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.

FOURS1					99.91 1	99.93 1	99.87 7	99.56 70	99.10 27	99.81 39

TSAR-MVS + MP.		99.58 9	99.50 13	99.81 44	99.91 1	99.66 53	99.63 87	99.39 226	98.91 58	99.78 50	99.85 54	99.36 2	99.94 69	98.84 118	99.88 54	99.82 54
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS_fast		99.51 18	99.40 27	99.85 28	99.91 1	99.79 30	99.76 36	99.56 70	97.72 192	99.76 59	99.75 139	99.13 12	99.92 98	99.07 85	99.92 27	99.85 35

MP-MVS-pluss		99.37 55	99.20 69	99.88 5	99.90 4	99.87 12	99.30 251	99.52 102	97.18 251	99.60 111	99.79 117	98.79 47	99.95 59	98.83 121	99.91 34	99.83 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MTAPA		99.52 17	99.39 29	99.89 4	99.90 4	99.86 13	99.66 73	99.47 178	98.79 70	99.68 77	99.81 92	98.43 83	99.97 21	98.88 105	99.90 42	99.83 49

HPM-MVS		99.42 44	99.28 57	99.83 40	99.90 4	99.72 42	99.81 19	99.54 86	97.59 206	99.68 77	99.63 199	98.91 34	99.94 69	98.58 156	99.91 34	99.84 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HyFIR lowres test		99.11 102	98.92 111	99.65 74	99.90 4	99.37 102	99.02 325	99.91 3	97.67 200	99.59 114	99.75 139	95.90 178	99.73 215	99.53 35	99.02 187	99.86 32

MSP-MVS		99.42 44	99.27 60	99.88 5	99.89 8	99.80 27	99.67 68	99.50 136	98.70 79	99.77 54	99.49 250	98.21 95	99.95 59	98.46 173	99.77 110	99.88 25
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CHOSEN 1792x2688		99.19 79	99.10 78	99.45 127	99.89 8	98.52 211	99.39 223	99.94 1	98.73 77	99.11 221	99.89 31	95.50 191	99.94 69	99.50 39	99.97 7	99.89 19

ACMMP		99.45 35	99.32 43	99.82 41	99.89 8	99.67 51	99.62 92	99.69 18	98.12 141	99.63 101	99.84 64	98.73 60	99.96 31	98.55 165	99.83 89	99.81 61
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
region2R		99.48 26	99.35 37	99.87 11	99.88 11	99.80 27	99.65 79	99.66 28	98.13 140	99.66 86	99.68 175	98.96 24	99.96 31	98.62 147	99.87 57	99.84 39

MP-MVS		99.33 59	99.15 73	99.87 11	99.88 11	99.82 22	99.66 73	99.46 187	98.09 146	99.48 135	99.74 144	98.29 92	99.96 31	97.93 217	99.87 57	99.82 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		99.44 39	99.30 51	99.86 21	99.88 11	99.79 30	99.69 59	99.48 158	98.12 141	99.50 131	99.75 139	98.78 48	99.97 21	98.57 159	99.89 51	99.83 49

COLMAP_ROB	97.56 6	98.86 134	98.75 134	99.17 173	99.88 11	98.53 207	99.34 243	99.59 58	97.55 212	98.70 288	99.89 31	95.83 180	99.90 121	98.10 201	99.90 42	99.08 238
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ZNCC-MVS		99.47 29	99.33 41	99.87 11	99.87 15	99.81 25	99.64 82	99.67 23	98.08 150	99.55 123	99.64 193	98.91 34	99.96 31	98.72 133	99.90 42	99.82 54

ACMMP_NAP		99.47 29	99.34 39	99.88 5	99.87 15	99.86 13	99.47 187	99.48 158	98.05 157	99.76 59	99.86 49	98.82 43	99.93 87	98.82 125	99.91 34	99.84 39

HFP-MVS		99.49 22	99.37 33	99.86 21	99.87 15	99.80 27	99.66 73	99.67 23	98.15 136	99.68 77	99.69 169	99.06 16	99.96 31	98.69 138	99.87 57	99.84 39

