 34	99.77 24	99.72 11	94.74 288	98.73 227	99.90 7	95.78 139	99.98 5	96.96 225	99.88 34	99.76 54

test9_res														97.49 190	99.72 85	99.75 55

train_agg		99.02 86	98.77 98	99.77 36	99.67 92	99.65 39	99.05 268	99.41 169	96.28 251	98.95 202	99.49 189	98.76 43	99.91 74	97.63 176	99.72 85	99.75 55

agg_prior3		98.97 93	98.71 104	99.75 39	99.67 92	99.60 46	99.04 273	99.41 169	95.93 273	98.87 212	99.48 195	98.61 55	99.91 74	97.63 176	99.72 85	99.75 55

agg_prior1		99.01 89	98.76 100	99.76 38	99.67 92	99.62 42	98.99 283	99.40 176	96.26 254	98.87 212	99.49 189	98.77 41	99.91 74	97.69 173	99.72 85	99.75 55

agg_prior2														97.21 206	99.73 84	99.75 55

test_prior3		99.21 55	99.05 59	99.68 51	99.67 92	99.48 64	98.96 292	99.56 48	98.34 66	99.01 191	99.52 181	98.68 51	99.83 125	97.96 145	99.74 81	99.74 60

test_prior				99.68 51	99.67 92	99.48 64		99.56 48					99.83 125			99.74 60

test12				99.75 39	99.64 105	99.61 44		99.29 226		99.21 157		98.38 67	99.89 94		99.74 81	99.74 60

114514_t		98.93 95	98.67 108	99.72 48	99.85 23	99.53 57	99.62 82	99.59 38	92.65 319	99.71 31	99.78 77	98.06 80	99.90 86	98.84 66	99.91 17	99.74 60

Vis-MVSNet		99.12 68	98.97 72	99.56 75	99.78 36	99.10 102	99.68 54	99.66 25	98.49 56	99.86 7	99.87 19	94.77 185	99.84 118	99.19 33	99.41 109	99.74 60
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
旧先验1					99.74 67	99.59 48		99.54 62			99.69 114	98.47 60			99.68 95	99.73 65

1121		99.09 76	98.87 85	99.75 39	99.74 67	99.60 46	99.27 218	99.48 113	96.82 214	99.25 143	99.65 130	98.38 67	99.93 57	97.53 186	99.67 96	99.73 65

EPNet		98.86 101	98.71 104	99.30 114	97.20 326	98.18 206	99.62 82	98.91 280	99.28 2	98.63 246	99.81 53	95.96 130	99.99 1	99.24 30	99.72 85	99.73 65
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		99.05 82	98.87 85	99.57 73	99.73 72	99.32 79	99.75 34	99.20 246	98.02 102	99.56 68	99.86 22	96.54 118	99.67 189	98.09 134	99.13 125	99.73 65

F-COLMAP		99.19 56	99.04 62	99.64 63	99.78 36	99.27 86	99.42 170	99.54 62	97.29 172	99.41 100	99.59 154	98.42 66	99.93 57	98.19 127	99.69 92	99.73 65

DeepC-MVS	98.35 2	99.30 45	99.19 47	99.64 63	99.82 29	99.23 90	99.62 82	99.55 55	98.94 26	99.63 53	99.95 2	95.82 138	99.94 42	99.37 17	99.97 3	99.73 65
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 39	99.75 56	99.59 48		99.54 62	96.76 215	99.29 127	99.64 137	98.43 63	99.94 42	96.92 229	99.66 97	99.72 71

无先验							98.99 283	99.51 85	96.89 210				99.93 57	97.53 186		99.72 71

test222					99.75 56	99.49 63	98.91 301	99.49 104	96.42 242	99.34 119	99.65 130	98.28 73			99.69 92	99.72 71

testdata				99.54 76	99.75 56	98.95 130		99.51 85	97.07 198	99.43 95	99.70 108	98.87 30	99.94 42	97.76 163	99.64 100	99.72 71

VNet		99.11 72	98.90 81	99.73 46	99.52 129	99.56 51	99.41 174	99.39 179	99.01 13	99.74 30	99.78 77	95.56 143	99.92 65	99.52 7	98.18 183	99.72 71

WTY-MVS		99.06 80	98.88 84	99.61 67	99.62 112	99.16 95	99.37 188	99.56 48	98.04 99	99.53 78	99.62 146	96.84 108	99.94 42	98.85 65	98.49 167	99.72 71

CSCG		99.32 42	99.32 26	99.32 110	99.85 23	98.29 202	99.71 41	99.66 25	98.11 86	99.41 100	99.80 64	98.37 69	99.96 19	98.99 50	99.96 5	99.72 71

原ACMM1				99.65 58	99.73 72	99.33 78		99.47 129	97.46 156	99.12 171	99.66 129	98.67 53	99.91 74	97.70 172	99.69 92	99.71 78

LFMVS		97.90 198	97.35 238	99.54 76	99.52 129	99.01 118	99.39 181	98.24 322	97.10 191	99.65 51	99.79 72	84.79 333	99.91 74	99.28 27	98.38 171	99.69 79

EPNet_dtu		98.03 177	97.96 158	98.23 256	98.27 310	95.54 293	99.23 231	98.75 295	99.02 10	97.82 283	99.71 105	96.11 129	99.48 211	93.04 313	99.65 99	99.69 79
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PAPM_NR		99.04 83	98.84 91	99.66 54	99.74 67	99.44 69	99.39 181	99.38 185	97.70 138	99.28 131	99.28 251	98.34 70	99.85 112	96.96 225	99.45 106	99.69 79

EPP-MVSNet		99.13 63	98.99 69	99.53 80	99.65 104	99.06 106	99.81 15	99.33 213	97.43 160	99.60 60	99.88 14	97.14 101	99.84 118	99.13 39	98.94 141	99.69 79

sss		99.17 59	99.05 59	99.53 80	99.62 112	98.97 125	99.36 194	99.62 31	97.83 122	99.67 43	99.65 130	97.37 97	99.95 33	99.19 33	99.19 122	99.68 83

PHI-MVS		99.30 45	99.17 49	99.70 50	99.56 126	99.52 60	99.58 99	99.80 8	97.12 187	99.62 56	99.73 100	98.58 57	99.90 86	98.61 92	99.91 17	99.68 83

PVSNet_0	94.43 19	96.09 287	95.47 289	97.94 274	99.31 177	94.34 312	97.81 338	99.70 15	97.12 187	97.46 287	98.75 295	89.71 300	99.79 145	97.69 173	81.69 342	99.68 83

TAMVS		99.12 68	99.08 57	99.24 127	99.46 143	98.55 186	99.51 128	99.46 138	98.09 89	99.45 91	99.82 44	98.34 70	99.51 210	98.70 80	98.93 142	99.67 86

CHOSEN 280x420		99.12 68	99.13 52	99.08 141	99.66 102	97.89 218	98.43 326	99.71 13	98.88 30	99.62 56	99.76 87	96.63 116	99.70 184	99.46 14	99.99 1	99.66 87

CDS-MVSNet		99.09 76	99.03 64	99.25 124	99.42 149	98.73 169	99.45 154	99.46 138	98.11 86	99.46 90	99.77 84	98.01 81	99.37 228	98.70 80	98.92 144	99.66 87
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		98.63 127	98.34 132	99.51 86	99.40 157	99.03 115	98.80 307	99.36 193	96.33 247	99.00 198	99.12 267	98.46 61	99.84 118	95.23 277	99.37 114	99.66 87

CANet		99.25 53	99.14 51	99.59 69	99.41 152	99.16 95	99.35 198	99.57 44	98.82 35	99.51 82	99.61 149	96.46 119	99.95 33	99.59 2	99.98 2	99.65 90

TSAR-MVS + GP.		99.36 38	99.36 19	99.36 105	99.67 92	98.61 184	99.07 262	99.33 213	99.00 17	99.82 14	99.81 53	99.06 8	99.84 118	99.09 42	99.42 108	99.65 90

MVSFormer		99.17 59	99.12 54	99.29 117	99.51 131	98.94 133	99.88 1	99.46 138	97.55 149	99.80 16	99.65 130	97.39 94	99.28 251	99.03 46	99.85 52	99.65 90

jason		99.13 63	99.03 64	99.45 95	99.46 143	98.87 141	99.12 251	99.26 239	98.03 101	99.79 18	99.65 130	97.02 104	99.85 112	99.02 48	99.90 24	99.65 90
jason: jason.
PLC	97.94 4	99.02 86	98.85 90	99.53 80	99.66 102	99.01 118	99.24 230	99.52 76	96.85 212	99.27 135	99.48 195	98.25 74	99.91 74	97.76 163	99.62 103	99.65 90
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	97.07 15	97.74 225	97.34 241	98.94 158	99.70 86	97.53 233	99.25 228	99.51 85	91.90 323	99.30 123	99.63 141	98.78 38	99.64 195	88.09 330	99.87 38	99.65 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_0304		99.06 80	98.86 88	99.66 54	99.51 131	99.36 76	99.22 235	99.51 85	98.95 24	99.58 64	99.65 130	93.74 227	99.98 5	99.66 1	99.95 6	99.64 96

