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.

region2R		94.43 16	94.27 17	94.92 12	98.65 1	86.67 24	96.92 14	97.23 22	88.60 58	93.58 28	97.27 14	85.22 40	99.54 11	92.21 32	98.74 19	98.56 11

ACMMPR		94.43 16	94.28 16	94.91 13	98.63 2	86.69 22	96.94 10	97.32 16	88.63 56	93.53 31	97.26 16	85.04 43	99.54 11	92.35 30	98.78 14	98.50 12

HFP-MVS		94.52 12	94.40 13	94.86 15	98.61 3	86.81 17	96.94 10	97.34 11	88.63 56	93.65 24	97.21 19	86.10 30	99.49 17	92.35 30	98.77 15	98.30 27

#test#		94.32 21	94.14 22	94.86 15	98.61 3	86.81 17	96.43 23	97.34 11	87.51 84	93.65 24	97.21 19	86.10 30	99.49 17	91.68 48	98.77 15	98.30 27

test_part2					98.55 5	87.22 11				96.40 3

ESAPD		95.32 3	95.38 3	95.17 7	98.55 5	87.22 11	95.99 37	97.45 6	88.25 66	96.40 3	97.60 5	91.93 1	99.62 1	93.18 19	99.02 3	98.67 5

XVS		94.45 14	94.32 14	94.85 17	98.54 7	86.60 27	96.93 12	97.19 23	90.66 22	92.85 37	97.16 24	85.02 44	99.49 17	91.99 39	98.56 36	98.47 15

X-MVStestdata		88.31 138	86.13 186	94.85 17	98.54 7	86.60 27	96.93 12	97.19 23	90.66 22	92.85 37	23.41 358	85.02 44	99.49 17	91.99 39	98.56 36	98.47 15

mPP-MVS		93.99 29	93.78 31	94.63 30	98.50 9	85.90 48	96.87 16	96.91 42	88.70 54	91.83 65	97.17 23	83.96 53	99.55 8	91.44 53	98.64 32	98.43 21

HSP-MVS		95.30 4	95.48 2	94.76 25	98.49 10	86.52 29	96.91 15	96.73 55	91.73 9	96.10 6	96.69 39	89.90 3	99.30 30	94.70 3	98.04 50	98.45 19

MP-MVS		94.25 22	94.07 25	94.77 24	98.47 11	86.31 37	96.71 20	96.98 34	89.04 46	91.98 61	97.19 21	85.43 38	99.56 3	92.06 38	98.79 12	98.44 20
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		94.45 14	94.20 21	95.19 6	98.46 12	87.50 9	95.00 90	97.12 27	87.13 90	92.51 51	96.30 55	89.24 8	99.34 24	93.46 13	98.62 33	98.73 3

PGM-MVS		93.96 30	93.72 33	94.68 28	98.43 13	86.22 40	95.30 65	97.78 1	87.45 85	93.26 32	97.33 12	84.62 48	99.51 15	90.75 62	98.57 35	98.32 26

zzz-MVS		94.47 13	94.30 15	95.00 10	98.42 14	86.95 13	95.06 86	96.97 35	91.07 14	93.14 35	97.56 7	84.30 50	99.56 3	93.43 14	98.75 17	98.47 15

MTAPA		94.42 18	94.22 18	95.00 10	98.42 14	86.95 13	94.36 143	96.97 35	91.07 14	93.14 35	97.56 7	84.30 50	99.56 3	93.43 14	98.75 17	98.47 15

HPM-MVS		94.02 28	93.88 28	94.43 38	98.39 16	85.78 50	97.25 5	97.07 31	86.90 101	92.62 48	96.80 36	84.85 47	99.17 36	92.43 27	98.65 31	98.33 25
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		94.34 19	94.21 20	94.74 27	98.39 16	86.64 26	97.60 1	97.24 20	88.53 60	92.73 44	97.23 17	85.20 41	99.32 28	92.15 35	98.83 11	98.25 35

HPM-MVS_fast		93.40 42	93.22 40	93.94 49	98.36 18	84.83 58	97.15 7	96.80 50	85.77 119	92.47 52	97.13 25	82.38 62	99.07 45	90.51 64	98.40 40	97.92 59

DP-MVS Recon		91.95 60	91.28 63	93.96 48	98.33 19	85.92 45	94.66 117	96.66 63	82.69 200	90.03 87	95.82 75	82.30 64	99.03 51	84.57 122	96.48 78	96.91 94

APDe-MVS		95.46 1	95.64 1	94.91 13	98.26 20	86.29 39	97.46 2	97.40 9	89.03 47	96.20 5	98.10 1	89.39 7	99.34 24	95.88 1	99.03 2	99.10 1

TSAR-MVS + MP.		94.85 9	94.94 8	94.58 32	98.25 21	86.33 35	96.11 33	96.62 66	88.14 70	96.10 6	96.96 29	89.09 9	98.94 66	94.48 5	98.68 25	98.48 14

HPM-MVS++		95.14 7	94.91 9	95.83 1	98.25 21	89.65 1	95.92 43	96.96 38	91.75 8	94.02 20	96.83 33	88.12 12	99.55 8	93.41 16	98.94 6	98.28 29

CPTT-MVS		91.99 59	91.80 58	92.55 88	98.24 23	81.98 128	96.76 19	96.49 72	81.89 216	90.24 83	96.44 52	78.59 103	98.61 87	89.68 68	97.85 54	97.06 89

MP-MVS-pluss		94.21 25	94.00 27	94.85 17	98.17 24	86.65 25	94.82 102	97.17 25	86.26 111	92.83 39	97.87 3	85.57 37	99.56 3	94.37 7	98.92 7	98.34 24
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SMA-MVS		95.20 5	95.10 7	95.51 3	98.14 25	88.26 4	96.26 28	97.31 17	86.04 116	97.82 1	98.10 1	88.43 11	99.56 3	94.66 4	99.13 1	98.71 4

CNVR-MVS		95.40 2	95.37 4	95.50 4	98.11 26	88.51 3	95.29 67	96.96 38	92.09 3	95.32 10	97.08 26	89.49 6	99.33 27	95.10 2	98.85 9	98.66 7

114514_t		89.51 106	88.50 114	92.54 89	98.11 26	81.99 127	95.16 80	96.36 80	70.19 326	85.81 152	95.25 89	76.70 120	98.63 85	82.07 158	96.86 69	97.00 91

ACMMP		93.24 48	92.88 49	94.30 42	98.09 28	85.33 54	96.86 17	97.45 6	88.33 63	90.15 85	97.03 27	81.44 76	99.51 15	90.85 61	95.74 84	98.04 49
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
APD-MVS		94.24 23	94.07 25	94.75 26	98.06 29	86.90 16	95.88 44	96.94 40	85.68 122	95.05 12	97.18 22	87.31 20	99.07 45	91.90 46	98.61 34	98.28 29
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CSCG		93.23 49	93.05 43	93.76 56	98.04 30	84.07 78	96.22 29	97.37 10	84.15 154	90.05 86	95.66 80	87.77 14	99.15 39	89.91 67	98.27 43	98.07 46

ACMMP_Plus		94.74 11	94.56 12	95.28 5	98.02 31	87.70 5	95.68 52	97.34 11	88.28 65	95.30 11	97.67 4	85.90 34	99.54 11	93.91 10	98.95 5	98.60 9

APD-MVS_3200maxsize		93.78 33	93.77 32	93.80 55	97.92 32	84.19 76	96.30 26	96.87 46	86.96 97	93.92 22	97.47 9	83.88 54	98.96 65	92.71 25	97.87 53	98.26 34

NCCC		94.81 10	94.69 11	95.17 7	97.83 33	87.46 10	95.66 54	96.93 41	92.34 2	93.94 21	96.58 46	87.74 15	99.44 21	92.83 23	98.40 40	98.62 8

CDPH-MVS		92.83 53	92.30 55	94.44 36	97.79 34	86.11 43	94.06 168	96.66 63	80.09 244	92.77 41	96.63 43	86.62 26	99.04 50	87.40 92	98.66 29	98.17 38

DP-MVS		87.25 186	85.36 207	92.90 77	97.65 35	83.24 97	94.81 103	92.00 257	74.99 292	81.92 249	95.00 96	72.66 184	99.05 47	66.92 302	92.33 140	96.40 105

PAPM_NR		91.22 73	90.78 74	92.52 90	97.60 36	81.46 137	94.37 139	96.24 86	86.39 109	87.41 124	94.80 104	82.06 71	98.48 93	82.80 146	95.37 91	97.61 69

TEST9					97.53 37	86.49 30	94.07 165	96.78 51	81.61 229	92.77 41	96.20 60	87.71 16	99.12 42

train_agg		93.44 40	93.08 42	94.52 34	97.53 37	86.49 30	94.07 165	96.78 51	81.86 224	92.77 41	96.20 60	87.63 17	99.12 42	92.14 36	98.69 22	97.94 55

abl_6		93.18 50	93.05 43	93.57 59	97.52 39	84.27 75	95.53 60	96.67 62	87.85 76	93.20 34	97.22 18	80.35 83	99.18 35	91.91 43	97.21 63	97.26 78

test_8					97.49 40	86.30 38	94.02 171	96.76 54	81.86 224	92.70 45	96.20 60	87.63 17	99.02 54

agg_prior3		93.27 45	92.89 48	94.40 40	97.49 40	86.12 42	94.07 165	96.73 55	81.46 232	92.46 53	96.05 68	86.90 24	99.15 39	92.14 36	98.69 22	97.94 55

DeepC-MVS_fast	89.43 2	94.04 27	93.79 30	94.80 23	97.48 42	86.78 19	95.65 57	96.89 43	89.40 38	92.81 40	96.97 28	85.37 39	99.24 32	90.87 60	98.69 22	98.38 23
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		89.89 99	89.07 102	92.37 97	97.41 43	83.03 103	94.42 132	95.92 108	82.81 196	86.34 145	94.65 107	73.89 167	99.02 54	80.69 178	95.51 87	95.05 148

agg_prior1		93.29 44	92.97 46	94.26 43	97.38 44	85.92 45	93.92 176	96.72 57	81.96 211	92.16 57	96.23 58	87.85 13	98.97 62	91.95 42	98.55 38	97.90 60

agg_prior					97.38 44	85.92 45		96.72 57		92.16 57			98.97 62

原ACMM1				92.01 108	97.34 46	81.05 149		96.81 49	78.89 254	90.45 81	95.92 71	82.65 60	98.84 76	80.68 179	98.26 44	96.14 112

MSLP-MVS++		93.72 34	94.08 24	92.65 84	97.31 47	83.43 93	95.79 47	97.33 14	90.03 27	93.58 28	96.96 29	84.87 46	97.76 143	92.19 34	98.66 29	96.76 98