ACMMPR		99.49 22	99.36 35	99.86 21	99.87 15	99.79 30	99.66 73	99.67 23	98.15 136	99.67 81	99.69 169	98.95 27	99.96 31	98.69 138	99.87 57	99.84 39

PGM-MVS		99.45 35	99.31 49	99.86 21	99.87 15	99.78 36	99.58 113	99.65 33	97.84 178	99.71 71	99.80 105	99.12 13	99.97 21	98.33 185	99.87 57	99.83 49

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 28	99.86 20	99.61 67	99.56 126	99.63 39	99.48 3	99.98 6	99.83 69	98.75 55	99.99 4	99.97 1	99.96 12	99.94 11

test_vis1_n_1920		98.63 163	98.40 170	99.31 149	99.86 20	97.94 249	99.67 68	99.62 41	99.43 7	99.99 2	99.91 20	87.29 372	100.00 1	99.92 11	99.92 27	99.98 2

GST-MVS		99.40 51	99.24 65	99.85 28	99.86 20	99.79 30	99.60 99	99.67 23	97.97 163	99.63 101	99.68 175	98.52 77	99.95 59	98.38 178	99.86 65	99.81 61

AllTest		98.87 131	98.72 136	99.31 149	99.86 20	98.48 217	99.56 126	99.61 48	97.85 176	99.36 167	99.85 54	95.95 173	99.85 151	96.66 313	99.83 89	99.59 151

TestCases				99.31 149	99.86 20	98.48 217		99.61 48	97.85 176	99.36 167	99.85 54	95.95 173	99.85 151	96.66 313	99.83 89	99.59 151

PVSNet_Blended_VisFu		99.36 56	99.28 57	99.61 86	99.86 20	99.07 144	99.47 187	99.93 2	97.66 201	99.71 71	99.86 49	97.73 112	99.96 31	99.47 46	99.82 93	99.79 74

DVP-MVS		99.57 12	99.47 17	99.88 5	99.85 26	99.89 4	99.57 120	99.37 242	99.10 27	99.81 39	99.80 105	98.94 29	99.96 31	98.93 99	99.86 65	99.81 61
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					99.85 26	99.89 4	99.62 92	99.50 136	99.10 27	99.86 30	99.82 78	98.94 29

XVS		99.53 16	99.42 22	99.87 11	99.85 26	99.83 16	99.69 59	99.68 20	98.98 48	99.37 164	99.74 144	98.81 44	99.94 69	98.79 126	99.86 65	99.84 39

X-MVStestdata		96.55 324	95.45 343	99.87 11	99.85 26	99.83 16	99.69 59	99.68 20	98.98 48	99.37 164	64.01 417	98.81 44	99.94 69	98.79 126	99.86 65	99.84 39

114514_t		98.93 126	98.67 142	99.72 66	99.85 26	99.53 83	99.62 92	99.59 58	92.65 386	99.71 71	99.78 124	98.06 103	99.90 121	98.84 118	99.91 34	99.74 92

CSCG		99.32 61	99.32 43	99.32 148	99.85 26	98.29 226	99.71 54	99.66 28	98.11 143	99.41 153	99.80 105	98.37 89	99.96 31	98.99 91	99.96 12	99.72 103

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 28	99.84 32	99.63 64	99.56 126	99.63 39	99.47 4	99.98 6	99.82 78	98.75 55	99.99 4	99.97 1	99.97 7	99.94 11

fmvsm_s_conf0.5_n		99.51 18	99.40 27	99.85 28	99.84 32	99.65 57	99.51 159	99.67 23	99.13 22	99.98 6	99.92 14	96.60 149	99.96 31	99.95 7	99.96 12	99.95 9

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 52	99.84 32	99.44 96	99.58 113	99.69 18	99.43 7	99.98 6	99.91 20	98.62 70	100.00 1	99.97 1	99.95 17	99.90 16