LCM-MVSNet-Re		97.83 206	98.15 141	96.87 305	99.30 178	92.25 327	99.59 92	98.26 321	97.43 160	96.20 303	99.13 264	96.27 125	98.73 305	98.17 129	98.99 136	99.64 96

BH-RMVSNet		98.41 135	98.08 148	99.40 103	99.41 152	98.83 148	99.30 208	98.77 294	97.70 138	98.94 204	99.65 130	92.91 238	99.74 160	96.52 251	99.55 104	99.64 96

MVS_111021_LR		99.41 32	99.33 25	99.65 58	99.77 41	99.51 62	98.94 298	99.85 6	98.82 35	99.65 51	99.74 97	98.51 58	99.80 142	98.83 68	99.89 32	99.64 96

MVS		97.28 258	96.55 265	99.48 89	98.78 280	98.95 130	99.27 218	99.39 179	83.53 339	98.08 273	99.54 169	96.97 105	99.87 103	94.23 300	99.16 123	99.63 100

MSLP-MVS++		99.46 21	99.47 8	99.44 98	99.60 118	99.16 95	99.41 174	99.71 13	98.98 19	99.45 91	99.78 77	99.19 4	99.54 209	99.28 27	99.84 57	99.63 100

GA-MVS		97.85 202	97.47 218	99.00 150	99.38 160	97.99 213	98.57 321	99.15 251	97.04 201	98.90 209	99.30 248	89.83 299	99.38 225	96.70 244	98.33 172	99.62 102

Vis-MVSNet (Re-imp)		98.87 98	98.72 102	99.31 111	99.71 81	98.88 140	99.80 19	99.44 157	97.91 115	99.36 113	99.78 77	95.49 146	99.43 223	97.91 149	99.11 126	99.62 102

VDD-MVS		97.73 226	97.35 238	98.88 183	99.47 142	97.12 243	99.34 201	98.85 286	98.19 76	99.67 43	99.85 26	82.98 337	99.92 65	99.49 12	98.32 173	99.60 104

DELS-MVS		99.48 17	99.42 11	99.65 58	99.72 75	99.40 74	99.05 268	99.66 25	99.14 6	99.57 67	99.80 64	98.46 61	99.94 42	99.57 4	99.84 57	99.60 104
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 78	98.97 72	99.42 102	99.76 44	98.79 164	98.78 308	99.91 3	96.74 216	99.67 43	99.49 189	97.53 91	99.88 101	98.98 51	99.85 52	99.60 104

OMC-MVS		99.08 78	99.04 62	99.20 131	99.67 92	98.22 205	99.28 215	99.52 76	98.07 93	99.66 48	99.81 53	97.79 86	99.78 153	97.79 159	99.81 68	99.60 104

AllTest		98.87 98	98.72 102	99.31 111	99.86 20	98.48 196	99.56 112	99.61 32	97.85 119	99.36 113	99.85 26	95.95 131	99.85 112	96.66 247	99.83 63	99.59 108

TestCases				99.31 111	99.86 20	98.48 196		99.61 32	97.85 119	99.36 113	99.85 26	95.95 131	99.85 112	96.66 247	99.83 63	99.59 108

lupinMVS		99.13 63	99.01 68	99.46 94	99.51 131	98.94 133	99.05 268	99.16 250	97.86 117	99.80 16	99.56 163	97.39 94	99.86 106	98.94 54	99.85 52	99.58 110

RPSCF		98.22 149	98.62 116	96.99 301	99.82 29	91.58 329	99.72 39	99.44 157	96.61 225	99.66 48	99.89 10	95.92 134	99.82 134	97.46 194	99.10 128	99.57 111

DSMNet-mixed		97.25 259	97.35 238	96.95 303	97.84 315	93.61 320	99.57 105	96.63 345	96.13 267	98.87 212	98.61 302	94.59 194	97.70 329	95.08 279	98.86 149	99.55 112

AdaColmap		99.01 89	98.80 95	99.66 54	99.56 126	99.54 54	99.18 242	99.70 15	98.18 79	99.35 116	99.63 141	96.32 123	99.90 86	97.48 191	99.77 76	99.55 112

alignmvs		98.81 110	98.56 123	99.58 72	99.43 148	99.42 71	99.51 128	98.96 273	98.61 50	99.35 116	98.92 282	94.78 181	99.77 155	99.35 18	98.11 199	99.54 114

PatchmatchNet		98.31 141	98.36 130	98.19 261	99.16 208	95.32 298	99.27 218	98.92 277	97.37 166	99.37 109	99.58 157	94.90 173	99.70 184	97.43 197	99.21 120	99.54 114
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PVSNet	96.02 17	98.85 107	98.84 91	98.89 176	99.73 72	97.28 236	98.32 330	99.60 35	97.86 117	99.50 83	99.57 161	96.75 113	99.86 106	98.56 100	99.70 91	99.54 114

MSDG		98.98 91	98.80 95	99.53 80	99.76 44	99.19 92	98.75 311	99.55 55	97.25 175	99.47 88	99.77 84	97.82 85	99.87 103	96.93 228	99.90 24	99.54 114

UGNet		98.87 98	98.69 106	99.40 103	99.22 193	98.72 171	99.44 158	99.68 19	99.24 3	99.18 165	99.42 210	92.74 242	99.96 19	99.34 22	99.94 10	99.53 118
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																99.52 119

sam_mvs1												94.86 176				99.52 119

Patchmatch-test		97.93 193	97.65 201	98.77 204	99.18 201	97.07 248	99.03 274	99.14 253	96.16 263	98.74 226	99.57 161	94.56 195	99.72 172	93.36 308	99.11 126	99.52 119

PMMVS		98.80 113	98.62 116	99.34 106	99.27 186	98.70 172	98.76 310	99.31 220	97.34 167	99.21 157	99.07 269	97.20 100	99.82 134	98.56 100	98.87 148	99.52 119

LS3D		99.27 50	99.12 54	99.74 44	99.18 201	99.75 23	99.56 112	99.57 44	98.45 59	99.49 86	99.85 26	97.77 87	99.94 42	98.33 121	99.84 57	99.52 119

Effi-MVS+		98.81 110	98.59 121	99.48 89	99.46 143	99.12 101	98.08 336	99.50 99	97.50 154	99.38 107	99.41 213	96.37 122	99.81 138	99.11 41	98.54 164	99.51 124

Patchmatch-RL test		95.84 289	95.81 278	95.95 312	95.61 329	90.57 330	98.24 332	98.39 318	95.10 284	95.20 309	98.67 297	94.78 181	97.77 327	96.28 257	90.02 322	99.51 124

mvs_anonymous		99.03 85	98.99 69	99.16 133	99.38 160	98.52 191	99.51 128	99.38 185	97.79 127	99.38 107	99.81 53	97.30 98	99.45 214	99.35 18	98.99 136	99.51 124

Patchmatch-test1		98.16 158	98.14 142	98.22 258	99.30 178	95.55 291	99.07 262	98.97 271	97.57 147	99.43 95	99.60 152	92.72 243	99.60 203	97.38 199	99.20 121	99.50 127

test_normal		97.44 253	96.77 263	99.44 98	97.75 318	99.00 120	99.10 259	98.64 311	97.71 136	93.93 322	98.82 290	87.39 323	99.83 125	98.61 92	98.97 138	99.49 128

ab-mvs		98.86 101	98.63 113	99.54 76	99.64 105	99.19 92	99.44 158	99.54 62	97.77 129	99.30 123	99.81 53	94.20 208	99.93 57	99.17 36	98.82 151	99.49 128

ADS-MVSNet2		98.02 179	98.07 150	97.87 279	99.33 169	95.19 301	99.23 231	99.08 258	96.24 256	99.10 176	99.67 123	94.11 213	98.93 300	96.81 238	99.05 132	99.48 130

ADS-MVSNet		98.20 154	98.08 148	98.56 221	99.33 169	96.48 274	99.23 231	99.15 251	96.24 256	99.10 176	99.67 123	94.11 213	99.71 178	96.81 238	99.05 132	99.48 130

tpm		97.67 237	97.55 207	98.03 267	99.02 232	95.01 304	99.43 163	98.54 317	96.44 240	99.12 171	99.34 240	91.83 273	99.60 203	97.75 165	96.46 250	99.48 130

CNLPA		99.14 62	98.99 69	99.59 69	99.58 121	99.41 72	99.16 244	99.44 157	98.45 59	99.19 163	99.49 189	98.08 79	99.89 94	97.73 167	99.75 79	99.48 130

canonicalmvs		99.02 86	98.86 88	99.51 86	99.42 149	99.32 79	99.80 19	99.48 113	98.63 48	99.31 122	98.81 291	97.09 102	99.75 159	99.27 29	97.90 205	99.47 134

Test4		95.05 297	93.67 305	99.22 130	96.07 328	98.94 133	99.20 240	99.27 238	97.71 136	89.96 337	97.59 328	66.18 345	99.25 260	98.06 141	98.96 139	99.47 134

MIMVSNet		97.73 226	97.45 221	98.57 219	99.45 147	97.50 234	99.02 277	98.98 270	96.11 268	99.41 100	99.14 263	90.28 293	98.74 304	95.74 265	98.93 142	99.47 134