新几何1				93.10 67	97.30 48	84.35 74		95.56 135	71.09 323	91.26 73	96.24 57	82.87 59	98.86 71	79.19 209	98.10 48	96.07 118

test_prior3		93.60 37	93.53 36	93.82 52	97.29 49	84.49 65	94.12 157	96.88 44	87.67 81	92.63 46	96.39 53	86.62 26	98.87 68	91.50 50	98.67 27	98.11 44

test_prior				93.82 52	97.29 49	84.49 65		96.88 44					98.87 68			98.11 44

1121		90.42 87	89.49 91	93.20 63	97.27 51	84.46 68	92.63 232	95.51 142	71.01 324	91.20 74	96.21 59	82.92 58	99.05 47	80.56 181	98.07 49	96.10 116

PLC	84.53 7	89.06 122	88.03 128	92.15 105	97.27 51	82.69 117	94.29 144	95.44 151	79.71 248	84.01 217	94.18 121	76.68 121	98.75 80	77.28 226	93.41 124	95.02 149
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		94.96 8	95.33 5	93.88 50	97.25 53	86.69 22	96.19 30	97.11 29	90.42 24	96.95 2	97.27 14	89.53 5	96.91 221	94.38 6	98.85 9	98.03 50

test12				94.34 41	97.13 54	86.15 41		96.29 82		91.04 76		85.08 42	99.01 56		98.13 47	97.86 61

MG-MVS		91.77 62	91.70 59	92.00 110	97.08 55	80.03 175	93.60 197	95.18 171	87.85 76	90.89 77	96.47 51	82.06 71	98.36 97	85.07 114	97.04 66	97.62 68

SteuartSystems-ACMMP		95.20 5	95.32 6	94.85 17	96.99 56	86.33 35	97.33 3	97.30 18	91.38 12	95.39 9	97.46 10	88.98 10	99.40 22	94.12 8	98.89 8	98.82 2
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		93.45 39	93.31 38	93.84 51	96.99 56	84.84 57	93.24 213	97.24 20	88.76 53	91.60 69	95.85 74	86.07 32	98.66 82	91.91 43	98.16 46	98.03 50

CNLPA		89.07 120	87.98 129	92.34 98	96.87 58	84.78 59	94.08 163	93.24 231	81.41 233	84.46 204	95.13 94	75.57 145	96.62 237	77.21 227	93.84 115	95.61 136

PHI-MVS		93.89 32	93.65 34	94.62 31	96.84 59	86.43 32	96.69 21	97.49 4	85.15 133	93.56 30	96.28 56	85.60 36	99.31 29	92.45 26	98.79 12	98.12 43

旧先验1					96.79 60	81.81 130		95.67 127			96.81 34	86.69 25			97.66 57	96.97 92

LFMVS		90.08 92	89.13 101	92.95 75	96.71 61	82.32 123	96.08 34	89.91 311	86.79 102	92.15 59	96.81 34	62.60 287	98.34 100	87.18 96	93.90 113	98.19 37

Anonymous202405211		87.68 161	86.13 186	92.31 99	96.66 62	80.74 159	94.87 99	91.49 275	80.47 240	89.46 92	95.44 83	54.72 323	98.23 104	82.19 156	89.89 170	97.97 53

TAPA-MVS	84.62 6	88.16 142	87.01 153	91.62 126	96.64 63	80.65 160	94.39 135	96.21 90	76.38 278	86.19 148	95.44 83	79.75 90	98.08 127	62.75 323	95.29 93	96.13 113
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MAR-MVS		90.30 88	89.37 95	93.07 71	96.61 64	84.48 67	95.68 52	95.67 127	82.36 204	87.85 113	92.85 166	76.63 122	98.80 78	80.01 191	96.68 72	95.91 123
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
VNet		92.24 58	91.91 57	93.24 62	96.59 65	83.43 93	94.84 101	96.44 73	89.19 43	94.08 19	95.90 72	77.85 114	98.17 109	88.90 74	93.38 125	98.13 42

TSAR-MVS + GP.		93.66 36	93.41 37	94.41 39	96.59 65	86.78 19	94.40 133	93.93 221	89.77 32	94.21 16	95.59 82	87.35 19	98.61 87	92.72 24	96.15 81	97.83 63

test222					96.55 67	81.70 131	92.22 246	95.01 176	68.36 330	90.20 84	96.14 65	80.26 86			97.80 55	96.05 120

Anonymous20240529		88.09 145	86.59 176	92.58 87	96.53 68	81.92 129	95.99 37	95.84 116	74.11 300	89.06 97	95.21 91	61.44 295	98.81 77	83.67 136	87.47 210	97.01 90

DeepPCF-MVS	89.96 1	94.20 26	94.77 10	92.49 91	96.52 69	80.00 176	94.00 173	97.08 30	90.05 26	95.65 8	97.29 13	89.66 4	98.97 62	93.95 9	98.71 20	98.50 12

testdata				90.49 165	96.40 70	77.89 247		95.37 157	72.51 313	93.63 26	96.69 39	82.08 69	97.65 148	83.08 140	97.39 61	95.94 122

PVSNet_Blended_VisFu		91.38 70	90.91 71	92.80 80	96.39 71	83.17 99	94.87 99	96.66 63	83.29 177	89.27 93	94.46 111	80.29 85	99.17 36	87.57 90	95.37 91	96.05 120

API-MVS		90.66 81	90.07 83	92.45 93	96.36 72	84.57 63	96.06 35	95.22 170	82.39 202	89.13 94	94.27 119	80.32 84	98.46 94	80.16 190	96.71 71	94.33 192

F-COLMAP		87.95 151	86.80 160	91.40 132	96.35 73	80.88 155	94.73 108	95.45 149	79.65 249	82.04 247	94.61 108	71.13 201	98.50 92	76.24 237	91.05 152	94.80 167

VDD-MVS		90.74 79	89.92 87	93.20 63	96.27 74	83.02 104	95.73 49	93.86 222	88.42 62	92.53 49	96.84 32	62.09 290	98.64 84	90.95 59	92.62 138	97.93 58

OMC-MVS		91.23 72	90.62 75	93.08 69	96.27 74	84.07 78	93.52 199	95.93 107	86.95 98	89.51 90	96.13 66	78.50 105	98.35 99	85.84 109	92.90 135	96.83 97

view600		87.62 170	86.65 169	90.53 157	96.19 76	78.52 226	95.29 67	91.09 282	87.08 92	87.84 114	93.03 160	68.86 238	98.11 115	69.44 283	91.02 154	94.96 154

view800		87.62 170	86.65 169	90.53 157	96.19 76	78.52 226	95.29 67	91.09 282	87.08 92	87.84 114	93.03 160	68.86 238	98.11 115	69.44 283	91.02 154	94.96 154

conf0.05thres1000		87.62 170	86.65 169	90.53 157	96.19 76	78.52 226	95.29 67	91.09 282	87.08 92	87.84 114	93.03 160	68.86 238	98.11 115	69.44 283	91.02 154	94.96 154

tfpn		87.62 170	86.65 169	90.53 157	96.19 76	78.52 226	95.29 67	91.09 282	87.08 92	87.84 114	93.03 160	68.86 238	98.11 115	69.44 283	91.02 154	94.96 154

CHOSEN 1792x2688		88.84 127	87.69 133	92.30 100	96.14 80	81.42 139	90.01 278	95.86 115	74.52 297	87.41 124	93.94 130	75.46 147	98.36 97	80.36 185	95.53 86	97.12 87

tfpn111		87.63 167	86.68 167	90.47 167	96.12 81	78.55 222	95.03 87	91.58 268	87.15 87	88.06 108	92.29 186	68.91 234	98.15 112	69.88 281	91.10 146	94.71 169

conf200view11		87.65 163	86.71 164	90.46 169	96.12 81	78.55 222	95.03 87	91.58 268	87.15 87	88.06 108	92.29 186	68.91 234	98.10 119	70.13 276	91.10 146	94.71 169

thres100view900		87.63 167	86.71 164	90.38 173	96.12 81	78.55 222	95.03 87	91.58 268	87.15 87	88.06 108	92.29 186	68.91 234	98.10 119	70.13 276	91.10 146	94.48 188

PVSNet_BlendedMVS		89.98 94	89.70 88	90.82 151	96.12 81	81.25 142	93.92 176	96.83 47	83.49 171	89.10 95	92.26 189	81.04 80	98.85 74	86.72 105	87.86 208	92.35 279

PVSNet_Blended		90.73 80	90.32 78	91.98 111	96.12 81	81.25 142	92.55 236	96.83 47	82.04 210	89.10 95	92.56 176	81.04 80	98.85 74	86.72 105	95.91 82	95.84 127

UA-Net		92.83 53	92.54 53	93.68 57	96.10 86	84.71 60	95.66 54	96.39 78	91.92 4	93.22 33	96.49 50	83.16 56	98.87 68	84.47 123	95.47 89	97.45 75

thres600view7		87.65 163	86.67 168	90.59 154	96.08 87	78.72 218	94.88 98	91.58 268	87.06 96	88.08 107	92.30 185	68.91 234	98.10 119	70.05 280	91.10 146	94.96 154

DeepC-MVS	88.79 3	93.31 43	92.99 45	94.26 43	96.07 88	85.83 49	94.89 96	96.99 33	89.02 48	89.56 89	97.37 11	82.51 61	99.38 23	92.20 33	98.30 42	97.57 71
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		87.89 152	86.32 182	92.59 86	96.07 88	82.92 108	95.23 75	94.92 184	75.66 285	82.89 235	95.98 69	72.48 188	99.21 33	68.43 293	95.23 95	95.64 135

HyFIR lowres test		88.09 145	86.81 159	91.93 114	96.00 90	80.63 161	90.01 278	95.79 120	73.42 304	87.68 122	92.10 195	73.86 168	97.96 134	80.75 177	91.70 142	97.19 83

tfpn200view9		87.58 176	86.64 173	90.41 170	95.99 91	78.64 220	94.58 120	91.98 259	86.94 99	88.09 105	91.77 206	69.18 231	98.10 119	70.13 276	91.10 146	94.48 188

thres400		87.62 170	86.64 173	90.57 155	95.99 91	78.64 220	94.58 120	91.98 259	86.94 99	88.09 105	91.77 206	69.18 231	98.10 119	70.13 276	91.10 146	94.96 154

MVS_111021_LR		92.47 56	92.29 56	92.98 74	95.99 91	84.43 72	93.08 218	96.09 95	88.20 69	91.12 75	95.72 79	81.33 78	97.76 143	91.74 47	97.37 62	96.75 99

PatchMatch-RL		86.77 200	85.54 200	90.47 167	95.88 94	82.71 116	90.54 271	92.31 247	79.82 247	84.32 211	91.57 216	68.77 242	96.39 250	73.16 260	93.48 123	92.32 280

EPP-MVSNet		91.70 66	91.56 60	92.13 107	95.88 94	80.50 166	97.33 3	95.25 164	86.15 113	89.76 88	95.60 81	83.42 55	98.32 102	87.37 94	93.25 128	97.56 72

IS-MVSNet		91.43 69	91.09 68	92.46 92	95.87 96	81.38 140	96.95 9	93.69 226	89.72 34	89.50 91	95.98 69	78.57 104	97.77 142	83.02 142	96.50 77	98.22 36