SED-MVS		99.61 7	99.52 11	99.88 5	99.84 32	99.90 2	99.60 99	99.48 158	99.08 33	99.91 17	99.81 92	99.20 7	99.96 31	98.91 102	99.85 72	99.79 74

IU-MVS					99.84 32	99.88 8		99.32 272	98.30 116	99.84 32				98.86 113	99.85 72	99.89 19

test_241102_ONE					99.84 32	99.90 2		99.48 158	99.07 35	99.91 17	99.74 144	99.20 7	99.76 204

test_0728_SECOND				99.91 2	99.84 32	99.89 4	99.57 120	99.51 116					99.96 31	98.93 99	99.86 65	99.88 25

fmvsm_s_conf0.5_n_a		99.56 13	99.47 17	99.85 28	99.83 39	99.64 63	99.52 152	99.65 33	99.10 27	99.98 6	99.92 14	97.35 122	99.96 31	99.94 9	99.92 27	99.95 9

dcpmvs_2		99.23 77	99.58 7	98.16 301	99.83 39	94.68 365	99.76 36	99.52 102	99.07 35	99.98 6	99.88 36	98.56 74	99.93 87	99.67 22	99.98 4	99.87 30

CP-MVS		99.45 35	99.32 43	99.85 28	99.83 39	99.75 39	99.69 59	99.52 102	98.07 151	99.53 126	99.63 199	98.93 33	99.97 21	98.74 130	99.91 34	99.83 49

test_fmvs1_n		98.41 174	98.14 186	99.21 169	99.82 42	97.71 262	99.74 45	99.49 146	99.32 14	99.99 2	99.95 3	85.32 382	99.97 21	99.82 16	99.84 80	99.96 7

SteuartSystems-ACMMP		99.54 15	99.42 22	99.87 11	99.82 42	99.81 25	99.59 105	99.51 116	98.62 85	99.79 45	99.83 69	99.28 4	99.97 21	98.48 169	99.90 42	99.84 39
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RPSCF		98.22 189	98.62 152	96.99 353	99.82 42	91.58 392	99.72 51	99.44 206	96.61 298	99.66 86	99.89 31	95.92 176	99.82 178	97.46 266	99.10 179	99.57 158

DeepC-MVS	98.35 2	99.30 63	99.19 70	99.64 79	99.82 42	99.23 122	99.62 92	99.55 78	98.94 54	99.63 101	99.95 3	95.82 181	99.94 69	99.37 52	99.97 7	99.73 97
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SDMVSNet		99.11 102	98.90 114	99.75 58	99.81 46	99.59 70	99.81 19	99.65 33	98.78 73	99.64 98	99.88 36	94.56 234	99.93 87	99.67 22	98.26 232	99.72 103

sd_testset		98.75 152	98.57 159	99.29 157	99.81 46	98.26 228	99.56 126	99.62 41	98.78 73	99.64 98	99.88 36	92.02 308	99.88 137	99.54 33	98.26 232	99.72 103

test_cas_vis1_n_1920		99.16 85	99.01 97	99.61 86	99.81 46	98.86 176	99.65 79	99.64 36	99.39 10	99.97 13	99.94 6	93.20 276	99.98 13	99.55 32	99.91 34	99.99 1

patch_mono-2		99.26 71	99.62 5	98.16 301	99.81 46	94.59 367	99.52 152	99.64 36	99.33 13	99.73 65	99.90 27	99.00 22	99.99 4	99.69 20	99.98 4	99.89 19

test_one_0601					99.81 46	99.88 8		99.49 146	98.97 51	99.65 93	99.81 92	99.09 14

test_part2					99.81 46	99.83 16				99.77 54

test_fmvsmconf_n		99.70 3	99.64 4	99.87 11	99.80 52	99.66 53	99.48 181	99.64 36	99.45 5	99.92 16	99.92 14	98.62 70	99.99 4	99.96 6	99.99 1	99.96 7