MVS_Test		99.10 75	98.97 72	99.48 89	99.49 138	99.14 99	99.67 56	99.34 205	97.31 170	99.58 64	99.76 87	97.65 90	99.82 134	98.87 61	99.07 131	99.46 137

MDTV_nov1_ep13_2view						95.18 302	99.35 198		96.84 213	99.58 64		95.19 157		97.82 156		99.46 137

MVS-HIRNet		95.75 290	95.16 294	97.51 294	99.30 178	93.69 319	98.88 303	95.78 346	85.09 338	98.78 223	92.65 342	91.29 286	99.37 228	94.85 282	99.85 52	99.46 137

DI_MVS_plusplus_test		97.45 252	96.79 261	99.44 98	97.76 317	99.04 108	99.21 238	98.61 314	97.74 133	94.01 319	98.83 289	87.38 324	99.83 125	98.63 88	98.90 146	99.44 140

DP-MVS Recon		99.12 68	98.95 76	99.65 58	99.74 67	99.70 30	99.27 218	99.57 44	96.40 245	99.42 98	99.68 119	98.75 46	99.80 142	97.98 144	99.72 85	99.44 140

PatchMatch-RL		98.84 109	98.62 116	99.52 84	99.71 81	99.28 84	99.06 266	99.77 9	97.74 133	99.50 83	99.53 176	95.41 147	99.84 118	97.17 211	99.64 100	99.44 140

VDDNet		97.55 242	97.02 257	99.16 133	99.49 138	98.12 210	99.38 186	99.30 222	95.35 281	99.68 37	99.90 7	82.62 339	99.93 57	99.31 25	98.13 191	99.42 143

PCF-MVS	97.08 14	97.66 238	97.06 256	99.47 92	99.61 116	99.09 103	98.04 337	99.25 241	91.24 326	98.51 252	99.70 108	94.55 196	99.91 74	92.76 316	99.85 52	99.42 143
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	97.30 7	98.85 107	98.64 112	99.47 92	99.42 149	99.08 104	99.62 82	99.36 193	97.39 165	99.28 131	99.68 119	96.44 120	99.92 65	98.37 117	98.22 179	99.40 145

Fast-Effi-MVS+		98.70 120	98.43 127	99.51 86	99.51 131	99.28 84	99.52 124	99.47 129	96.11 268	99.01 191	99.34 240	96.20 127	99.84 118	97.88 151	98.82 151	99.39 146

diffmvs		98.72 119	98.49 125	99.43 101	99.48 141	99.19 92	99.62 82	99.42 166	95.58 279	99.37 109	99.67 123	96.14 128	99.74 160	98.14 131	98.96 139	99.37 147

CANet_DTU		98.97 93	98.87 85	99.25 124	99.33 169	98.42 200	99.08 261	99.30 222	99.16 5	99.43 95	99.75 92	95.27 151	99.97 11	98.56 100	99.95 6	99.36 148

EPMVS		97.82 209	97.65 201	98.35 241	98.88 264	95.98 285	99.49 141	94.71 349	97.57 147	99.26 139	99.48 195	92.46 262	99.71 178	97.87 152	99.08 130	99.35 149

CostFormer		97.72 228	97.73 192	97.71 290	99.15 211	94.02 314	99.54 120	99.02 267	94.67 289	99.04 188	99.35 237	92.35 265	99.77 155	98.50 107	97.94 204	99.34 150

BH-untuned		98.42 134	98.36 130	98.59 217	99.49 138	96.70 267	99.27 218	99.13 254	97.24 177	98.80 221	99.38 222	95.75 140	99.74 160	97.07 218	99.16 123	99.33 151

PAPM		97.59 241	97.09 255	99.07 142	99.06 225	98.26 204	98.30 331	99.10 256	94.88 285	98.08 273	99.34 240	96.27 125	99.64 195	89.87 324	98.92 144	99.31 152

tpm2		97.44 253	97.34 241	97.74 289	99.15 211	94.36 311	99.45 154	98.94 274	93.45 314	98.90 209	99.44 207	91.35 285	99.59 205	97.31 202	98.07 200	99.29 153

JIA-IIPM		97.50 249	97.02 257	98.93 161	98.73 286	97.80 228	99.30 208	98.97 271	91.73 324	98.91 207	94.86 340	95.10 160	99.71 178	97.58 179	97.98 203	99.28 154

LP		97.04 264	96.80 260	97.77 287	98.90 260	95.23 299	98.97 290	99.06 263	94.02 304	98.09 272	99.41 213	93.88 220	98.82 302	90.46 322	98.42 170	99.26 155

dp		97.75 223	97.80 178	97.59 292	99.10 219	93.71 318	99.32 203	98.88 284	96.48 238	99.08 181	99.55 166	92.67 253	99.82 134	96.52 251	98.58 160	99.24 156

TESTMET0.1,1		97.55 242	97.27 250	98.40 238	98.93 255	96.53 272	98.67 315	97.61 340	96.96 205	98.64 245	99.28 251	88.63 313	99.45 214	97.30 203	99.38 110	99.21 157

DWT-MVSNet_test		97.53 244	97.40 232	97.93 275	99.03 231	94.86 305	99.57 105	98.63 312	96.59 229	98.36 261	98.79 292	89.32 303	99.74 160	98.14 131	98.16 190	99.20 158

tfpn1000		98.33 139	98.02 153	99.25 124	99.78 36	98.73 169	99.70 42	97.55 341	97.48 155	99.69 36	99.53 176	92.37 264	99.85 112	97.82 156	98.26 178	99.16 159

CR-MVSNet		98.17 156	97.93 161	98.87 187	99.18 201	98.49 194	99.22 235	99.33 213	96.96 205	99.56 68	99.38 222	94.33 204	99.00 290	94.83 283	98.58 160	99.14 160

RPMNet		96.61 268	95.85 276	98.87 187	99.18 201	98.49 194	99.22 235	99.08 258	88.72 335	99.56 68	97.38 331	94.08 215	99.00 290	86.87 335	98.58 160	99.14 160

testgi		97.65 239	97.50 213	98.13 264	99.36 164	96.45 275	99.42 170	99.48 113	97.76 130	97.87 281	99.45 206	91.09 287	98.81 303	94.53 287	98.52 165	99.13 162

test-LLR		98.06 168	97.90 162	98.55 223	98.79 276	97.10 244	98.67 315	97.75 330	97.34 167	98.61 249	98.85 287	94.45 200	99.45 214	97.25 204	99.38 110	99.10 163

test-mter		97.49 251	97.13 254	98.55 223	98.79 276	97.10 244	98.67 315	97.75 330	96.65 222	98.61 249	98.85 287	88.23 318	99.45 214	97.25 204	99.38 110	99.10 163

IB-MVS	95.67 18	96.22 283	95.44 291	98.57 219	99.21 194	96.70 267	98.65 318	97.74 332	96.71 218	97.27 290	98.54 305	86.03 327	99.92 65	98.47 110	86.30 338	99.10 163
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		98.86 101	98.63 113	99.54 76	99.37 162	99.66 36	99.45 154	99.54 62	96.61 225	99.01 191	99.40 217	97.09 102	99.86 106	97.68 175	99.53 105	99.10 163
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		98.33 139	98.48 126	97.90 278	99.16 208	94.78 306	99.31 206	99.11 255	97.27 173	99.45 91	99.59 154	95.33 148	99.84 118	98.48 108	98.61 157	99.09 167

xiu_mvs_v1_base_debu		99.29 47	99.27 40	99.34 106	99.63 108	98.97 125	99.12 251	99.51 85	98.86 31	99.84 8	99.47 199	98.18 76	99.99 1	99.50 8	99.31 115	99.08 168

xiu_mvs_v1_base		99.29 47	99.27 40	99.34 106	99.63 108	98.97 125	99.12 251	99.51 85	98.86 31	99.84 8	99.47 199	98.18 76	99.99 1	99.50 8	99.31 115	99.08 168

xiu_mvs_v1_base_debi		99.29 47	99.27 40	99.34 106	99.63 108	98.97 125	99.12 251	99.51 85	98.86 31	99.84 8	99.47 199	98.18 76	99.99 1	99.50 8	99.31 115	99.08 168

COLMAP_ROB	97.56 6	98.86 101	98.75 101	99.17 132	99.88 11	98.53 188	99.34 201	99.59 38	97.55 149	98.70 235	99.89 10	95.83 137	99.90 86	98.10 133	99.90 24	99.08 168
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpmp4_e23		97.34 256	97.29 247	97.52 293	99.25 190	93.73 316	99.58 99	99.19 249	94.00 305	98.20 268	99.41 213	90.74 291	99.74 160	97.13 214	98.07 200	99.07 172

PatchFormer-LS_test		98.01 182	98.05 151	97.87 279	99.15 211	94.76 307	99.42 170	98.93 275	97.12 187	98.84 218	98.59 303	93.74 227	99.80 142	98.55 103	98.17 189	99.06 173

OpenMVS	96.50 16	98.47 130	98.12 144	99.52 84	99.04 229	99.53 57	99.82 13	99.72 11	94.56 294	98.08 273	99.88 14	94.73 188	99.98 5	97.47 193	99.76 78	99.06 173

PatchT		97.03 265	96.44 266	98.79 201	98.99 235	98.34 201	99.16 244	99.07 261	92.13 320	99.52 80	97.31 333	94.54 197	98.98 292	88.54 328	98.73 156	99.03 175