PAPR		90.02 93	89.27 99	92.29 101	95.78 97	80.95 153	92.68 231	96.22 87	81.91 214	86.66 138	93.75 141	82.23 65	98.44 96	79.40 208	94.79 97	97.48 74

Vis-MVSNet (Re-imp)		89.59 104	89.44 93	90.03 195	95.74 98	75.85 276	95.61 58	90.80 295	87.66 83	87.83 118	95.40 86	76.79 119	96.46 247	78.37 214	96.73 70	97.80 64

MVS_0304		93.25 47	92.62 51	95.14 9	95.72 99	87.58 8	94.71 113	96.59 68	91.78 7	91.46 70	96.18 64	75.45 148	99.55 8	93.53 11	98.19 45	98.28 29

canonicalmvs		93.27 45	92.75 50	94.85 17	95.70 100	87.66 6	96.33 25	96.41 76	90.00 28	94.09 18	94.60 109	82.33 63	98.62 86	92.40 29	92.86 136	98.27 32

CANet		93.54 38	93.20 41	94.55 33	95.65 101	85.73 51	94.94 93	96.69 61	91.89 5	90.69 79	95.88 73	81.99 73	99.54 11	93.14 21	97.95 52	98.39 22

3Dnovator+	87.14 4	92.42 57	91.37 61	95.55 2	95.63 102	88.73 2	97.07 8	96.77 53	90.84 17	84.02 216	96.62 44	75.95 137	99.34 24	87.77 87	97.68 56	98.59 10

alignmvs		93.08 51	92.50 54	94.81 22	95.62 103	87.61 7	95.99 37	96.07 97	89.77 32	94.12 17	94.87 99	80.56 82	98.66 82	92.42 28	93.10 131	98.15 40

tfpn1000		86.06 212	84.92 216	89.49 219	95.54 104	77.79 250	94.72 111	89.07 326	82.05 208	85.36 184	91.94 202	68.32 257	96.65 235	67.04 299	90.24 164	94.02 206

Regformer-1		94.22 24	94.13 23	94.51 35	95.54 104	86.36 34	94.57 122	96.44 73	91.69 10	94.32 15	96.56 48	87.05 23	99.03 51	93.35 17	97.65 58	98.15 40

Regformer-2		94.33 20	94.22 18	94.68 28	95.54 104	86.75 21	94.57 122	96.70 59	91.84 6	94.41 13	96.56 48	87.19 21	99.13 41	93.50 12	97.65 58	98.16 39

tfpn_ndepth		86.10 211	84.98 212	89.43 221	95.52 107	78.29 237	94.62 118	89.60 317	81.88 223	85.43 176	90.54 252	68.47 248	96.85 225	68.46 292	90.34 163	93.15 255

WTY-MVS		89.60 103	88.92 106	91.67 125	95.47 108	81.15 147	92.38 241	94.78 191	83.11 180	89.06 97	94.32 114	78.67 102	96.61 239	81.57 167	90.89 158	97.24 79

DELS-MVS		93.43 41	93.25 39	93.97 47	95.42 109	85.04 56	93.06 220	97.13 26	90.74 20	91.84 63	95.09 95	86.32 29	99.21 33	91.22 54	98.45 39	97.65 67
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
Regformer-3		93.68 35	93.64 35	93.81 54	95.36 110	84.61 61	94.68 114	95.83 117	91.27 13	93.60 27	96.71 37	85.75 35	98.86 71	92.87 22	96.65 73	97.96 54

Regformer-4		93.91 31	93.81 29	94.19 45	95.36 110	85.47 52	94.68 114	96.41 76	91.60 11	93.75 23	96.71 37	85.95 33	99.10 44	93.21 18	96.65 73	98.01 52

thres200		87.21 189	86.24 185	90.12 186	95.36 110	78.53 225	93.26 211	92.10 252	86.42 108	88.00 111	91.11 241	69.24 230	98.00 132	69.58 282	91.04 153	93.83 217

Vis-MVSNet		91.75 63	91.23 65	93.29 60	95.32 113	83.78 83	96.14 32	95.98 104	89.89 29	90.45 81	96.58 46	75.09 152	98.31 103	84.75 120	96.90 67	97.78 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
BH-RMVSNet		88.37 136	87.48 136	91.02 146	95.28 114	79.45 192	92.89 226	93.07 234	85.45 126	86.91 133	94.84 103	70.35 215	97.76 143	73.97 255	94.59 102	95.85 126

COLMAP_ROB	80.39 16	83.96 259	82.04 265	89.74 206	95.28 114	79.75 182	94.25 146	92.28 248	75.17 290	78.02 285	93.77 139	58.60 311	97.84 140	65.06 316	85.92 222	91.63 292
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
conf0.01		85.83 219	84.54 225	89.71 208	95.26 116	77.63 256	94.21 149	89.33 319	81.89 216	84.94 191	91.51 220	68.43 250	96.80 226	66.05 305	89.23 182	94.71 169

conf0.002		85.83 219	84.54 225	89.71 208	95.26 116	77.63 256	94.21 149	89.33 319	81.89 216	84.94 191	91.51 220	68.43 250	96.80 226	66.05 305	89.23 182	94.71 169

thresconf0.02		85.75 223	84.54 225	89.38 224	95.26 116	77.63 256	94.21 149	89.33 319	81.89 216	84.94 191	91.51 220	68.43 250	96.80 226	66.05 305	89.23 182	93.70 227

tfpn_n400		85.75 223	84.54 225	89.38 224	95.26 116	77.63 256	94.21 149	89.33 319	81.89 216	84.94 191	91.51 220	68.43 250	96.80 226	66.05 305	89.23 182	93.70 227

tfpnconf		85.75 223	84.54 225	89.38 224	95.26 116	77.63 256	94.21 149	89.33 319	81.89 216	84.94 191	91.51 220	68.43 250	96.80 226	66.05 305	89.23 182	93.70 227

tfpnview11		85.75 223	84.54 225	89.38 224	95.26 116	77.63 256	94.21 149	89.33 319	81.89 216	84.94 191	91.51 220	68.43 250	96.80 226	66.05 305	89.23 182	93.70 227

PS-MVSNAJ		91.18 74	90.92 70	91.96 112	95.26 116	82.60 120	92.09 251	95.70 126	86.27 110	91.84 63	92.46 177	79.70 92	98.99 60	89.08 72	95.86 83	94.29 193

BH-untuned		88.60 132	88.13 127	90.01 197	95.24 123	78.50 231	93.29 209	94.15 208	84.75 141	84.46 204	93.40 143	75.76 142	97.40 180	77.59 223	94.52 104	94.12 199

ab-mvs		89.41 112	88.35 118	92.60 85	95.15 124	82.65 118	92.20 247	95.60 133	83.97 157	88.55 101	93.70 142	74.16 164	98.21 107	82.46 152	89.37 178	96.94 93

VDDNet		89.56 105	88.49 116	92.76 82	95.07 125	82.09 125	96.30 26	93.19 232	81.05 237	91.88 62	96.86 31	61.16 300	98.33 101	88.43 79	92.49 139	97.84 62

AllTest		83.42 264	81.39 268	89.52 216	95.01 126	77.79 250	93.12 215	90.89 293	77.41 270	76.12 302	93.34 144	54.08 326	97.51 156	68.31 294	84.27 237	93.26 249

TestCases				89.52 216	95.01 126	77.79 250		90.89 293	77.41 270	76.12 302	93.34 144	54.08 326	97.51 156	68.31 294	84.27 237	93.26 249

EI-MVSNet-Vis-set		93.01 52	92.92 47	93.29 60	95.01 126	83.51 92	94.48 125	95.77 121	90.87 16	92.52 50	96.67 41	84.50 49	99.00 59	91.99 39	94.44 108	97.36 76

xiu_mvs_v2_base		91.13 75	90.89 72	91.86 117	94.97 129	82.42 121	92.24 245	95.64 132	86.11 115	91.74 68	93.14 155	79.67 95	98.89 67	89.06 73	95.46 90	94.28 194

Test_1112_low_res		87.65 163	86.51 178	91.08 142	94.94 130	79.28 206	91.77 254	94.30 204	76.04 283	83.51 228	92.37 182	77.86 113	97.73 147	78.69 213	89.13 190	96.22 110

1112_ss		88.42 134	87.33 140	91.72 123	94.92 131	80.98 151	92.97 224	94.54 196	78.16 267	83.82 220	93.88 135	78.78 100	97.91 138	79.45 204	89.41 177	96.26 109

QAPM		89.51 106	88.15 126	93.59 58	94.92 131	84.58 62	96.82 18	96.70 59	78.43 262	83.41 230	96.19 63	73.18 178	99.30 30	77.11 229	96.54 76	96.89 96

BH-w/o		87.57 177	87.05 152	89.12 233	94.90 133	77.90 246	92.41 239	93.51 228	82.89 195	83.70 223	91.34 227	75.75 143	97.07 208	75.49 241	93.49 121	92.39 277

EI-MVSNet-UG-set		92.74 55	92.62 51	93.12 66	94.86 134	83.20 98	94.40 133	95.74 124	90.71 21	92.05 60	96.60 45	84.00 52	98.99 60	91.55 49	93.63 118	97.17 84

HY-MVS	83.01 12	89.03 123	87.94 131	92.29 101	94.86 134	82.77 110	92.08 252	94.49 197	81.52 231	86.93 132	92.79 172	78.32 108	98.23 104	79.93 194	90.55 159	95.88 125

Fast-Effi-MVS+		89.41 112	88.64 111	91.71 124	94.74 136	80.81 157	93.54 198	95.10 173	83.11 180	86.82 136	90.67 248	79.74 91	97.75 146	80.51 183	93.55 119	96.57 103

ACMP	84.23 8	89.01 125	88.35 118	90.99 148	94.73 137	81.27 141	95.07 84	95.89 113	86.48 106	83.67 224	94.30 115	69.33 226	97.99 133	87.10 101	88.55 195	93.72 226
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet	78.82 18	85.55 229	84.65 223	88.23 262	94.72 138	71.93 305	87.12 312	92.75 240	78.80 257	84.95 190	90.53 254	64.43 282	96.71 234	74.74 249	93.86 114	96.06 119

LCM-MVSNet-Re		88.30 139	88.32 121	88.27 259	94.71 139	72.41 304	93.15 214	90.98 290	87.77 78	79.25 279	91.96 201	78.35 107	95.75 275	83.04 141	95.62 85	96.65 101

HQP_MVS		90.60 85	90.19 80	91.82 120	94.70 140	82.73 114	95.85 45	96.22 87	90.81 18	86.91 133	94.86 100	74.23 160	98.12 113	88.15 81	89.99 167	94.63 174

plane_prior7					94.70 140	82.74 113

casdiffmvs		91.72 65	91.26 64	93.10 67	94.66 142	83.75 84	94.77 106	96.00 103	83.98 156	90.74 78	93.96 129	82.08 69	98.19 108	91.47 52	93.68 116	97.36 76