CPTT-MVS		99.11 102	98.90 114	99.74 61	99.80 52	99.46 94	99.59 105	99.49 146	97.03 269	99.63 101	99.69 169	97.27 126	99.96 31	97.82 228	99.84 80	99.81 61

SF-MVS		99.38 54	99.24 65	99.79 49	99.79 54	99.68 48	99.57 120	99.54 86	97.82 183	99.71 71	99.80 105	98.95 27	99.93 87	98.19 195	99.84 80	99.74 92

MCST-MVS		99.43 42	99.30 51	99.82 41	99.79 54	99.74 41	99.29 256	99.40 223	98.79 70	99.52 128	99.62 204	98.91 34	99.90 121	98.64 144	99.75 115	99.82 54

DPE-MVS		99.46 31	99.32 43	99.91 2	99.78 56	99.88 8	99.36 235	99.51 116	98.73 77	99.88 21	99.84 64	98.72 61	99.96 31	98.16 199	99.87 57	99.88 25
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS-test		99.49 22	99.48 15	99.54 99	99.78 56	99.30 113	99.89 2	99.58 62	98.56 90	99.73 65	99.69 169	98.55 75	99.82 178	99.69 20	99.85 72	99.48 183

EI-MVSNet-UG-set		99.58 9	99.57 8	99.64 79	99.78 56	99.14 134	99.60 99	99.45 198	99.01 40	99.90 19	99.83 69	98.98 23	99.93 87	99.59 27	99.95 17	99.86 32

EI-MVSNet-Vis-set		99.58 9	99.56 10	99.64 79	99.78 56	99.15 133	99.61 98	99.45 198	99.01 40	99.89 20	99.82 78	99.01 18	99.92 98	99.56 31	99.95 17	99.85 35

Vis-MVSNet		99.12 98	98.97 103	99.56 96	99.78 56	99.10 138	99.68 65	99.66 28	98.49 96	99.86 30	99.87 45	94.77 221	99.84 158	99.19 72	99.41 152	99.74 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
F-COLMAP		99.19 79	99.04 87	99.64 79	99.78 56	99.27 117	99.42 208	99.54 86	97.29 242	99.41 153	99.59 213	98.42 85	99.93 87	98.19 195	99.69 126	99.73 97

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 62	99.89 4	99.75 41	99.56 70	99.02 38	99.88 21	99.85 54	99.18 10	99.96 31	99.22 70	99.92 27	99.90 16
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MVS_111021_LR		99.41 48	99.33 41	99.65 74	99.77 62	99.51 87	98.94 345	99.85 6	98.82 65	99.65 93	99.74 144	98.51 78	99.80 190	98.83 121	99.89 51	99.64 136

DP-MVS		99.16 85	98.95 109	99.78 52	99.77 62	99.53 83	99.41 211	99.50 136	97.03 269	99.04 237	99.88 36	97.39 118	99.92 98	98.66 142	99.90 42	99.87 30

SR-MVS-dyc-post		99.45 35	99.31 49	99.85 28	99.76 65	99.82 22	99.63 87	99.52 102	98.38 106	99.76 59	99.82 78	98.53 76	99.95 59	98.61 150	99.81 96	99.77 82

RE-MVS-def			99.34 39		99.76 65	99.82 22	99.63 87	99.52 102	98.38 106	99.76 59	99.82 78	98.75 55		98.61 150	99.81 96	99.77 82

save fliter					99.76 65	99.59 70	99.14 298	99.40 223	99.00 43

CS-MVS		99.50 20	99.48 15	99.54 99	99.76 65	99.42 98	99.90 1	99.55 78	98.56 90	99.78 50	99.70 159	98.65 68	99.79 193	99.65 24	99.78 107	99.41 204

APD-MVS_3200maxsize		99.48 26	99.35 37	99.85 28	99.76 65	99.83 16	99.63 87	99.54 86	98.36 110	99.79 45	99.82 78	98.86 38	99.95 59	98.62 147	99.81 96	99.78 80