BH-w/o		98.00 183	97.89 166	98.32 243	99.35 165	96.20 283	99.01 281	98.90 282	96.42 242	98.38 259	99.00 275	95.26 153	99.72 172	96.06 259	98.61 157	99.03 175

Fast-Effi-MVS+-dtu		98.77 116	98.83 94	98.60 216	99.41 152	96.99 255	99.52 124	99.49 104	98.11 86	99.24 148	99.34 240	96.96 106	99.79 145	97.95 147	99.45 106	99.02 177

XVG-OURS-SEG-HR		98.69 121	98.62 116	98.89 176	99.71 81	97.74 230	99.12 251	99.54 62	98.44 62	99.42 98	99.71 105	94.20 208	99.92 65	98.54 105	98.90 146	99.00 178

XVG-OURS		98.73 118	98.68 107	98.88 183	99.70 86	97.73 231	98.92 299	99.55 55	98.52 55	99.45 91	99.84 35	95.27 151	99.91 74	98.08 138	98.84 150	99.00 178

tpm cat1		97.39 255	97.36 236	97.50 295	99.17 206	93.73 316	99.43 163	99.31 220	91.27 325	98.71 229	99.08 268	94.31 206	99.77 155	96.41 255	98.50 166	99.00 178

xiu_mvs_v2_base		99.26 52	99.25 44	99.29 117	99.53 128	98.91 138	99.02 277	99.45 149	98.80 39	99.71 31	99.26 254	98.94 26	99.98 5	99.34 22	99.23 119	98.98 181

thresconf0.02		98.24 145	97.89 166	99.27 120	99.76 44	99.04 108	99.67 56	97.71 333	97.10 191	99.55 71	99.54 169	92.70 246	99.79 145	96.90 231	98.12 193	98.97 182

tfpn_n400		98.24 145	97.89 166	99.27 120	99.76 44	99.04 108	99.67 56	97.71 333	97.10 191	99.55 71	99.54 169	92.70 246	99.79 145	96.90 231	98.12 193	98.97 182

tfpnconf		98.24 145	97.89 166	99.27 120	99.76 44	99.04 108	99.67 56	97.71 333	97.10 191	99.55 71	99.54 169	92.70 246	99.79 145	96.90 231	98.12 193	98.97 182

tfpnview11		98.24 145	97.89 166	99.27 120	99.76 44	99.04 108	99.67 56	97.71 333	97.10 191	99.55 71	99.54 169	92.70 246	99.79 145	96.90 231	98.12 193	98.97 182

PS-MVSNAJ		99.32 42	99.32 26	99.30 114	99.57 123	98.94 133	98.97 290	99.46 138	98.92 28	99.71 31	99.24 256	99.01 11	99.98 5	99.35 18	99.66 97	98.97 182

tpmvs		97.98 184	98.02 153	97.84 282	99.04 229	94.73 308	99.31 206	99.20 246	96.10 271	98.76 225	99.42 210	94.94 168	99.81 138	96.97 224	98.45 168	98.97 182

view600		97.97 187	97.66 196	98.89 176	99.75 56	97.81 224	99.69 45	98.80 290	98.02 102	99.25 143	98.88 283	91.95 268	99.89 94	94.36 292	98.29 174	98.96 188

view800		97.97 187	97.66 196	98.89 176	99.75 56	97.81 224	99.69 45	98.80 290	98.02 102	99.25 143	98.88 283	91.95 268	99.89 94	94.36 292	98.29 174	98.96 188

conf0.05thres1000		97.97 187	97.66 196	98.89 176	99.75 56	97.81 224	99.69 45	98.80 290	98.02 102	99.25 143	98.88 283	91.95 268	99.89 94	94.36 292	98.29 174	98.96 188

tfpn		97.97 187	97.66 196	98.89 176	99.75 56	97.81 224	99.69 45	98.80 290	98.02 102	99.25 143	98.88 283	91.95 268	99.89 94	94.36 292	98.29 174	98.96 188

mvs-test1		98.86 101	98.84 91	98.89 176	99.33 169	97.77 229	99.44 158	99.30 222	98.47 57	99.10 176	99.43 208	96.78 110	99.95 33	98.73 77	99.02 134	98.96 188

thres600view7		97.86 201	97.51 211	98.92 166	99.72 75	97.95 217	99.59 92	98.74 298	97.94 111	99.27 135	98.62 298	91.75 274	99.86 106	93.73 304	98.19 182	98.96 188

thres400		97.77 218	97.38 234	98.92 166	99.69 88	97.96 215	99.50 133	98.73 307	97.83 122	99.17 166	98.45 307	91.67 280	99.83 125	93.22 309	98.18 183	98.96 188

TR-MVS		97.76 219	97.41 231	98.82 197	99.06 225	97.87 219	98.87 304	98.56 316	96.63 224	98.68 237	99.22 258	92.49 258	99.65 193	95.40 274	97.79 207	98.95 195

test0.0.03 1		97.71 231	97.42 230	98.56 221	98.41 308	97.82 223	98.78 308	98.63 312	97.34 167	98.05 277	98.98 279	94.45 200	98.98 292	95.04 280	97.15 241	98.89 196

cascas		97.69 232	97.43 229	98.48 228	98.60 300	97.30 235	98.18 335	99.39 179	92.96 316	98.41 257	98.78 294	93.77 224	99.27 254	98.16 130	98.61 157	98.86 197

1314		98.68 122	98.54 124	99.11 140	98.89 263	98.65 177	99.27 218	99.49 104	96.89 210	97.99 278	99.56 163	97.72 89	99.83 125	97.74 166	99.27 118	98.84 198

tfpn_ndepth		98.17 156	97.84 174	99.15 135	99.75 56	98.76 168	99.61 88	97.39 343	96.92 209	99.61 58	99.38 222	92.19 266	99.86 106	97.57 181	98.13 191	98.82 199

PS-MVSNAJss		98.92 96	98.92 78	98.90 174	98.78 280	98.53 188	99.78 22	99.54 62	98.07 93	99.00 198	99.76 87	99.01 11	99.37 228	99.13 39	97.23 237	98.81 200

pcd1.5k->3k		40.85 328	43.49 330	32.93 342	98.95 247	0.00 360	0.00 351	99.53 72	0.00 355	0.00 356	0.27 357	95.32 149	0.00 358	0.00 355	97.30 235	98.80 201

FC-MVSNet-test		98.75 117	98.62 116	99.15 135	99.08 222	99.45 68	99.86 8	99.60 35	98.23 75	98.70 235	99.82 44	96.80 109	99.22 266	99.07 44	96.38 252	98.79 202

nrg030		98.64 126	98.42 128	99.28 119	99.05 228	99.69 31	99.81 15	99.46 138	98.04 99	99.01 191	99.82 44	96.69 115	99.38 225	99.34 22	94.59 290	98.78 203

FIs		98.78 114	98.63 113	99.23 129	99.18 201	99.54 54	99.83 12	99.59 38	98.28 70	98.79 222	99.81 53	96.75 113	99.37 228	99.08 43	96.38 252	98.78 203

EU-MVSNet		97.98 184	98.03 152	97.81 285	98.72 288	96.65 270	99.66 65	99.66 25	98.09 89	98.35 262	99.82 44	95.25 154	98.01 321	97.41 198	95.30 270	98.78 203

jajsoiax		98.43 133	98.28 137	98.88 183	98.60 300	98.43 198	99.82 13	99.53 72	98.19 76	98.63 246	99.80 64	93.22 232	99.44 219	99.22 31	97.50 222	98.77 206

mvs_tets		98.40 136	98.23 139	98.91 170	98.67 295	98.51 193	99.66 65	99.53 72	98.19 76	98.65 244	99.81 53	92.75 240	99.44 219	99.31 25	97.48 226	98.77 206

XXY-MVS		98.38 137	98.09 147	99.24 127	99.26 188	99.32 79	99.56 112	99.55 55	97.45 159	98.71 229	99.83 37	93.23 231	99.63 200	98.88 57	96.32 254	98.76 208

v7n		97.87 200	97.52 209	98.92 166	98.76 284	98.58 185	99.84 9	99.46 138	96.20 259	98.91 207	99.70 108	94.89 174	99.44 219	96.03 260	93.89 303	98.75 209

PS-CasMVS		97.93 193	97.59 206	98.95 157	98.99 235	99.06 106	99.68 54	99.52 76	97.13 185	98.31 264	99.68 119	92.44 263	99.05 284	98.51 106	94.08 299	98.75 209

test_djsdf		98.67 123	98.57 122	98.98 152	98.70 291	98.91 138	99.88 1	99.46 138	97.55 149	99.22 155	99.88 14	95.73 141	99.28 251	99.03 46	97.62 212	98.75 209

Effi-MVS+-dtu		98.78 114	98.89 83	98.47 230	99.33 169	96.91 261	99.57 105	99.30 222	98.47 57	99.41 100	98.99 276	96.78 110	99.74 160	98.73 77	99.38 110	98.74 212

CP-MVSNet		98.09 166	97.78 181	99.01 148	98.97 242	99.24 89	99.67 56	99.46 138	97.25 175	98.48 255	99.64 137	93.79 223	99.06 283	98.63 88	94.10 298	98.74 212