ACMH+	81.04 14	85.05 238	83.46 250	89.82 202	94.66 142	79.37 200	94.44 130	94.12 210	82.19 206	78.04 284	92.82 169	58.23 312	97.54 154	73.77 257	82.90 251	92.54 271

ACMM	84.12 9	89.14 118	88.48 117	91.12 139	94.65 144	81.22 144	95.31 63	96.12 94	85.31 129	85.92 151	94.34 112	70.19 218	98.06 129	85.65 110	88.86 193	94.08 203
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
plane_prior1					94.59 145

3Dnovator	86.66 5	91.73 64	90.82 73	94.44 36	94.59 145	86.37 33	97.18 6	97.02 32	89.20 42	84.31 212	96.66 42	73.74 171	99.17 36	86.74 102	97.96 51	97.79 65

plane_prior6					94.52 147	82.75 111						74.23 160

UGNet		89.95 96	88.95 105	92.95 75	94.51 148	83.31 96	95.70 51	95.23 168	89.37 39	87.58 123	93.94 130	64.00 283	98.78 79	83.92 133	96.31 80	96.74 100
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
LPG-MVS_test		89.45 109	88.90 107	91.12 139	94.47 149	81.49 135	95.30 65	96.14 91	86.73 103	85.45 173	95.16 92	69.89 219	98.10 119	87.70 88	89.23 182	93.77 222

LGP-MVS_train				91.12 139	94.47 149	81.49 135		96.14 91	86.73 103	85.45 173	95.16 92	69.89 219	98.10 119	87.70 88	89.23 182	93.77 222

ACMH	80.38 17	85.36 231	83.68 243	90.39 171	94.45 151	80.63 161	94.73 108	94.85 187	82.09 207	77.24 291	92.65 174	60.01 306	97.58 151	72.25 264	84.87 232	92.96 260
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	82.13 13	86.26 209	84.90 217	90.34 176	94.44 152	81.50 134	92.31 243	94.89 185	83.03 187	79.63 276	92.67 173	69.69 222	97.79 141	71.20 268	86.26 221	91.72 290
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
MVS_Test		91.31 71	91.11 66	91.93 114	94.37 153	80.14 170	93.46 202	95.80 119	86.46 107	91.35 72	93.77 139	82.21 66	98.09 126	87.57 90	94.95 96	97.55 73

NP-MVS					94.37 153	82.42 121					93.98 127

TR-MVS		86.78 198	85.76 198	89.82 202	94.37 153	78.41 233	92.47 238	92.83 237	81.11 236	86.36 144	92.40 180	68.73 243	97.48 158	73.75 258	89.85 172	93.57 240

Effi-MVS+		91.59 68	91.11 66	93.01 73	94.35 156	83.39 95	94.60 119	95.10 173	87.10 91	90.57 80	93.10 157	81.43 77	98.07 128	89.29 71	94.48 105	97.59 70

CLD-MVS		89.47 108	88.90 107	91.18 138	94.22 157	82.07 126	92.13 249	96.09 95	87.90 74	85.37 183	92.45 178	74.38 158	97.56 153	87.15 97	90.43 160	93.93 208
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-NCC					94.17 158		94.39 135		88.81 50	85.43 176

ACMP_Plane					94.17 158		94.39 135		88.81 50	85.43 176

HQP-MVS		89.80 100	89.28 98	91.34 133	94.17 158	81.56 132	94.39 135	96.04 101	88.81 50	85.43 176	93.97 128	73.83 169	97.96 134	87.11 99	89.77 173	94.50 185

XVG-OURS		89.40 114	88.70 110	91.52 128	94.06 161	81.46 137	91.27 266	96.07 97	86.14 114	88.89 99	95.77 77	68.73 243	97.26 193	87.39 93	89.96 169	95.83 128

sss		88.93 126	88.26 125	90.94 150	94.05 162	80.78 158	91.71 257	95.38 155	81.55 230	88.63 100	93.91 134	75.04 153	95.47 286	82.47 151	91.61 143	96.57 103

PCF-MVS	84.11 10	87.74 160	86.08 190	92.70 83	94.02 163	84.43 72	89.27 289	95.87 114	73.62 303	84.43 206	94.33 113	78.48 106	98.86 71	70.27 272	94.45 107	94.81 166
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GBi-Net		87.26 184	85.98 192	91.08 142	94.01 164	83.10 100	95.14 81	94.94 180	83.57 167	84.37 207	91.64 209	66.59 266	96.34 253	78.23 217	85.36 227	93.79 218

test1		87.26 184	85.98 192	91.08 142	94.01 164	83.10 100	95.14 81	94.94 180	83.57 167	84.37 207	91.64 209	66.59 266	96.34 253	78.23 217	85.36 227	93.79 218

FMVSNet2		87.19 190	85.82 197	91.30 135	94.01 164	83.67 87	94.79 104	94.94 180	83.57 167	83.88 218	92.05 199	66.59 266	96.51 243	77.56 224	85.01 231	93.73 225

XVG-OURS-SEG-HR		89.95 96	89.45 92	91.47 130	94.00 167	81.21 145	91.87 253	96.06 99	85.78 118	88.55 101	95.73 78	74.67 156	97.27 191	88.71 76	89.64 175	95.91 123

FIs		90.51 86	90.35 77	90.99 148	93.99 168	80.98 151	95.73 49	97.54 3	89.15 44	86.72 137	94.68 105	81.83 75	97.24 195	85.18 113	88.31 203	94.76 168

xiu_mvs_v1_base_debu		90.64 82	90.05 84	92.40 94	93.97 169	84.46 68	93.32 204	95.46 145	85.17 130	92.25 54	94.03 122	70.59 210	98.57 89	90.97 56	94.67 98	94.18 195

xiu_mvs_v1_base		90.64 82	90.05 84	92.40 94	93.97 169	84.46 68	93.32 204	95.46 145	85.17 130	92.25 54	94.03 122	70.59 210	98.57 89	90.97 56	94.67 98	94.18 195

xiu_mvs_v1_base_debi		90.64 82	90.05 84	92.40 94	93.97 169	84.46 68	93.32 204	95.46 145	85.17 130	92.25 54	94.03 122	70.59 210	98.57 89	90.97 56	94.67 98	94.18 195

VPA-MVSNet		89.62 102	88.96 104	91.60 127	93.86 172	82.89 109	95.46 61	97.33 14	87.91 73	88.43 103	93.31 147	74.17 163	97.40 180	87.32 95	82.86 252	94.52 183

MVSFormer		91.68 67	91.30 62	92.80 80	93.86 172	83.88 81	95.96 41	95.90 111	84.66 143	91.76 66	94.91 97	77.92 111	97.30 187	89.64 69	97.11 64	97.24 79

lupinMVS		90.92 77	90.21 79	93.03 72	93.86 172	83.88 81	92.81 227	93.86 222	79.84 246	91.76 66	94.29 116	77.92 111	98.04 130	90.48 65	97.11 64	97.17 84

IterMVS-LS		88.36 137	87.91 132	89.70 210	93.80 175	78.29 237	93.73 188	95.08 175	85.73 120	84.75 198	91.90 204	79.88 88	96.92 220	83.83 134	82.51 254	93.89 210
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 245	83.09 256	90.14 185	93.80 175	80.05 173	89.18 292	93.09 233	78.89 254	78.19 282	91.91 203	65.86 276	97.27 191	68.47 291	88.45 199	93.11 256

FMVSNet3		87.40 182	86.11 188	91.30 135	93.79 177	83.64 88	94.20 155	94.81 190	83.89 158	84.37 207	91.87 205	68.45 249	96.56 240	78.23 217	85.36 227	93.70 227

FC-MVSNet-test		90.27 89	90.18 81	90.53 157	93.71 178	79.85 180	95.77 48	97.59 2	89.31 40	86.27 146	94.67 106	81.93 74	97.01 212	84.26 128	88.09 206	94.71 169

TAMVS		89.21 117	88.29 123	91.96 112	93.71 178	82.62 119	93.30 208	94.19 206	82.22 205	87.78 120	93.94 130	78.83 99	96.95 218	77.70 222	92.98 133	96.32 107

CDS-MVSNet		89.45 109	88.51 113	92.29 101	93.62 180	83.61 90	93.01 221	94.68 193	81.95 212	87.82 119	93.24 151	78.69 101	96.99 213	80.34 186	93.23 129	96.28 108
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UniMVSNet (Re)		89.80 100	89.07 102	92.01 108	93.60 181	84.52 64	94.78 105	97.47 5	89.26 41	86.44 143	92.32 184	82.10 68	97.39 183	84.81 119	80.84 283	94.12 199

VPNet		88.20 141	87.47 137	90.39 171	93.56 182	79.46 190	94.04 169	95.54 138	88.67 55	86.96 131	94.58 110	69.33 226	97.15 201	84.05 132	80.53 288	94.56 181

mvs_anonymous		89.37 115	89.32 96	89.51 218	93.47 183	74.22 283	91.65 260	94.83 189	82.91 194	85.45 173	93.79 138	81.23 79	96.36 252	86.47 108	94.09 111	97.94 55

CANet_DTU		90.26 90	89.41 94	92.81 79	93.46 184	83.01 105	93.48 200	94.47 198	89.43 37	87.76 121	94.23 120	70.54 214	99.03 51	84.97 115	96.39 79	96.38 106

UniMVSNet_NR-MVSNet		89.92 98	89.29 97	91.81 122	93.39 185	83.72 85	94.43 131	97.12 27	89.80 31	86.46 140	93.32 146	83.16 56	97.23 197	84.92 116	81.02 279	94.49 187

Effi-MVS+-dtu		88.65 131	88.35 118	89.54 215	93.33 186	76.39 271	94.47 128	94.36 201	87.70 79	85.43 176	89.56 270	73.45 174	97.26 193	85.57 111	91.28 145	94.97 151

mvs-test1		89.45 109	89.14 100	90.38 173	93.33 186	77.63 256	94.95 92	94.36 201	87.70 79	87.10 130	92.81 170	73.45 174	98.03 131	85.57 111	93.04 132	95.48 138

WR-MVS		88.38 135	87.67 134	90.52 163	93.30 188	80.18 168	93.26 211	95.96 106	88.57 59	85.47 172	92.81 170	76.12 126	96.91 221	81.24 169	82.29 256	94.47 190

diffmvs		89.07 120	88.32 121	91.34 133	93.24 189	79.79 181	92.29 244	94.98 179	80.24 241	87.38 127	92.45 178	78.02 109	97.33 185	83.29 139	92.93 134	96.91 94

WR-MVS_H		87.80 158	87.37 139	89.10 235	93.23 190	78.12 241	95.61 58	97.30 18	87.90 74	83.72 222	92.01 200	79.65 96	96.01 264	76.36 234	80.54 287	93.16 253

test_0402		81.30 287	79.17 291	87.67 271	93.19 191	78.17 240	92.98 223	91.71 264	75.25 289	76.02 305	90.31 258	59.23 309	96.37 251	50.22 341	83.63 244	88.47 335