PVSNet_BlendedMVS		98.86 134	98.80 128	99.03 188	99.76 65	98.79 185	99.28 261	99.91 3	97.42 231	99.67 81	99.37 286	97.53 115	99.88 137	98.98 92	97.29 290	98.42 349

PVSNet_Blended		99.08 108	98.97 103	99.42 132	99.76 65	98.79 185	98.78 361	99.91 3	96.74 286	99.67 81	99.49 250	97.53 115	99.88 137	98.98 92	99.85 72	99.60 147

MSDG		98.98 122	98.80 128	99.53 107	99.76 65	99.19 124	98.75 364	99.55 78	97.25 245	99.47 136	99.77 132	97.82 109	99.87 142	96.93 300	99.90 42	99.54 163

SR-MVS		99.43 42	99.29 55	99.86 21	99.75 73	99.83 16	99.59 105	99.62 41	98.21 129	99.73 65	99.79 117	98.68 64	99.96 31	98.44 175	99.77 110	99.79 74

HPM-MVS++		99.39 53	99.23 67	99.87 11	99.75 73	99.84 15	99.43 201	99.51 116	98.68 82	99.27 188	99.53 237	98.64 69	99.96 31	98.44 175	99.80 100	99.79 74

新几何1				99.75 58	99.75 73	99.59 70		99.54 86	96.76 285	99.29 182	99.64 193	98.43 83	99.94 69	96.92 302	99.66 131	99.72 103

test222					99.75 73	99.49 89	98.91 349	99.49 146	96.42 315	99.34 173	99.65 187	98.28 93			99.69 126	99.72 103

testdata				99.54 99	99.75 73	98.95 163		99.51 116	97.07 263	99.43 146	99.70 159	98.87 37	99.94 69	97.76 235	99.64 134	99.72 103

CDPH-MVS		99.13 92	98.91 113	99.80 46	99.75 73	99.71 44	99.15 296	99.41 217	96.60 301	99.60 111	99.55 228	98.83 42	99.90 121	97.48 263	99.83 89	99.78 80

APD-MVS		99.27 69	99.08 82	99.84 39	99.75 73	99.79 30	99.50 166	99.50 136	97.16 253	99.77 54	99.82 78	98.78 48	99.94 69	97.56 256	99.86 65	99.80 70
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test2506		96.81 320	96.65 316	97.29 347	99.74 80	92.21 390	99.60 99	85.06 421	99.13 22	99.77 54	99.93 9	87.82 370	99.85 151	99.38 51	99.38 153	99.80 70

test1111		98.04 214	98.11 190	97.83 327	99.74 80	93.82 375	99.58 113	95.40 408	99.12 25	99.65 93	99.93 9	90.73 333	99.84 158	99.43 49	99.38 153	99.82 54

ECVR-MVS		98.04 214	98.05 199	98.00 314	99.74 80	94.37 370	99.59 105	94.98 409	99.13 22	99.66 86	99.93 9	90.67 334	99.84 158	99.40 50	99.38 153	99.80 70

旧先验1					99.74 80	99.59 70		99.54 86			99.69 169	98.47 80			99.68 129	99.73 97

SD-MVS		99.41 48	99.52 11	99.05 186	99.74 80	99.68 48	99.46 190	99.52 102	99.11 26	99.88 21	99.91 20	99.43 1	97.70 393	98.72 133	99.93 25	99.77 82
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
DP-MVS Recon		99.12 98	98.95 109	99.65 74	99.74 80	99.70 46	99.27 266	99.57 65	96.40 317	99.42 149	99.68 175	98.75 55	99.80 190	97.98 214	99.72 121	99.44 199

PAPM_NR		99.04 113	98.84 125	99.66 70	99.74 80	99.44 96	99.39 223	99.38 234	97.70 196	99.28 183	99.28 310	98.34 90	99.85 151	96.96 297	99.45 149	99.69 115