VPA-MVSNet		98.29 142	97.95 159	99.30 114	99.16 208	99.54 54	99.50 133	99.58 43	98.27 71	99.35 116	99.37 226	92.53 257	99.65 193	99.35 18	94.46 291	98.72 214

PEN-MVS		97.76 219	97.44 226	98.72 208	98.77 283	98.54 187	99.78 22	99.51 85	97.06 200	98.29 266	99.64 137	92.63 254	98.89 301	98.09 134	93.16 309	98.72 214

VPNet		97.84 204	97.44 226	99.01 148	99.21 194	98.94 133	99.48 146	99.57 44	98.38 64	99.28 131	99.73 100	88.89 307	99.39 224	99.19 33	93.27 308	98.71 216

EI-MVSNet		98.67 123	98.67 108	98.68 211	99.35 165	97.97 214	99.50 133	99.38 185	96.93 208	99.20 160	99.83 37	97.87 83	99.36 232	98.38 116	97.56 217	98.71 216

WR-MVS		98.06 168	97.73 192	99.06 143	98.86 270	99.25 88	99.19 241	99.35 197	97.30 171	98.66 238	99.43 208	93.94 218	99.21 270	98.58 95	94.28 294	98.71 216

IterMVS-LS		98.46 131	98.42 128	98.58 218	99.59 120	98.00 212	99.37 188	99.43 165	96.94 207	99.07 182	99.59 154	97.87 83	99.03 287	98.32 123	95.62 265	98.71 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		97.92 196	97.60 205	98.87 187	98.83 273	98.65 177	99.55 117	99.34 205	96.20 259	99.32 121	99.40 217	94.36 203	99.26 259	96.37 256	95.03 277	98.70 220

v748		97.52 245	97.23 251	98.41 237	98.69 292	97.23 241	99.87 4	99.45 149	95.72 276	98.51 252	99.53 176	94.13 212	99.30 248	96.78 240	92.39 317	98.70 220

v1240		97.69 232	97.32 244	98.79 201	98.85 271	98.43 198	99.48 146	99.36 193	96.11 268	99.27 135	99.36 233	93.76 225	99.24 262	94.46 289	95.23 271	98.70 220

DTE-MVSNet		97.51 248	97.19 253	98.46 231	98.63 298	98.13 209	99.84 9	99.48 113	96.68 220	97.97 279	99.67 123	92.92 236	98.56 307	96.88 237	92.60 316	98.70 220

TranMVSNet+NR-MVSNet		97.93 193	97.66 196	98.76 206	98.78 280	98.62 181	99.65 75	99.49 104	97.76 130	98.49 254	99.60 152	94.23 207	98.97 299	98.00 143	92.90 311	98.70 220

v1921920		97.80 213	97.45 221	98.84 195	98.80 274	98.53 188	99.52 124	99.34 205	96.15 265	99.24 148	99.47 199	93.98 217	99.29 250	95.40 274	95.13 275	98.69 225

v1192		97.81 210	97.44 226	98.91 170	98.88 264	98.68 173	99.51 128	99.34 205	96.18 261	99.20 160	99.34 240	94.03 216	99.36 232	95.32 276	95.18 272	98.69 225

v2v482		98.06 168	97.77 185	98.92 166	98.90 260	98.82 155	99.57 105	99.36 193	96.65 222	99.19 163	99.35 237	94.20 208	99.25 260	97.72 171	94.97 278	98.69 225

UniMVSNet_NR-MVSNet		98.22 149	97.97 157	98.96 155	98.92 257	98.98 122	99.48 146	99.53 72	97.76 130	98.71 229	99.46 203	96.43 121	99.22 266	98.57 97	92.87 313	98.69 225

OurMVSNet-221017-0		97.88 199	97.77 185	98.19 261	98.71 290	96.53 272	99.88 1	99.00 268	97.79 127	98.78 223	99.94 3	91.68 279	99.35 235	97.21 206	96.99 243	98.69 225

gg-mvs-nofinetune		96.17 285	95.32 292	98.73 207	98.79 276	98.14 208	99.38 186	94.09 350	91.07 328	98.07 276	91.04 346	89.62 302	99.35 235	96.75 241	99.09 129	98.68 230

v1144		97.98 184	97.69 195	98.85 194	98.87 267	98.66 176	99.54 120	99.35 197	96.27 253	99.23 153	99.35 237	94.67 191	99.23 263	96.73 242	95.16 273	98.68 230

v1141		98.05 174	97.76 188	98.91 170	98.91 259	98.78 166	99.57 105	99.35 197	96.41 244	99.23 153	99.36 233	94.93 170	99.27 254	97.38 199	94.72 284	98.68 230

testing_2		94.44 302	92.93 308	98.98 152	94.16 336	99.00 120	99.42 170	99.28 233	96.60 227	84.86 339	96.84 334	70.91 342	99.27 254	98.23 126	96.08 258	98.68 230

divwei89l23v2f112		98.06 168	97.78 181	98.91 170	98.90 260	98.77 167	99.57 105	99.35 197	96.45 239	99.24 148	99.37 226	94.92 171	99.27 254	97.50 189	94.71 286	98.68 230

v1		98.05 174	97.76 188	98.93 161	98.92 257	98.80 162	99.57 105	99.35 197	96.39 246	99.28 131	99.36 233	94.86 176	99.32 242	97.38 199	94.72 284	98.68 230

DU-MVS		98.08 167	97.79 179	98.96 155	98.87 267	98.98 122	99.41 174	99.45 149	97.87 116	98.71 229	99.50 186	94.82 178	99.22 266	98.57 97	92.87 313	98.68 230

NR-MVSNet		97.97 187	97.61 204	99.02 147	98.87 267	99.26 87	99.47 150	99.42 166	97.63 143	97.08 294	99.50 186	95.07 161	99.13 276	97.86 153	93.59 305	98.68 230

LPG-MVS_test		98.22 149	98.13 143	98.49 226	99.33 169	97.05 250	99.58 99	99.55 55	97.46 156	99.24 148	99.83 37	92.58 255	99.72 172	98.09 134	97.51 220	98.68 230

LGP-MVS_train				98.49 226	99.33 169	97.05 250		99.55 55	97.46 156	99.24 148	99.83 37	92.58 255	99.72 172	98.09 134	97.51 220	98.68 230

LTVRE_ROB	97.16 12	98.02 179	97.90 162	98.40 238	99.23 191	96.80 265	99.70 42	99.60 35	97.12 187	98.18 269	99.70 108	91.73 278	99.72 172	98.39 114	97.45 227	98.68 230
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
semantic-postprocess				98.06 266	99.57 123	96.36 278		99.49 104	97.18 181	98.71 229	99.72 104	92.70 246	99.14 273	97.44 196	95.86 261	98.67 241

v1neww		98.12 162	97.84 174	98.93 161	98.97 242	98.81 157	99.66 65	99.35 197	96.49 232	99.29 127	99.37 226	95.02 163	99.32 242	97.73 167	94.73 282	98.67 241

v7new		98.12 162	97.84 174	98.93 161	98.97 242	98.81 157	99.66 65	99.35 197	96.49 232	99.29 127	99.37 226	95.02 163	99.32 242	97.73 167	94.73 282	98.67 241

pm-mvs1		97.68 234	97.28 248	98.88 183	99.06 225	98.62 181	99.50 133	99.45 149	96.32 248	97.87 281	99.79 72	92.47 259	99.35 235	97.54 185	93.54 306	98.67 241

v6		98.12 162	97.84 174	98.94 158	98.94 250	98.83 148	99.66 65	99.34 205	96.49 232	99.30 123	99.37 226	94.95 167	99.34 238	97.77 162	94.74 281	98.67 241

v10		97.85 202	97.52 209	98.86 191	98.99 235	98.67 174	99.75 34	99.41 169	95.70 277	98.98 200	99.41 213	94.75 187	99.23 263	96.01 261	94.63 289	98.67 241

HQP_MVS		98.27 144	98.22 140	98.44 235	99.29 181	96.97 257	99.39 181	99.47 129	98.97 22	99.11 173	99.61 149	92.71 244	99.69 187	97.78 160	97.63 210	98.67 241

plane_prior5								99.47 129					99.69 187	97.78 160	97.63 210	98.67 241

SixPastTwentyTwo		97.50 249	97.33 243	98.03 267	98.65 296	96.23 282	99.77 24	98.68 310	97.14 184	97.90 280	99.93 4	90.45 292	99.18 272	97.00 221	96.43 251	98.67 241

IterMVS		97.83 206	97.77 185	98.02 269	99.58 121	96.27 281	99.02 277	99.48 113	97.22 179	98.71 229	99.70 108	92.75 240	99.13 276	97.46 194	96.00 259	98.67 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	97.28 8	98.10 165	97.99 156	98.44 235	99.41 152	96.96 259	99.60 90	99.56 48	98.09 89	98.15 270	99.91 5	90.87 290	99.70 184	98.88 57	97.45 227	98.67 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v8		97.95 192	97.63 203	98.93 161	98.95 247	98.81 157	99.80 19	99.41 169	96.03 272	99.10 176	99.42 210	94.92 171	99.30 248	96.94 227	94.08 299	98.66 252

v7		98.05 174	97.78 181	98.87 187	98.99 235	98.67 174	99.64 77	99.34 205	96.31 250	99.29 127	99.51 184	94.78 181	99.27 254	97.03 219	95.15 274	98.66 252