OPM-MVS		90.12 91	89.56 90	91.82 120	93.14 192	83.90 80	94.16 156	95.74 124	88.96 49	87.86 112	95.43 85	72.48 188	97.91 138	88.10 84	90.18 166	93.65 232

CP-MVSNet		87.63 167	87.26 143	88.74 240	93.12 193	76.59 270	95.29 67	96.58 70	88.43 61	83.49 229	92.98 164	75.28 149	95.83 271	78.97 210	81.15 276	93.79 218

nrg030		91.08 76	90.39 76	93.17 65	93.07 194	86.91 15	96.41 24	96.26 83	88.30 64	88.37 104	94.85 102	82.19 67	97.64 150	91.09 55	82.95 250	94.96 154

PAPM		86.68 201	85.39 206	90.53 157	93.05 195	79.33 205	89.79 282	94.77 192	78.82 256	81.95 248	93.24 151	76.81 118	97.30 187	66.94 300	93.16 130	94.95 161

DU-MVS		89.34 116	88.50 114	91.85 118	93.04 196	83.72 85	94.47 128	96.59 68	89.50 36	86.46 140	93.29 149	77.25 115	97.23 197	84.92 116	81.02 279	94.59 178

NR-MVSNet		88.58 133	87.47 137	91.93 114	93.04 196	84.16 77	94.77 106	96.25 85	89.05 45	80.04 273	93.29 149	79.02 98	97.05 210	81.71 166	80.05 293	94.59 178

jason		90.80 78	90.10 82	92.90 77	93.04 196	83.53 91	93.08 218	94.15 208	80.22 242	91.41 71	94.91 97	76.87 117	97.93 137	90.28 66	96.90 67	97.24 79
jason: jason.
PS-CasMVS		87.32 183	86.88 155	88.63 243	92.99 199	76.33 273	95.33 62	96.61 67	88.22 68	83.30 232	93.07 158	73.03 180	95.79 274	78.36 215	81.00 281	93.75 224

MVSTER		88.84 127	88.29 123	90.51 164	92.95 200	80.44 167	93.73 188	95.01 176	84.66 143	87.15 128	93.12 156	72.79 182	97.21 199	87.86 86	87.36 213	93.87 213

RPSCF		85.07 237	84.27 231	87.48 277	92.91 201	70.62 317	91.69 259	92.46 245	76.20 282	82.67 238	95.22 90	63.94 284	97.29 190	77.51 225	85.80 224	94.53 182

Anonymous20240521		86.87 195	85.95 194	89.64 212	92.89 202	78.88 217	95.66 54	96.05 100	84.77 140	81.92 249	92.39 181	71.54 196	96.96 216	76.46 233	81.87 266	93.08 258

FMVSNet1		85.85 217	84.11 233	91.08 142	92.81 203	83.10 100	95.14 81	94.94 180	81.64 227	82.68 237	91.64 209	59.01 310	96.34 253	75.37 243	83.78 240	93.79 218

tfpnnormal		84.72 251	83.23 255	89.20 232	92.79 204	80.05 173	94.48 125	95.81 118	82.38 203	81.08 259	91.21 235	69.01 233	96.95 218	61.69 325	80.59 286	90.58 317

OpenMVS	83.78 11	88.74 130	87.29 141	93.08 69	92.70 205	85.39 53	96.57 22	96.43 75	78.74 259	80.85 261	96.07 67	69.64 223	99.01 56	78.01 220	96.65 73	94.83 165

TranMVSNet+NR-MVSNet		88.84 127	87.95 130	91.49 129	92.68 206	83.01 105	94.92 95	96.31 81	89.88 30	85.53 167	93.85 137	76.63 122	96.96 216	81.91 162	79.87 298	94.50 185

MVS		87.44 180	86.10 189	91.44 131	92.61 207	83.62 89	92.63 232	95.66 129	67.26 334	81.47 253	92.15 191	77.95 110	98.22 106	79.71 200	95.48 88	92.47 274

CHOSEN 280x420		85.15 236	83.99 235	88.65 242	92.47 208	78.40 234	79.68 344	92.76 239	74.90 294	81.41 255	89.59 268	69.85 221	95.51 282	79.92 195	95.29 93	92.03 284

1314		87.51 178	86.57 177	90.34 176	92.42 209	79.74 183	92.63 232	95.35 159	78.35 263	80.14 271	91.62 213	74.05 165	97.15 201	81.05 170	93.53 120	94.12 199

pcd1.5k->3k		37.02 334	38.84 335	31.53 347	92.33 210	0.00 367	0.00 359	96.13 93	0.00 362	0.00 363	0.00 364	72.70 183	0.00 365	0.00 362	88.43 200	94.60 177

PEN-MVS		86.80 197	86.27 184	88.40 256	92.32 211	75.71 277	95.18 78	96.38 79	87.97 71	82.82 236	93.15 154	73.39 176	95.92 267	76.15 238	79.03 302	93.59 239

Patchmatch-test1		85.81 221	84.71 221	89.12 233	92.15 212	76.60 269	91.12 269	91.69 266	83.53 170	85.50 170	88.56 283	66.79 264	95.00 303	72.69 262	90.35 162	95.76 131

XXY-MVS		87.65 163	86.85 157	90.03 195	92.14 213	80.60 163	93.76 185	95.23 168	82.94 192	84.60 200	94.02 125	74.27 159	95.49 285	81.04 171	83.68 243	94.01 207

IB-MVS	80.51 15	85.24 235	83.26 254	91.19 137	92.13 214	79.86 179	91.75 255	91.29 280	83.28 178	80.66 264	88.49 284	61.28 296	98.46 94	80.99 174	79.46 300	95.25 145
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
cascas		86.43 207	84.98 212	90.80 152	92.10 215	80.92 154	90.24 274	95.91 110	73.10 307	83.57 227	88.39 285	65.15 278	97.46 160	84.90 118	91.43 144	94.03 205

Fast-Effi-MVS+-dtu		87.44 180	86.72 163	89.63 213	92.04 216	77.68 255	94.03 170	93.94 220	85.81 117	82.42 239	91.32 230	70.33 216	97.06 209	80.33 187	90.23 165	94.14 198

PS-MVSNAJss		89.97 95	89.62 89	91.02 146	91.90 217	80.85 156	95.26 74	95.98 104	86.26 111	86.21 147	94.29 116	79.70 92	97.65 148	88.87 75	88.10 204	94.57 180

ITE_SJBPF				88.24 261	91.88 218	77.05 266		92.92 235	85.54 124	80.13 272	93.30 148	57.29 315	96.20 257	72.46 263	84.71 233	91.49 294

EI-MVSNet		89.10 119	88.86 109	89.80 205	91.84 219	78.30 236	93.70 192	95.01 176	85.73 120	87.15 128	95.28 87	79.87 89	97.21 199	83.81 135	87.36 213	93.88 212

CVMVSNet		84.69 253	84.79 220	84.37 311	91.84 219	64.92 335	93.70 192	91.47 276	66.19 336	86.16 149	95.28 87	67.18 262	93.33 319	80.89 176	90.42 161	94.88 163

MVS-HIRNet		73.70 311	72.20 311	78.18 325	91.81 221	56.42 347	82.94 338	82.58 347	55.24 346	68.88 330	66.48 347	55.32 321	95.13 299	58.12 332	88.42 201	83.01 342

PatchmatchNet		85.85 217	84.70 222	89.29 229	91.76 222	75.54 278	88.49 300	91.30 279	81.63 228	85.05 188	88.70 280	71.71 192	96.24 256	74.61 251	89.05 191	96.08 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TransMVSNet (Re)		84.43 256	83.06 257	88.54 252	91.72 223	78.44 232	95.18 78	92.82 238	82.73 198	79.67 275	92.12 192	73.49 173	95.96 266	71.10 271	68.73 337	91.21 299

semantic-postprocess				88.18 263	91.71 224	76.87 268		92.65 243	85.40 127	81.44 254	90.54 252	66.21 270	95.00 303	81.04 171	81.05 277	92.66 269

TinyColmap		79.76 297	77.69 297	85.97 299	91.71 224	73.12 293	89.55 283	90.36 301	75.03 291	72.03 325	90.19 259	46.22 340	96.19 258	63.11 321	81.03 278	88.59 331

MDTV_nov1_ep13			83.56 246		91.69 226	69.93 321	87.75 308	91.54 273	78.60 260	84.86 197	88.90 276	69.54 224	96.03 262	70.25 273	88.93 192

DTE-MVSNet		86.11 210	85.48 204	87.98 266	91.65 227	74.92 280	94.93 94	95.75 123	87.36 86	82.26 241	93.04 159	72.85 181	95.82 272	74.04 254	77.46 308	93.20 251

PatchFormer-LS_test		86.02 213	85.13 210	88.70 241	91.52 228	74.12 286	91.19 268	92.09 253	82.71 199	84.30 213	87.24 301	70.87 205	96.98 214	81.04 171	85.17 230	95.00 150

MIMVSNet		82.59 272	80.53 275	88.76 239	91.51 229	78.32 235	86.57 315	90.13 305	79.32 250	80.70 263	88.69 281	52.98 328	93.07 324	66.03 311	88.86 193	94.90 162

tpmp4_e23		83.87 262	82.33 263	88.48 253	91.46 230	72.82 296	89.82 281	91.57 272	73.02 309	81.86 251	89.05 273	66.20 271	96.97 215	71.57 266	86.39 220	95.66 134

pm-mvs1		86.61 202	85.54 200	89.82 202	91.44 231	80.18 168	95.28 73	94.85 187	83.84 159	81.66 252	92.62 175	72.45 190	96.48 245	79.67 201	78.06 304	92.82 266

Baseline_NR-MVSNet		87.07 192	86.63 175	88.40 256	91.44 231	77.87 248	94.23 148	92.57 244	84.12 155	85.74 158	92.08 196	77.25 115	96.04 261	82.29 155	79.94 296	91.30 298

IterMVS		84.88 244	83.98 236	87.60 272	91.44 231	76.03 275	90.18 276	92.41 246	83.24 179	81.06 260	90.42 257	66.60 265	94.28 309	79.46 203	80.98 282	92.48 273
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DWT-MVSNet_test		84.95 242	83.68 243	88.77 238	91.43 234	73.75 289	91.74 256	90.98 290	80.66 239	83.84 219	87.36 299	62.44 288	97.11 205	78.84 212	85.81 223	95.46 139