SMA-MVS		99.44 39	99.30 51	99.85 28	99.73 87	99.83 16	99.56 126	99.47 178	97.45 225	99.78 50	99.82 78	99.18 10	99.91 109	98.79 126	99.89 51	99.81 61
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
原ACMM1				99.65 74	99.73 87	99.33 106		99.47 178	97.46 222	99.12 219	99.66 186	98.67 66	99.91 109	97.70 244	99.69 126	99.71 112

IS-MVSNet		99.05 112	98.87 119	99.57 94	99.73 87	99.32 107	99.75 41	99.20 300	98.02 161	99.56 119	99.86 49	96.54 152	99.67 239	98.09 202	99.13 175	99.73 97

PVSNet	96.02 17	98.85 141	98.84 125	98.89 214	99.73 87	97.28 275	98.32 392	99.60 54	97.86 173	99.50 131	99.57 222	96.75 144	99.86 145	98.56 162	99.70 125	99.54 163

9.14			99.10 78		99.72 91		99.40 219	99.51 116	97.53 216	99.64 98	99.78 124	98.84 41	99.91 109	97.63 247	99.82 93

thres100view900		97.76 260	97.45 267	98.69 244	99.72 91	97.86 253	99.59 105	98.74 362	97.93 166	99.26 192	98.62 367	91.75 314	99.83 171	93.22 371	98.18 239	98.37 355

thres600view7		97.86 242	97.51 258	98.92 205	99.72 91	97.95 247	99.59 105	98.74 362	97.94 165	99.27 188	98.62 367	91.75 314	99.86 145	93.73 366	98.19 238	98.96 255

DELS-MVS		99.48 26	99.42 22	99.65 74	99.72 91	99.40 101	99.05 317	99.66 28	99.14 21	99.57 118	99.80 105	98.46 81	99.94 69	99.57 30	99.84 80	99.60 147
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
MVS_111021_HR		99.41 48	99.32 43	99.66 70	99.72 91	99.47 93	98.95 343	99.85 6	98.82 65	99.54 124	99.73 150	98.51 78	99.74 209	98.91 102	99.88 54	99.77 82

ZD-MVS					99.71 96	99.79 30		99.61 48	96.84 282	99.56 119	99.54 233	98.58 72	99.96 31	96.93 300	99.75 115

Anonymous20231211		97.88 238	97.54 255	98.90 211	99.71 96	98.53 207	99.48 181	99.57 65	94.16 370	98.81 271	99.68 175	93.23 273	99.42 280	98.84 118	94.42 354	98.76 269

XVG-OURS-SEG-HR		98.69 157	98.62 152	98.89 214	99.71 96	97.74 256	99.12 302	99.54 86	98.44 102	99.42 149	99.71 155	94.20 248	99.92 98	98.54 166	98.90 195	99.00 249

Vis-MVSNet (Re-imp)		98.87 131	98.72 136	99.31 149	99.71 96	98.88 172	99.80 24	99.44 206	97.91 168	99.36 167	99.78 124	95.49 192	99.43 279	97.91 218	99.11 176	99.62 142

PatchMatch-RL		98.84 144	98.62 152	99.52 113	99.71 96	99.28 115	99.06 315	99.77 9	97.74 191	99.50 131	99.53 237	95.41 193	99.84 158	97.17 287	99.64 134	99.44 199

fmvsm_s_conf0.1_n		99.29 65	99.10 78	99.86 21	99.70 101	99.65 57	99.53 151	99.62 41	98.74 76	99.99 2	99.95 3	94.53 238	99.94 69	99.89 12	99.96 12	99.97 4

h-mvs33		97.70 274	97.28 295	98.97 196	99.70 101	97.27 276	99.36 235	99.45 198	98.94 54	99.66 86	99.64 193	94.93 208	99.99 4	99.48 44	84.36 400	99.65 129

XVG-OURS		98.73 155	98.68 141	98.88 216	99.70 101	97.73 257	98.92 347	99.55 78	98.52 94	99.45 139	99.84 64	95.27 199	99.91 109	98.08 206	98.84 199	99.00 249