UniMVSNet (Re)		98.29 142	98.00 155	99.13 139	99.00 234	99.36 76	99.49 141	99.51 85	97.95 110	98.97 201	99.13 264	96.30 124	99.38 225	98.36 119	93.34 307	98.66 252

pmmvs6		96.53 270	96.09 271	97.82 284	98.69 292	95.47 295	99.37 188	99.47 129	93.46 313	97.41 288	99.78 77	87.06 325	99.33 239	96.92 229	92.70 315	98.65 255

K. test v3		97.10 263	96.79 261	98.01 270	98.72 288	96.33 279	99.87 4	97.05 344	97.59 144	96.16 304	99.80 64	88.71 309	99.04 285	96.69 245	96.55 249	98.65 255

YYNet1		95.36 295	94.51 300	97.92 276	97.89 314	97.10 244	99.10 259	99.23 243	93.26 315	80.77 343	99.04 273	92.81 239	98.02 320	94.30 297	94.18 297	98.64 257

MDA-MVSNet_test_wron		95.45 293	94.60 298	98.01 270	98.16 312	97.21 242	99.11 257	99.24 242	93.49 312	80.73 344	98.98 279	93.02 233	98.18 309	94.22 301	94.45 292	98.64 257

Baseline_NR-MVSNet		97.76 219	97.45 221	98.68 211	99.09 221	98.29 202	99.41 174	98.85 286	95.65 278	98.63 246	99.67 123	94.82 178	99.10 281	98.07 140	92.89 312	98.64 257

HQP4-MVS										98.66 238			99.64 195			98.64 257

HQP-MVS		98.02 179	97.90 162	98.37 240	99.19 198	96.83 262	98.98 287	99.39 179	98.24 72	98.66 238	99.40 217	92.47 259	99.64 195	97.19 208	97.58 215	98.64 257

ACMM	97.58 5	98.37 138	98.34 132	98.48 228	99.41 152	97.10 244	99.56 112	99.45 149	98.53 54	99.04 188	99.85 26	93.00 234	99.71 178	98.74 75	97.45 227	98.64 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs5		97.52 245	97.30 246	98.16 263	98.57 302	96.73 266	99.27 218	98.90 282	96.14 266	98.37 260	99.53 176	91.54 284	99.14 273	97.51 188	95.87 260	98.63 263

v148		97.79 215	97.55 207	98.50 225	98.74 285	97.72 232	99.54 120	99.33 213	96.26 254	98.90 209	99.51 184	94.68 190	99.14 273	97.83 155	93.15 310	98.63 263

MDA-MVSNet-bldmvs		94.96 298	93.98 303	97.92 276	98.24 311	97.27 237	99.15 247	99.33 213	93.80 308	80.09 345	99.03 274	88.31 317	97.86 325	93.49 307	94.36 293	98.62 265

TransMVSNet (Re)		97.15 261	96.58 264	98.86 191	99.12 214	98.85 144	99.49 141	98.91 280	95.48 280	97.16 293	99.80 64	93.38 229	99.11 279	94.16 302	91.73 318	98.62 265

lessismore_v0				97.79 286	98.69 292	95.44 297		94.75 348		95.71 308	99.87 19	88.69 310	99.32 242	95.89 262	94.93 280	98.62 265

MVSTER		98.49 129	98.32 134	99.00 150	99.35 165	99.02 116	99.54 120	99.38 185	97.41 163	99.20 160	99.73 100	93.86 222	99.36 232	98.87 61	97.56 217	98.62 265

GBi-Net		97.68 234	97.48 216	98.29 246	99.51 131	97.26 238	99.43 163	99.48 113	96.49 232	99.07 182	99.32 245	90.26 294	98.98 292	97.10 215	96.65 245	98.62 265

test1		97.68 234	97.48 216	98.29 246	99.51 131	97.26 238	99.43 163	99.48 113	96.49 232	99.07 182	99.32 245	90.26 294	98.98 292	97.10 215	96.65 245	98.62 265

FMVSNet1		96.84 266	96.36 267	98.29 246	99.32 176	97.26 238	99.43 163	99.48 113	95.11 283	98.55 251	99.32 245	83.95 336	98.98 292	95.81 264	96.26 255	98.62 265

ACMP	97.20 11	98.06 168	97.94 160	98.45 232	99.37 162	97.01 253	99.44 158	99.49 104	97.54 152	98.45 256	99.79 72	91.95 268	99.72 172	97.91 149	97.49 225	98.62 265
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	97.24 10	97.92 196	97.78 181	98.32 243	99.46 143	96.68 269	99.56 112	99.54 62	98.41 63	97.79 285	99.87 19	90.18 297	99.66 191	98.05 142	97.18 240	98.62 265

tfpn111		97.81 210	97.49 215	98.78 203	99.72 75	97.86 220	99.59 92	98.74 298	97.93 112	99.26 139	98.62 298	91.75 274	99.86 106	93.57 305	98.18 183	98.61 274

conf0.01		98.21 152	97.89 166	99.15 135	99.76 44	99.04 108	99.67 56	97.71 333	97.10 191	99.55 71	99.54 169	92.70 246	99.79 145	96.90 231	98.12 193	98.61 274

conf0.002		98.21 152	97.89 166	99.15 135	99.76 44	99.04 108	99.67 56	97.71 333	97.10 191	99.55 71	99.54 169	92.70 246	99.79 145	96.90 231	98.12 193	98.61 274

conf200view11		97.78 217	97.45 221	98.77 204	99.72 75	97.86 220	99.59 92	98.74 298	97.93 112	99.26 139	98.62 298	91.75 274	99.83 125	93.22 309	98.18 183	98.61 274

OPM-MVS		98.19 155	98.10 145	98.45 232	98.88 264	97.07 248	99.28 215	99.38 185	98.57 52	99.22 155	99.81 53	92.12 267	99.66 191	98.08 138	97.54 219	98.61 274

WR-MVS_H		98.13 160	97.87 173	98.90 174	99.02 232	98.84 145	99.70 42	99.59 38	97.27 173	98.40 258	99.19 260	95.53 144	99.23 263	98.34 120	93.78 304	98.61 274

MIMVSNet1		95.51 292	95.04 295	96.92 304	97.38 321	95.60 289	99.52 124	99.50 99	93.65 309	96.97 298	99.17 261	85.28 331	96.56 336	88.36 329	95.55 267	98.60 280

test2356		94.07 306	94.46 301	92.89 320	95.18 332	86.13 336	97.60 341	99.06 263	93.61 310	96.15 306	98.28 310	85.60 330	93.95 343	86.68 336	98.00 202	98.59 281

test1235678		92.91 309	93.30 306	91.71 326	93.14 339	83.01 340	98.75 311	98.58 315	92.80 318	92.45 329	97.91 314	88.51 315	93.54 344	82.26 340	95.35 269	98.59 281

N_pmnet		94.95 299	95.83 277	92.31 323	98.47 306	79.33 346	99.12 251	92.81 355	93.87 307	97.68 286	99.13 264	93.87 221	99.01 289	91.38 320	96.19 256	98.59 281

FMVSNet2		97.72 228	97.36 236	98.80 200	99.51 131	98.84 145	99.45 154	99.42 166	96.49 232	98.86 217	99.29 250	90.26 294	98.98 292	96.44 253	96.56 248	98.58 284

anonymousdsp		98.44 132	98.28 137	98.94 158	98.50 305	98.96 129	99.77 24	99.50 99	97.07 198	98.87 212	99.77 84	94.76 186	99.28 251	98.66 85	97.60 213	98.57 285

FMVSNet3		98.03 177	97.76 188	98.84 195	99.39 159	98.98 122	99.40 180	99.38 185	96.67 221	99.07 182	99.28 251	92.93 235	98.98 292	97.10 215	96.65 245	98.56 286

XVG-ACMP-BASELINE		97.83 206	97.71 194	98.20 260	99.11 216	96.33 279	99.41 174	99.52 76	98.06 97	99.05 187	99.50 186	89.64 301	99.73 168	97.73 167	97.38 233	98.53 287

Patchmtry		97.75 223	97.40 232	98.81 198	99.10 219	98.87 141	99.11 257	99.33 213	94.83 286	98.81 220	99.38 222	94.33 204	99.02 288	96.10 258	95.57 266	98.53 287

USDC		97.34 256	97.20 252	97.75 288	99.07 223	95.20 300	98.51 324	99.04 265	97.99 107	98.31 264	99.86 22	89.02 305	99.55 208	95.67 269	97.36 234	98.49 289

CLD-MVS		98.16 158	98.10 145	98.33 242	99.29 181	96.82 264	98.75 311	99.44 157	97.83 122	99.13 169	99.55 166	92.92 236	99.67 189	98.32 123	97.69 209	98.48 290
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous20231206		96.22 283	96.03 272	96.79 307	97.31 324	94.14 313	99.63 79	99.08 258	96.17 262	97.04 295	99.06 271	93.94 218	97.76 328	86.96 334	95.06 276	98.47 291