MS-PatchMatch		85.05 238	84.16 232	87.73 270	91.42 235	78.51 230	91.25 267	93.53 227	77.50 269	80.15 270	91.58 214	61.99 291	95.51 282	75.69 240	94.35 110	89.16 325

v16		84.96 241	83.74 240	88.62 244	91.40 236	79.48 188	93.83 179	94.04 211	83.03 187	76.54 296	86.59 305	76.11 129	95.42 288	80.33 187	71.80 321	90.95 306

tpm2		84.08 258	82.94 258	87.48 277	91.39 237	71.27 309	89.23 291	90.37 300	71.95 317	84.64 199	89.33 271	67.30 259	96.55 242	75.17 245	87.09 217	94.63 174

v1neww		87.98 148	87.25 144	90.16 180	91.38 238	79.41 194	94.37 139	95.28 160	84.48 146	85.77 154	91.53 218	76.12 126	97.45 162	84.45 125	81.89 263	93.61 237

v7new		87.98 148	87.25 144	90.16 180	91.38 238	79.41 194	94.37 139	95.28 160	84.48 146	85.77 154	91.53 218	76.12 126	97.45 162	84.45 125	81.89 263	93.61 237

v8		87.50 179	86.71 164	89.89 200	91.37 240	79.40 198	94.50 124	95.38 155	84.81 139	83.60 226	91.33 228	76.05 130	97.42 173	82.84 145	80.51 290	92.84 264

v18		84.97 240	83.76 238	88.60 246	91.36 241	79.41 194	93.82 181	94.04 211	83.00 190	76.61 295	86.60 304	76.19 124	95.43 287	80.39 184	71.79 322	90.96 304

v17		84.93 243	83.70 242	88.62 244	91.36 241	79.48 188	93.83 179	94.03 213	83.04 186	76.51 297	86.57 306	76.05 130	95.42 288	80.31 189	71.65 323	90.96 304

v6		87.98 148	87.25 144	90.16 180	91.36 241	79.39 199	94.37 139	95.27 163	84.48 146	85.78 153	91.51 220	76.15 125	97.46 160	84.46 124	81.88 265	93.62 236

ADS-MVSNet2		81.66 280	79.71 286	87.50 275	91.35 244	74.19 284	83.33 335	88.48 330	72.90 310	82.24 242	85.77 316	64.98 279	93.20 321	64.57 317	83.74 241	95.12 146

ADS-MVSNet		81.56 282	79.78 284	86.90 290	91.35 244	71.82 306	83.33 335	89.16 325	72.90 310	82.24 242	85.77 316	64.98 279	93.76 313	64.57 317	83.74 241	95.12 146

V9		84.77 248	83.50 249	88.58 247	91.33 246	79.46 190	93.75 186	94.00 217	83.07 182	76.07 304	86.43 307	75.97 135	95.37 291	79.91 196	70.93 329	90.91 308

GA-MVS		86.61 202	85.27 209	90.66 153	91.33 246	78.71 219	90.40 272	93.81 225	85.34 128	85.12 187	89.57 269	61.25 297	97.11 205	80.99 174	89.59 176	96.15 111

v12		84.74 249	83.46 250	88.58 247	91.32 248	79.50 185	93.75 186	94.01 214	83.06 183	75.98 306	86.41 311	75.82 141	95.36 294	79.87 197	70.89 330	90.89 310

v11		84.67 254	83.41 253	88.44 255	91.32 248	79.13 213	93.69 195	93.99 219	82.81 196	76.20 300	86.24 314	75.48 146	95.35 295	79.53 202	71.48 325	90.85 312

V14		84.79 246	83.52 248	88.57 250	91.32 248	79.43 193	93.72 190	94.01 214	83.06 183	76.22 299	86.43 307	76.01 134	95.37 291	79.96 193	70.99 327	90.91 308

v13		84.72 251	83.44 252	88.58 247	91.31 251	79.52 184	93.77 184	94.00 217	83.03 187	75.85 307	86.38 312	75.84 140	95.35 295	79.83 198	70.95 328	90.87 311

v15		84.79 246	83.53 247	88.57 250	91.30 252	79.41 194	93.70 192	94.01 214	83.06 183	76.27 298	86.42 310	76.03 133	95.38 290	80.01 191	71.00 326	90.92 307

v1141		87.84 154	87.09 148	90.11 191	91.23 253	79.25 208	94.08 163	95.24 165	84.44 150	85.69 161	91.31 231	75.91 138	97.44 169	84.17 130	81.74 270	93.63 235

divwei89l23v2f112		87.84 154	87.09 148	90.10 193	91.23 253	79.24 210	94.09 161	95.24 165	84.44 150	85.70 159	91.31 231	75.91 138	97.44 169	84.17 130	81.73 271	93.64 233

v1		87.85 153	87.10 147	90.11 191	91.21 255	79.24 210	94.09 161	95.24 165	84.44 150	85.70 159	91.31 231	75.96 136	97.45 162	84.18 129	81.73 271	93.64 233

v7		87.75 159	86.96 154	90.12 186	91.20 256	79.50 185	94.28 145	95.46 145	83.45 172	85.75 156	91.56 217	75.13 150	97.43 171	83.60 137	82.18 258	93.42 246

XVG-ACMP-BASELINE		86.00 214	84.84 219	89.45 220	91.20 256	78.00 243	91.70 258	95.55 136	85.05 135	82.97 234	92.25 190	54.49 324	97.48 158	82.93 143	87.45 212	92.89 262

v10		87.25 186	86.38 179	89.85 201	91.19 258	79.50 185	94.48 125	95.45 149	83.79 162	83.62 225	91.19 236	75.13 150	97.42 173	81.94 161	80.60 285	92.63 270

FMVSNet5		81.52 283	79.60 287	87.27 279	91.17 259	77.95 244	91.49 262	92.26 249	76.87 275	76.16 301	87.91 294	51.67 329	92.34 326	67.74 298	81.16 274	91.52 293

USDC		82.76 269	81.26 270	87.26 280	91.17 259	74.55 281	89.27 289	93.39 230	78.26 265	75.30 309	92.08 196	54.43 325	96.63 236	71.64 265	85.79 225	90.61 314

CostFormer		85.77 222	84.94 215	88.26 260	91.16 261	72.58 303	89.47 287	91.04 289	76.26 281	86.45 142	89.97 263	70.74 208	96.86 224	82.35 153	87.07 218	95.34 144

tpm cat1		81.96 275	80.27 278	87.01 287	91.09 262	71.02 313	87.38 311	91.53 274	66.25 335	80.17 269	86.35 313	68.22 258	96.15 259	69.16 287	82.29 256	93.86 215

tpmvs		83.35 267	82.07 264	87.20 285	91.07 263	71.00 314	88.31 303	91.70 265	78.91 253	80.49 267	87.18 302	69.30 229	97.08 207	68.12 297	83.56 245	93.51 244

v1144		87.61 175	86.79 161	90.06 194	91.01 264	79.34 202	93.95 175	95.42 154	83.36 176	85.66 163	91.31 231	74.98 154	97.42 173	83.37 138	82.06 259	93.42 246

v2v482		87.84 154	87.06 151	90.17 179	90.99 265	79.23 212	94.00 173	95.13 172	84.87 137	85.53 167	92.07 198	74.45 157	97.45 162	84.71 121	81.75 269	93.85 216

SixPastTwentyTwo		83.91 260	82.90 259	86.92 289	90.99 265	70.67 316	93.48 200	91.99 258	85.54 124	77.62 289	92.11 194	60.59 302	96.87 223	76.05 239	77.75 305	93.20 251

test-LLR		85.87 216	85.41 205	87.25 281	90.95 267	71.67 307	89.55 283	89.88 312	83.41 173	84.54 202	87.95 292	67.25 260	95.11 300	81.82 163	93.37 126	94.97 151

test-mter		84.54 255	83.64 245	87.25 281	90.95 267	71.67 307	89.55 283	89.88 312	79.17 251	84.54 202	87.95 292	55.56 319	95.11 300	81.82 163	93.37 126	94.97 151

v148		87.04 193	86.32 182	89.21 231	90.94 269	77.26 264	93.71 191	94.43 199	84.84 138	84.36 210	90.80 246	76.04 132	97.05 210	82.12 157	79.60 299	93.31 248

mvs_tets		88.06 147	87.28 142	90.38 173	90.94 269	79.88 178	95.22 76	95.66 129	85.10 134	84.21 215	93.94 130	63.53 285	97.40 180	88.50 78	88.40 202	93.87 213

MVP-Stereo		85.97 215	84.86 218	89.32 228	90.92 271	82.19 124	92.11 250	94.19 206	78.76 258	78.77 281	91.63 212	68.38 256	96.56 240	75.01 248	93.95 112	89.20 324
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Patchmatch-test		81.37 285	79.30 289	87.58 273	90.92 271	74.16 285	80.99 341	87.68 336	70.52 325	76.63 294	88.81 277	71.21 200	92.76 325	60.01 331	86.93 219	95.83 128

jajsoiax		88.24 140	87.50 135	90.48 166	90.89 273	80.14 170	95.31 63	95.65 131	84.97 136	84.24 214	94.02 125	65.31 277	97.42 173	88.56 77	88.52 197	93.89 210

tpmrst		85.35 232	84.99 211	86.43 295	90.88 274	67.88 327	88.71 297	91.43 277	80.13 243	86.08 150	88.80 278	73.05 179	96.02 263	82.48 150	83.40 249	95.40 141

gg-mvs-nofinetune		81.77 278	79.37 288	88.99 236	90.85 275	77.73 254	86.29 316	79.63 353	74.88 295	83.19 233	69.05 346	60.34 303	96.11 260	75.46 242	94.64 101	93.11 256

OurMVSNet-221017-0		85.35 232	84.64 224	87.49 276	90.77 276	72.59 302	94.01 172	94.40 200	84.72 142	79.62 277	93.17 153	61.91 292	96.72 232	81.99 160	81.16 274	93.16 253

v1192		87.25 186	86.33 181	90.00 198	90.76 277	79.04 214	93.80 182	95.48 144	82.57 201	85.48 171	91.18 237	73.38 177	97.42 173	82.30 154	82.06 259	93.53 241

test_djsdf		89.03 123	88.64 111	90.21 178	90.74 278	79.28 206	95.96 41	95.90 111	84.66 143	85.33 185	92.94 165	74.02 166	97.30 187	89.64 69	88.53 196	94.05 204

v7n		86.81 196	85.76 198	89.95 199	90.72 279	79.25 208	95.07 84	95.92 108	84.45 149	82.29 240	90.86 245	72.60 186	97.53 155	79.42 207	80.52 289	93.08 258

PVSNet_0	73.20 20	77.22 304	74.83 307	84.37 311	90.70 280	71.10 312	83.09 337	89.67 315	72.81 312	73.93 316	83.13 327	60.79 301	93.70 314	68.54 290	50.84 349	88.30 336

DI_MVS_plusplus_test		88.15 143	86.82 158	92.14 106	90.67 281	81.07 148	93.01 221	94.59 195	83.83 161	77.78 286	90.63 249	68.51 246	98.16 110	88.02 85	94.37 109	97.17 84

v144192		87.19 190	86.35 180	89.74 206	90.64 282	78.24 239	93.92 176	95.43 152	81.93 213	85.51 169	91.05 243	74.21 162	97.45 162	82.86 144	81.56 273	93.53 241

V42		87.68 161	86.86 156	90.15 184	90.58 283	80.14 170	94.24 147	95.28 160	83.66 164	85.67 162	91.33 228	74.73 155	97.41 178	84.43 127	81.83 267	92.89 262