FMVSNet5		96.43 272	96.19 269	97.15 298	99.11 216	95.89 287	99.32 203	99.52 76	94.47 298	98.34 263	99.07 269	87.54 322	97.07 332	92.61 317	95.72 263	98.47 291

pmmvs4		98.13 160	97.90 162	98.81 198	98.61 299	98.87 141	98.99 283	99.21 245	96.44 240	99.06 186	99.58 157	95.90 135	99.11 279	97.18 210	96.11 257	98.46 293

V42		98.06 168	97.79 179	98.86 191	98.98 239	98.84 145	99.69 45	99.34 205	96.53 231	99.30 123	99.37 226	94.67 191	99.32 242	97.57 181	94.66 287	98.42 294

PVSNet_BlendedMVS		98.86 101	98.80 95	99.03 146	99.76 44	98.79 164	99.28 215	99.91 3	97.42 162	99.67 43	99.37 226	97.53 91	99.88 101	98.98 51	97.29 236	98.42 294

UnsupCasMVSNet_eth		96.44 271	96.12 270	97.40 297	98.65 296	95.65 288	99.36 194	99.51 85	97.13 185	96.04 307	98.99 276	88.40 316	98.17 310	96.71 243	90.27 321	98.40 296

TinyColmap		97.12 262	96.89 259	97.83 283	99.07 223	95.52 294	98.57 321	98.74 298	97.58 146	97.81 284	99.79 72	88.16 319	99.56 206	95.10 278	97.21 238	98.39 297

thres100view900		97.76 219	97.45 221	98.69 210	99.72 75	97.86 220	99.59 92	98.74 298	97.93 112	99.26 139	98.62 298	91.75 274	99.83 125	93.22 309	98.18 183	98.37 298

tfpn200view9		97.72 228	97.38 234	98.72 208	99.69 88	97.96 215	99.50 133	98.73 307	97.83 122	99.17 166	98.45 307	91.67 280	99.83 125	93.22 309	98.18 183	98.37 298

testus		94.61 300	95.30 293	92.54 322	96.44 327	84.18 338	98.36 327	99.03 266	94.18 303	96.49 300	98.57 304	88.74 308	95.09 341	87.41 332	98.45 168	98.36 300

tfpnnormal		97.84 204	97.47 218	98.98 152	99.20 196	99.22 91	99.64 77	99.61 32	96.32 248	98.27 267	99.70 108	93.35 230	99.44 219	95.69 267	95.40 268	98.27 301

test20.03		96.12 286	95.96 275	96.63 308	97.44 320	95.45 296	99.51 128	99.38 185	96.55 230	96.16 304	99.25 255	93.76 225	96.17 337	87.35 333	94.22 296	98.27 301

ITE_SJBPF				98.08 265	99.29 181	96.37 277		98.92 277	98.34 66	98.83 219	99.75 92	91.09 287	99.62 201	95.82 263	97.40 231	98.25 303

EG-PatchMatch MVS		95.97 288	95.69 282	96.81 306	97.78 316	92.79 324	99.16 244	98.93 275	96.16 263	94.08 316	99.22 258	82.72 338	99.47 212	95.67 269	97.50 222	98.17 304

TDRefinement		95.42 294	94.57 299	97.97 273	89.83 345	96.11 284	99.48 146	98.75 295	96.74 216	96.68 299	99.88 14	88.65 312	99.71 178	98.37 117	82.74 341	98.09 305

API-MVS		99.04 83	99.03 64	99.06 143	99.40 157	99.31 82	99.55 117	99.56 48	98.54 53	99.33 120	99.39 221	98.76 43	99.78 153	96.98 223	99.78 74	98.07 306

v52		97.79 215	97.50 213	98.66 214	98.80 274	98.62 181	99.87 4	99.44 157	95.87 274	99.01 191	99.46 203	94.44 202	99.33 239	96.65 249	93.96 302	98.05 307

V4		97.80 213	97.51 211	98.67 213	98.79 276	98.63 179	99.87 4	99.44 157	95.87 274	99.01 191	99.46 203	94.52 198	99.33 239	96.64 250	93.97 301	98.05 307

new_pmnet		96.38 276	96.03 272	97.41 296	98.13 313	95.16 303	99.05 268	99.20 246	93.94 306	97.39 289	98.79 292	91.61 283	99.04 285	90.43 323	95.77 262	98.05 307

thres200		97.61 240	97.28 248	98.62 215	99.64 105	98.03 211	99.26 226	98.74 298	97.68 140	99.09 180	98.32 309	91.66 282	99.81 138	92.88 315	98.22 179	98.03 310

DeepMVS_CX				93.34 318	99.29 181	82.27 343		99.22 244	85.15 337	96.33 302	99.05 272	90.97 289	99.73 168	93.57 305	97.77 208	98.01 311

GG-mvs-BLEND				98.45 232	98.55 303	98.16 207	99.43 163	93.68 351		97.23 291	98.46 306	89.30 304	99.22 266	95.43 273	98.22 179	97.98 312

pmmvs3		94.09 305	93.25 307	96.60 309	94.76 334	94.49 309	98.92 299	98.18 325	89.66 330	96.48 301	98.06 312	86.28 326	97.33 331	89.68 325	87.20 332	97.97 313

LF4IMVS		97.52 245	97.46 220	97.70 291	98.98 239	95.55 291	99.29 212	98.82 289	98.07 93	98.66 238	99.64 137	89.97 298	99.61 202	97.01 220	96.68 244	97.94 314

test_0402		96.64 267	96.24 268	97.85 281	98.85 271	96.43 276	99.44 158	99.26 239	93.52 311	96.98 297	99.52 181	88.52 314	99.20 271	92.58 318	97.50 222	97.93 315

MVP-Stereo		97.81 210	97.75 191	97.99 272	97.53 319	96.60 271	98.96 292	98.85 286	97.22 179	97.23 291	99.36 233	95.28 150	99.46 213	95.51 271	99.78 74	97.92 316
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MS-PatchMatch		97.24 260	97.32 244	96.99 301	98.45 307	93.51 321	98.82 306	99.32 219	97.41 163	98.13 271	99.30 248	88.99 306	99.56 206	95.68 268	99.80 70	97.90 317

v13		96.24 280	95.58 285	98.25 253	98.98 239	98.83 148	99.75 34	99.29 226	94.35 301	93.89 323	97.60 326	95.17 158	98.11 317	94.27 299	86.86 336	97.81 318

V9		96.25 279	95.58 285	98.26 249	98.94 250	98.83 148	99.75 34	99.29 226	94.45 299	93.96 320	97.62 324	94.94 168	98.14 314	94.40 291	86.87 335	97.81 318

v17		96.42 273	95.81 278	98.25 253	98.94 250	98.80 162	99.76 27	99.28 233	94.57 292	94.18 313	97.71 317	95.23 155	98.16 311	94.86 281	87.73 330	97.80 320

v16		96.39 275	95.76 281	98.26 249	98.96 245	98.81 157	99.76 27	99.28 233	94.57 292	94.10 315	97.70 318	95.04 162	98.16 311	94.70 285	87.77 329	97.80 320

v15		96.28 277	95.62 283	98.25 253	98.94 250	98.83 148	99.76 27	99.29 226	94.52 296	94.02 318	97.61 325	95.02 163	98.13 315	94.53 287	86.92 333	97.80 320

v12		96.24 280	95.58 285	98.23 256	98.96 245	98.81 157	99.76 27	99.29 226	94.42 300	93.85 324	97.60 326	95.12 159	98.09 318	94.32 296	86.85 337	97.80 320

V14		96.26 278	95.60 284	98.26 249	98.94 250	98.83 148	99.76 27	99.29 226	94.49 297	93.96 320	97.66 321	94.99 166	98.13 315	94.41 290	86.90 334	97.80 320

v18		96.42 273	95.80 280	98.26 249	98.95 247	98.82 155	99.76 27	99.28 233	94.58 291	94.12 314	97.70 318	95.22 156	98.16 311	94.83 283	87.80 328	97.79 325

Anonymous20231211		90.69 313	89.39 314	94.58 315	94.25 335	88.18 333	99.29 212	99.07 261	82.45 341	92.95 328	97.65 322	63.96 348	97.79 326	89.27 326	85.63 339	97.77 326

v11		96.23 282	95.57 288	98.21 259	98.93 255	98.83 148	99.72 39	99.29 226	94.29 302	94.05 317	97.64 323	94.88 175	98.04 319	92.89 314	88.43 326	97.77 326

ambc				93.06 319	92.68 340	82.36 342	98.47 325	98.73 307		95.09 310	97.41 330	55.55 350	99.10 281	96.42 254	91.32 319	97.71 328

new-patchmatchnet		94.48 301	94.08 302	95.67 313	95.08 333	92.41 325	99.18 242	99.28 233	94.55 295	93.49 326	97.37 332	87.86 321	97.01 333	91.57 319	88.36 327	97.61 329

pmmvs-eth3d		95.34 296	94.73 297	97.15 298	95.53 331	95.94 286	99.35 198	99.10 256	95.13 282	93.55 325	97.54 329	88.15 320	97.91 323	94.58 286	89.69 324	97.61 329