CR-MVSNet		85.35 232	83.76 238	90.12 186	90.58 283	79.34 202	85.24 324	91.96 261	78.27 264	85.55 165	87.87 295	71.03 203	95.61 277	73.96 256	89.36 179	95.40 141

RPMNet		83.18 268	80.87 274	90.12 186	90.58 283	79.34 202	85.24 324	90.78 296	71.44 319	85.55 165	82.97 328	70.87 205	95.61 277	61.01 327	89.36 179	95.40 141

test_normal		88.13 144	86.78 162	92.18 104	90.55 286	81.19 146	92.74 229	94.64 194	83.84 159	77.49 290	90.51 255	68.49 247	98.16 110	88.22 80	94.55 103	97.21 82

v1921920		86.97 194	86.06 191	89.69 211	90.53 287	78.11 242	93.80 182	95.43 152	81.90 215	85.33 185	91.05 243	72.66 184	97.41 178	82.05 159	81.80 268	93.53 241

v1240		86.78 198	85.85 196	89.56 214	90.45 288	77.79 250	93.61 196	95.37 157	81.65 226	85.43 176	91.15 239	71.50 198	97.43 171	81.47 168	82.05 261	93.47 245

tpm		84.73 250	84.02 234	86.87 292	90.33 289	68.90 324	89.06 293	89.94 310	80.85 238	85.75 156	89.86 265	68.54 245	95.97 265	77.76 221	84.05 239	95.75 132

EG-PatchMatch MVS		82.37 274	80.34 277	88.46 254	90.27 290	79.35 201	92.80 228	94.33 203	77.14 274	73.26 320	90.18 260	47.47 338	96.72 232	70.25 273	87.32 215	89.30 322

v748		86.27 208	85.28 208	89.25 230	90.26 291	77.58 263	94.89 96	95.50 143	84.28 153	81.41 255	90.46 256	72.57 187	97.32 186	79.81 199	78.36 303	92.84 264

EPNet_dtu		86.49 206	85.94 195	88.14 264	90.24 292	72.82 296	94.11 159	92.20 250	86.66 105	79.42 278	92.36 183	73.52 172	95.81 273	71.26 267	93.66 117	95.80 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		83.90 261	82.70 262	87.51 274	90.23 293	72.67 299	88.62 299	81.96 349	81.37 234	85.01 189	88.34 286	66.31 269	94.45 306	75.30 244	87.12 216	95.43 140

EPNet		91.79 61	91.02 69	94.10 46	90.10 294	85.25 55	96.03 36	92.05 255	92.83 1	87.39 126	95.78 76	79.39 97	99.01 56	88.13 83	97.48 60	98.05 48
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchT		82.68 271	81.27 269	86.89 291	90.09 295	70.94 315	84.06 331	90.15 304	74.91 293	85.63 164	83.57 324	69.37 225	94.87 305	65.19 313	88.50 198	94.84 164

Patchmtry		82.71 270	80.93 273	88.06 265	90.05 296	76.37 272	84.74 326	91.96 261	72.28 315	81.32 257	87.87 295	71.03 203	95.50 284	68.97 288	80.15 292	92.32 280

pmmvs4		85.43 230	83.86 237	90.16 180	90.02 297	82.97 107	90.27 273	92.67 242	75.93 284	80.73 262	91.74 208	71.05 202	95.73 276	78.85 211	83.46 247	91.78 287

TESTMET0.1,1		83.74 263	82.85 260	86.42 296	89.96 298	71.21 311	89.55 283	87.88 333	77.41 270	83.37 231	87.31 300	56.71 316	93.65 315	80.62 180	92.85 137	94.40 191

dp		81.47 284	80.23 279	85.17 306	89.92 299	65.49 334	86.74 313	90.10 306	76.30 280	81.10 258	87.12 303	62.81 286	95.92 267	68.13 296	79.88 297	94.09 202

K. test v3		81.59 281	80.15 281	85.91 300	89.89 300	69.42 323	92.57 235	87.71 335	85.56 123	73.44 318	89.71 267	55.58 318	95.52 281	77.17 228	69.76 333	92.78 267

MDA-MVSNet-bldmvs		78.85 302	76.31 303	86.46 294	89.76 301	73.88 288	88.79 296	90.42 299	79.16 252	59.18 343	88.33 287	60.20 304	94.04 311	62.00 324	68.96 335	91.48 295

GG-mvs-BLEND				87.94 268	89.73 302	77.91 245	87.80 306	78.23 355		80.58 265	83.86 322	59.88 307	95.33 297	71.20 268	92.22 141	90.60 316

gm-plane-assit					89.60 303	68.00 326			77.28 273		88.99 274		97.57 152	79.44 205

v52		86.50 204	85.53 203	89.39 222	89.17 304	78.99 215	94.72 111	95.54 138	83.59 165	82.10 244	90.60 251	71.59 195	97.45 162	82.52 148	79.99 295	91.73 289

anonymousdsp		87.84 154	87.09 148	90.12 186	89.13 305	80.54 164	94.67 116	95.55 136	82.05 208	83.82 220	92.12 192	71.47 199	97.15 201	87.15 97	87.80 209	92.67 268

V4		86.50 204	85.54 200	89.39 222	89.13 305	78.99 215	94.73 108	95.54 138	83.59 165	82.10 244	90.61 250	71.60 194	97.45 162	82.52 148	80.01 294	91.74 288

N_pmnet		68.89 318	68.44 319	70.23 334	89.07 307	28.79 363	88.06 304	19.50 364	69.47 328	71.86 326	84.93 319	61.24 298	91.75 331	54.70 335	77.15 309	90.15 318

pmmvs5		84.21 257	82.84 261	88.34 258	88.95 308	76.94 267	92.41 239	91.91 263	75.63 286	80.28 268	91.18 237	64.59 281	95.57 279	77.09 230	83.47 246	92.53 272

PMMVS		85.71 228	84.96 214	87.95 267	88.90 309	77.09 265	88.68 298	90.06 307	72.32 314	86.47 139	90.76 247	72.15 191	94.40 307	81.78 165	93.49 121	92.36 278

JIA-IIPM		81.04 288	78.98 294	87.25 281	88.64 310	73.48 291	81.75 340	89.61 316	73.19 306	82.05 246	73.71 343	66.07 275	95.87 270	71.18 270	84.60 234	92.41 276

Gipuma		57.99 326	54.91 327	67.24 339	88.51 311	65.59 333	52.21 357	90.33 302	43.58 352	42.84 351	51.18 354	20.29 359	85.07 349	34.77 354	70.45 331	51.05 355
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EU-MVSNet		81.32 286	80.95 272	82.42 319	88.50 312	63.67 336	93.32 204	91.33 278	64.02 340	80.57 266	92.83 168	61.21 299	92.27 327	76.34 235	80.38 291	91.32 297

our_test_3		81.93 276	80.46 276	86.33 297	88.46 313	73.48 291	88.46 301	91.11 281	76.46 276	76.69 293	88.25 288	66.89 263	94.36 308	68.75 289	79.08 301	91.14 301

ppachtmachnet_test		81.84 277	80.07 282	87.15 286	88.46 313	74.43 282	89.04 294	92.16 251	75.33 288	77.75 287	88.99 274	66.20 271	95.37 291	65.12 315	77.60 306	91.65 291

lessismore_v0				86.04 298	88.46 313	68.78 325		80.59 351		73.01 321	90.11 261	55.39 320	96.43 249	75.06 247	65.06 339	92.90 261

test0.0.03 1		82.41 273	81.69 266	84.59 309	88.23 316	72.89 295	90.24 274	87.83 334	83.41 173	79.86 274	89.78 266	67.25 260	88.99 337	65.18 314	83.42 248	91.90 286

MDA-MVSNet_test_wron		79.21 301	77.19 301	85.29 304	88.22 317	72.77 298	85.87 319	90.06 307	74.34 298	62.62 342	87.56 298	66.14 273	91.99 329	66.90 303	73.01 316	91.10 303

YYNet1		79.22 300	77.20 300	85.28 305	88.20 318	72.66 300	85.87 319	90.05 309	74.33 299	62.70 341	87.61 297	66.09 274	92.03 328	66.94 300	72.97 317	91.15 300

Test4		85.75 223	83.72 241	91.83 119	88.08 319	81.03 150	92.48 237	95.54 138	83.38 175	73.40 319	88.57 282	50.99 331	97.37 184	86.61 107	94.47 106	97.09 88

pmmvs6		83.42 264	81.60 267	88.87 237	88.01 320	77.87 248	94.96 91	94.24 205	74.67 296	78.80 280	91.09 242	60.17 305	96.49 244	77.06 231	75.40 313	92.23 282

testgi		80.94 291	80.20 280	83.18 315	87.96 321	66.29 331	91.28 265	90.70 298	83.70 163	78.12 283	92.84 167	51.37 330	90.82 334	63.34 320	82.46 255	92.43 275

LP		75.51 308	72.15 312	85.61 302	87.86 322	73.93 287	80.20 343	88.43 331	67.39 331	70.05 328	80.56 336	58.18 313	93.18 322	46.28 347	70.36 332	89.71 321

Anonymous20231206		81.03 289	79.77 285	84.82 308	87.85 323	70.26 319	91.42 263	92.08 254	73.67 302	77.75 287	89.25 272	62.43 289	93.08 323	61.50 326	82.00 262	91.12 302

OpenMVS_ROB	74.94 19	79.51 298	77.03 302	86.93 288	87.00 324	76.23 274	92.33 242	90.74 297	68.93 329	74.52 313	88.23 289	49.58 333	96.62 237	57.64 333	84.29 236	87.94 337

LF4IMVS		80.37 293	79.07 293	84.27 313	86.64 325	69.87 322	89.39 288	91.05 288	76.38 278	74.97 311	90.00 262	47.85 337	94.25 310	74.55 252	80.82 284	88.69 330

MIMVSNet1		79.38 299	77.28 299	85.69 301	86.35 326	73.67 290	91.61 261	92.75 240	78.11 268	72.64 323	88.12 290	48.16 336	91.97 330	60.32 328	77.49 307	91.43 296

test20.03		79.95 295	79.08 292	82.55 318	85.79 327	67.74 328	91.09 270	91.08 286	81.23 235	74.48 314	89.96 264	61.63 293	90.15 335	60.08 329	76.38 310	89.76 319

Patchmatch-RL test		81.67 279	79.96 283	86.81 293	85.42 328	71.23 310	82.17 339	87.50 338	78.47 261	77.19 292	82.50 329	70.81 207	93.48 317	82.66 147	72.89 318	95.71 133