UnsupCasMVSNet_bld		93.53 307	92.51 309	96.58 310	97.38 321	93.82 315	98.24 332	99.48 113	91.10 327	93.10 327	96.66 335	74.89 341	98.37 308	94.03 303	87.71 331	97.56 331

PM-MVS		92.96 308	92.23 310	95.14 314	95.61 329	89.98 332	99.37 188	98.21 323	94.80 287	95.04 311	97.69 320	65.06 346	97.90 324	94.30 297	89.98 323	97.54 332

LCM-MVSNet		86.80 316	85.22 319	91.53 327	87.81 347	80.96 344	98.23 334	98.99 269	71.05 345	90.13 336	96.51 336	48.45 353	96.88 334	90.51 321	85.30 340	96.76 333

OpenMVS_ROB	92.34 20	94.38 303	93.70 304	96.41 311	97.38 321	93.17 322	99.06 266	98.75 295	86.58 336	94.84 312	98.26 311	81.53 340	99.32 242	89.01 327	97.87 206	96.76 333

CMPMVS	69.68 23	94.13 304	94.90 296	91.84 324	97.24 325	80.01 345	98.52 323	99.48 113	89.01 333	91.99 331	99.67 123	85.67 329	99.13 276	95.44 272	97.03 242	96.39 335
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
1111		92.30 310	92.21 311	92.55 321	93.30 337	86.27 334	99.15 247	98.74 298	91.94 321	90.85 334	97.82 315	84.18 334	95.21 339	79.65 342	94.27 295	96.19 336

test12356		91.74 311	92.19 312	90.37 329	91.22 341	82.41 341	98.61 319	98.28 320	90.66 329	91.82 332	97.92 313	84.90 332	92.61 345	81.64 341	94.66 287	96.09 337

PMMVS2		86.87 315	85.37 318	91.35 328	90.21 344	83.80 339	98.89 302	97.45 342	83.13 340	91.67 333	95.03 338	48.49 352	94.70 342	85.86 337	77.62 343	95.54 338

tmp_tt		82.80 320	81.52 320	86.66 331	66.61 356	68.44 354	92.79 349	97.92 327	68.96 347	80.04 346	99.85 26	85.77 328	96.15 338	97.86 153	43.89 351	95.39 339

testmv		87.91 314	87.80 315	88.24 330	87.68 348	77.50 348	99.07 262	97.66 339	89.27 331	86.47 338	96.22 337	68.35 344	92.49 347	76.63 346	88.82 325	94.72 340

no-one		83.04 319	80.12 321	91.79 325	89.44 346	85.65 337	99.32 203	98.32 319	89.06 332	79.79 347	89.16 348	44.86 354	96.67 335	84.33 339	46.78 350	93.05 341

testpf		95.66 291	96.02 274	94.58 315	98.35 309	92.32 326	97.25 343	97.91 329	92.83 317	97.03 296	98.99 276	88.69 310	98.61 306	95.72 266	97.40 231	92.80 342

FPMVS		84.93 317	85.65 317	82.75 337	86.77 349	63.39 355	98.35 329	98.92 277	74.11 344	83.39 341	98.98 279	50.85 351	92.40 348	84.54 338	94.97 278	92.46 343

Gipuma		90.99 312	90.15 313	93.51 317	98.73 286	90.12 331	93.98 347	99.45 149	79.32 342	92.28 330	94.91 339	69.61 343	97.98 322	87.42 331	95.67 264	92.45 344
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		77.30 324	74.86 326	84.62 334	75.88 354	77.61 347	97.63 340	93.15 354	88.81 334	64.27 350	89.29 347	36.51 355	83.93 354	75.89 347	52.31 349	92.33 345

PNet_i23d		79.43 323	77.68 324	84.67 333	86.18 350	71.69 353	96.50 345	93.68 351	75.17 343	71.33 348	91.18 345	32.18 357	90.62 349	78.57 345	74.34 344	91.71 346

MVE	76.82 21	76.91 325	74.31 327	84.70 332	85.38 352	76.05 351	96.88 344	93.17 353	67.39 348	71.28 349	89.01 349	21.66 362	87.69 351	71.74 349	72.29 345	90.35 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	70.75 22	75.98 326	74.97 325	79.01 339	70.98 355	55.18 356	93.37 348	98.21 323	65.08 351	61.78 352	93.83 341	21.74 361	92.53 346	78.59 344	91.12 320	89.34 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		74.42 327	71.19 328	84.14 335	76.16 353	74.29 352	96.00 346	92.57 356	69.57 346	63.84 351	87.49 350	21.98 359	88.86 350	75.56 348	57.50 348	89.26 349

EMVS		80.02 322	79.22 323	82.43 338	91.19 342	76.40 349	97.55 342	92.49 357	66.36 350	83.01 342	91.27 344	64.63 347	85.79 353	65.82 351	60.65 347	85.08 350

E-PMN		80.61 321	79.88 322	82.81 336	90.75 343	76.38 350	97.69 339	95.76 347	66.44 349	83.52 340	92.25 343	62.54 349	87.16 352	68.53 350	61.40 346	84.89 351

test123		39.01 331	42.50 331	28.53 343	39.17 357	20.91 358	98.75 311	19.17 360	19.83 354	38.57 353	66.67 352	33.16 356	15.42 356	37.50 354	29.66 354	49.26 352

.test1245		83.42 318	86.17 316	75.15 340	93.30 337	86.27 334	99.15 247	98.74 298	91.94 321	90.85 334	97.82 315	84.18 334	95.21 339	79.65 342	39.90 352	43.98 353

testmvs		39.17 330	43.78 329	25.37 344	36.04 358	16.84 359	98.36 327	26.56 358	20.06 353	38.51 354	67.32 351	29.64 358	15.30 357	37.59 353	39.90 352	43.98 353

wuyk23d		40.18 329	41.29 332	36.84 341	86.18 350	49.12 357	79.73 350	22.81 359	27.64 352	25.46 355	28.45 356	21.98 359	48.89 355	55.80 352	23.56 355	12.51 355

cdsmvs_eth3d_5k		24.64 332	32.85 333	0.00 345	0.00 359	0.00 360	0.00 351	99.51 85	0.00 355	0.00 356	99.56 163	96.58 117	0.00 358	0.00 355	0.00 356	0.00 356

pcd_1.5k_mvsjas		8.27 334	11.03 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.27 357	99.01 11	0.00 358	0.00 355	0.00 356	0.00 356

sosnet-low-res		0.02 335	0.03 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.27 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

sosnet		0.02 335	0.03 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.27 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uncertanet		0.02 335	0.03 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.27 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

Regformer		0.02 335	0.03 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.27 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

ab-mvs-re		8.30 333	11.06 334	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	99.58 157	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uanet		0.02 335	0.03 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.27 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

test_part3							99.37 188		97.97 108		99.78 77		99.95 33	97.15 212

test_part2					99.81 32	99.83 7				99.77 23

sam_mvs												94.72 189

MTGPA								99.47 129

test_post1							99.23 231				65.14 354	94.18 211	99.71 178	97.58 179

test_post											65.99 353	94.65 193	99.73 168

patchmatchnet-post											98.70 296	94.79 180	99.74 160

MTMP								98.88 284

gm-plane-assit					98.54 304	92.96 323			94.65 290		99.15 262		99.64 195	97.56 183

TEST9					99.67 92	99.65 39	99.05 268	99.41 169	96.22 258	98.95 202	99.49 189	98.77 41	99.91 74

test_8					99.67 92	99.61 44	99.03 274	99.41 169	96.28 251	98.93 205	99.48 195	98.76 43	99.91 74

agg_prior					99.67 92	99.62 42		99.40 176		98.87 212			99.91 74

test_prior4						99.56 51	98.99 283

test_prior2							98.96 292		98.34 66	99.01 191	99.52 181	98.68 51		97.96 145	99.74 81

旧先验2							98.96 292		96.70 219	99.47 88			99.94 42	98.19 127

新几何2							99.01 281

原ACMM2							98.95 296

testdata2													99.95 33	96.67 246

segment_acmp												98.96 20

testdata1							98.85 305		98.32 69

plane_prior7					99.29 181	97.03 252

plane_prior6					99.27 186	96.98 256						92.71 244

plane_prior4											99.61 149

plane_prior3						97.00 254			98.69 46	99.11 173

plane_prior2							99.39 181		98.97 22

plane_prior1					99.26 188

plane_prior						96.97 257	99.21 238		98.45 59						97.60 213

n2								0.00 361

nn								0.00 361

door-mid								98.05 326

test11								99.35 197

door								97.92 327

HQP5-MVS						96.83 262

HQP-NCC					99.19 198		98.98 287		98.24 72	98.66 238

ACMP_Plane					99.19 198		98.98 287		98.24 72	98.66 238

BP-MVS														97.19 208

HQP3-MVS								99.39 179							97.58 215

HQP2-MVS												92.47 259

NP-MVS					99.23 191	96.92 260					99.40 217

MDTV_nov1_ep13			98.32 134		99.11 216	94.44 310	99.27 218	98.74 298	97.51 153	99.40 104	99.62 146	94.78 181	99.76 158	97.59 178	98.81 153

ACMMP++_ref															97.19 239

ACMMP++															97.43 230

Test By Simon												98.75 46

High-resolution multi-view benchmark