UnsupCasMVSNet_eth		80.07 294	78.27 296	85.46 303	85.24 329	72.63 301	88.45 302	94.87 186	82.99 191	71.64 327	88.07 291	56.34 317	91.75 331	73.48 259	63.36 344	92.01 285

testing_2		83.40 266	81.02 271	90.56 156	85.06 330	80.51 165	91.37 264	95.57 134	82.92 193	67.06 335	85.54 318	49.47 334	97.24 195	86.74 102	85.44 226	93.93 208

pmmvs-eth3d		80.97 290	78.72 295	87.74 269	84.99 331	79.97 177	90.11 277	91.65 267	75.36 287	73.51 317	86.03 315	59.45 308	93.96 312	75.17 245	72.21 319	89.29 323

testpf		71.41 316	72.11 313	69.30 336	84.53 332	59.79 340	62.74 354	83.14 346	71.11 322	68.83 332	81.57 334	46.70 339	84.83 350	74.51 253	75.86 312	63.30 350

CMPMVS	59.16 21	80.52 292	79.20 290	84.48 310	83.98 333	67.63 329	89.95 280	93.84 224	64.79 339	66.81 336	91.14 240	57.93 314	95.17 298	76.25 236	88.10 204	90.65 313
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		76.23 307	73.27 309	85.09 307	83.79 334	72.92 294	85.65 323	93.47 229	71.52 318	68.84 331	79.08 339	49.77 332	93.21 320	66.81 304	60.52 346	89.13 327

PM-MVS		78.11 303	76.12 305	84.09 314	83.54 335	70.08 320	88.97 295	85.27 343	79.93 245	74.73 312	86.43 307	34.70 349	93.48 317	79.43 206	72.06 320	88.72 329

DSMNet-mixed		76.94 305	76.29 304	78.89 322	83.10 336	56.11 348	87.78 307	79.77 352	60.65 344	75.64 308	88.71 279	61.56 294	88.34 339	60.07 330	89.29 181	92.21 283

Anonymous20231211		72.97 312	69.63 317	83.00 317	83.05 337	66.91 330	92.69 230	89.45 318	61.06 343	67.50 334	83.46 325	34.34 350	93.61 316	51.11 338	63.97 342	88.48 334

new_pmnet		72.15 314	70.13 315	78.20 323	82.95 338	65.68 332	83.91 332	82.40 348	62.94 342	64.47 339	79.82 338	42.85 343	86.26 345	57.41 334	74.44 315	82.65 343

new-patchmatchnet		76.41 306	75.17 306	80.13 321	82.65 339	59.61 341	87.66 309	91.08 286	78.23 266	69.85 329	83.22 326	54.76 322	91.63 333	64.14 319	64.89 340	89.16 325

testus		74.41 310	73.35 308	77.59 327	82.49 340	57.08 344	86.02 317	90.21 303	72.28 315	72.89 322	84.32 321	37.08 347	86.96 343	52.24 337	82.65 253	88.73 328

test2356		74.50 309	73.27 309	78.20 323	80.81 341	59.84 339	83.76 334	88.33 332	71.43 320	72.37 324	81.84 332	45.60 341	86.26 345	50.97 339	84.32 235	88.50 332

ambc				83.06 316	79.99 342	63.51 337	77.47 347	92.86 236		74.34 315	84.45 320	28.74 351	95.06 302	73.06 261	68.89 336	90.61 314

1111		70.54 317	69.71 316	73.04 331	79.30 343	44.83 356	84.23 329	88.96 327	67.33 332	65.42 337	82.28 330	41.11 345	88.11 340	47.12 345	71.60 324	86.19 339

.test1245		57.63 327	61.79 324	45.14 345	79.30 343	44.83 356	84.23 329	88.96 327	67.33 332	65.42 337	82.28 330	41.11 345	88.11 340	47.12 345	0.39 360	2.46 361

TDRefinement		79.81 296	77.34 298	87.22 284	79.24 345	75.48 279	93.12 215	92.03 256	76.45 277	75.01 310	91.58 214	49.19 335	96.44 248	70.22 275	69.18 334	89.75 320

test1235678		72.22 313	70.31 314	77.93 326	78.04 346	58.04 343	85.76 321	89.80 314	70.15 327	63.43 340	80.20 337	42.24 344	87.24 342	48.68 343	74.50 314	88.50 332

pmmvs3		71.81 315	68.71 318	81.11 320	75.86 347	70.42 318	86.74 313	83.66 345	58.95 345	68.64 333	80.89 335	36.93 348	89.52 336	63.10 322	63.59 343	83.39 341

DeepMVS_CX				56.31 343	74.23 348	51.81 352		56.67 362	44.85 350	48.54 349	75.16 341	27.87 353	58.74 360	40.92 351	52.22 348	58.39 354

test12356		64.99 321	63.78 320	68.61 338	72.69 349	39.14 359	78.46 345	87.61 337	64.91 338	55.77 344	77.48 340	28.10 352	85.59 347	44.69 348	64.35 341	81.12 345

FPMVS		64.63 322	62.55 322	70.88 333	70.80 350	56.71 345	84.42 328	84.42 344	51.78 348	49.57 347	81.61 333	23.49 356	81.48 352	40.61 352	76.25 311	74.46 349

PMMVS2		59.60 324	56.40 326	69.21 337	68.83 351	46.58 354	73.02 352	77.48 356	55.07 347	49.21 348	72.95 345	17.43 361	80.04 353	49.32 342	44.33 350	80.99 346

testmv		65.49 320	62.66 321	73.96 330	68.78 352	53.14 351	84.70 327	88.56 329	65.94 337	52.35 346	74.65 342	25.02 355	85.14 348	43.54 349	60.40 347	83.60 340

PNet_i23d		50.48 330	47.18 330	60.36 341	68.59 353	44.56 358	72.75 353	72.61 357	43.92 351	33.91 354	60.19 352	6.16 363	73.52 356	38.50 353	28.04 353	63.01 351

wuyk23d		21.27 337	20.48 338	23.63 349	68.59 353	36.41 361	49.57 358	6.85 365	9.37 358	7.89 360	4.46 363	4.03 366	31.37 361	17.47 359	16.07 359	3.12 359

no-one		61.56 323	56.58 325	76.49 329	67.80 355	62.76 338	78.13 346	86.11 339	63.16 341	43.24 350	64.70 349	26.12 354	88.95 338	50.84 340	29.15 352	77.77 347

E-PMN		43.23 332	42.29 332	46.03 344	65.58 356	37.41 360	73.51 349	64.62 358	33.99 355	28.47 357	47.87 355	19.90 360	67.91 357	22.23 357	24.45 355	32.77 356

LCM-MVSNet		66.00 319	62.16 323	77.51 328	64.51 357	58.29 342	83.87 333	90.90 292	48.17 349	54.69 345	73.31 344	16.83 362	86.75 344	65.47 312	61.67 345	87.48 338

EMVS		42.07 333	41.12 333	44.92 346	63.45 358	35.56 362	73.65 348	63.48 359	33.05 356	26.88 358	45.45 357	21.27 358	67.14 358	19.80 358	23.02 357	32.06 357

wuykxyi23d		50.55 329	44.13 331	69.81 335	56.77 359	54.58 350	73.22 351	80.78 350	39.79 354	22.08 359	46.69 356	4.03 366	79.71 354	47.65 344	26.13 354	75.14 348

MVE	39.65 23	43.39 331	38.59 336	57.77 342	56.52 360	48.77 353	55.38 356	58.64 361	29.33 357	28.96 356	52.65 353	4.68 365	64.62 359	28.11 356	33.07 351	59.93 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		58.88 325	54.22 328	72.86 332	56.50 361	56.67 346	80.75 342	86.00 340	73.09 308	37.39 352	64.63 350	22.17 357	79.49 355	43.51 350	23.96 356	82.43 344

PMVS	47.18 22	52.22 328	48.46 329	63.48 340	45.72 362	46.20 355	73.41 350	78.31 354	41.03 353	30.06 355	65.68 348	6.05 364	83.43 351	30.04 355	65.86 338	60.80 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		35.64 335	39.24 334	24.84 348	14.87 363	23.90 364	62.71 355	51.51 363	6.58 359	36.66 353	62.08 351	44.37 342	30.34 362	52.40 336	22.00 358	20.27 358

testmvs		8.92 338	11.52 339	1.12 351	1.06 364	0.46 366	86.02 317	0.65 366	0.62 360	2.74 361	9.52 361	0.31 369	0.45 364	2.38 360	0.39 360	2.46 361

test123		8.76 339	11.22 340	1.39 350	0.85 365	0.97 365	85.76 321	0.35 367	0.54 361	2.45 362	8.14 362	0.60 368	0.48 363	2.16 361	0.17 362	2.71 360

cdsmvs_eth3d_5k		22.14 336	29.52 337	0.00 352	0.00 366	0.00 367	0.00 359	95.76 122	0.00 362	0.00 363	94.29 116	75.66 144	0.00 365	0.00 362	0.00 363	0.00 363

pcd_1.5k_mvsjas		6.64 341	8.86 342	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	79.70 92	0.00 365	0.00 362	0.00 363	0.00 363

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

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

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

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

ab-mvs-re		7.82 340	10.43 341	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	93.88 135	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

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

GSMVS																96.12 114

test_part3							95.99 37		88.25 66		97.60 5		99.62 1	93.18 19

test_part1								97.45 6				91.93 1			99.02 3	98.67 5

sam_mvs1												71.70 193				96.12 114

sam_mvs												70.60 209

MTGPA								96.97 35

test_post1							88.00 305				9.81 360	69.31 228	95.53 280	76.65 232

test_post											10.29 359	70.57 213	95.91 269

patchmatchnet-post											83.76 323	71.53 197	96.48 245

MTMP							96.16 31	60.64 360

test9_res														91.91 43	98.71 20	98.07 46

agg_prior2														90.54 63	98.68 25	98.27 32

test_prior4						85.96 44	94.11 159

test_prior2							94.12 157		87.67 81	92.63 46	96.39 53	86.62 26		91.50 50	98.67 27

旧先验2							93.36 203		71.25 321	94.37 14			97.13 204	86.74 102

新几何2							93.11 217

无先验							93.28 210	96.26 83	73.95 301				99.05 47	80.56 181		96.59 102

原ACMM2							92.94 225

testdata2													98.75 80	78.30 216

segment_acmp												87.16 22

testdata1							92.15 248		87.94 72

plane_prior5								96.22 87					98.12 113	88.15 81	89.99 167	94.63 174

plane_prior4											94.86 100

plane_prior3						82.75 111			90.26 25	86.91 133

plane_prior2							95.85 45		90.81 18

plane_prior						82.73 114	95.21 77		89.66 35						89.88 171

n2								0.00 368

nn								0.00 368

door-mid								85.49 341

test11								96.57 71

door								85.33 342

HQP5-MVS						81.56 132

BP-MVS														87.11 99

HQP4-MVS										85.43 176			97.96 134			94.51 184

HQP3-MVS								96.04 101							89.77 173

HQP2-MVS												73.83 169

MDTV_nov1_ep13_2view						55.91 349	87.62 310		73.32 305	84.59 201		70.33 216		74.65 250		95.50 137

ACMMP++_ref															87.47 210

ACMMP++															88.01 207

Test By Simon												80.02 87

High-resolution multi-view benchmark