TAPA-MVS	97.07 15	97.74 266	97.34 287	98.94 201	99.70 101	97.53 267	99.25 277	99.51 116	91.90 388	99.30 179	99.63 199	98.78 48	99.64 250	88.09 397	99.87 57	99.65 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_fmvs1		98.88 130	98.79 131	99.16 174	99.69 105	97.61 266	99.55 139	99.49 146	99.32 14	99.98 6	99.91 20	91.41 324	99.96 31	99.82 16	99.92 27	99.90 16

tfpn200view9		97.72 270	97.38 280	98.72 240	99.69 105	97.96 245	99.50 166	98.73 368	97.83 179	99.17 213	98.45 372	91.67 318	99.83 171	93.22 371	98.18 239	98.37 355

thres400		97.77 259	97.38 280	98.92 205	99.69 105	97.96 245	99.50 166	98.73 368	97.83 179	99.17 213	98.45 372	91.67 318	99.83 171	93.22 371	98.18 239	98.96 255

Test_1112_low_res		98.89 129	98.66 145	99.57 94	99.69 105	98.95 163	99.03 322	99.47 178	96.98 271	99.15 215	99.23 318	96.77 143	99.89 132	98.83 121	98.78 204	99.86 32

MVSMamba_PlusPlus		99.46 31	99.41 26	99.64 79	99.68 109	99.50 88	99.75 41	99.50 136	98.27 119	99.87 26	99.92 14	98.09 101	99.94 69	99.65 24	99.95 17	99.47 189

1112_ss		98.98 122	98.77 132	99.59 89	99.68 109	99.02 149	99.25 277	99.48 158	97.23 248	99.13 217	99.58 217	96.93 139	99.90 121	98.87 108	98.78 204	99.84 39

MM		99.40 51	99.28 57	99.74 61	99.67 111	99.31 111	99.52 152	98.87 347	99.55 1	99.74 63	99.80 105	96.47 155	99.98 13	99.97 1	99.97 7	99.94 11

test_vis1_rt		95.81 340	95.65 339	96.32 366	99.67 111	91.35 393	99.49 177	96.74 402	98.25 122	95.24 381	98.10 387	74.96 402	99.90 121	99.53 35	98.85 198	97.70 387

TEST9					99.67 111	99.65 57	99.05 317	99.41 217	96.22 327	98.95 251	99.49 250	98.77 51	99.91 109

train_agg		99.02 116	98.77 132	99.77 55	99.67 111	99.65 57	99.05 317	99.41 217	96.28 321	98.95 251	99.49 250	98.76 52	99.91 109	97.63 247	99.72 121	99.75 88

test_8					99.67 111	99.61 67	99.03 322	99.41 217	96.28 321	98.93 254	99.48 255	98.76 52	99.91 109

agg_prior					99.67 111	99.62 65		99.40 223		98.87 264			99.91 109

mamv4		99.33 59	99.42 22	99.07 182	99.67 111	97.73 257	99.42 208	99.60 54	98.15 136	99.94 15	99.91 20	98.42 85	99.94 69	99.72 18	99.96 12	99.54 163

test_prior				99.68 68	99.67 111	99.48 91		99.56 70					99.83 171			99.74 92

TSAR-MVS + GP.		99.36 56	99.36 35	99.36 140	99.67 111	98.61 201	99.07 312	99.33 262	99.00 43	99.82 38	99.81 92	99.06 16	99.84 158	99.09 83	99.42 151	99.65 129

OMC-MVS		99.08 108	99.04 87	99.20 170	99.67 111	98.22 230	99.28 261	99.52 102	98.07 151	99.66 86	99.81 92	97.79 110	99.78 198	97.79 230	99.81 96	99.60 147

Anonymous20240529		98.09 204	97.68 240	99.34 142	99.66 121	98.44 220	99.40 219	99.43 212	93.67 374	99.22 199	99.89 31	90.23 340	99.93 87	99.26 68	98.33 226	99.66 125

tttt0517		98.42 172	98.14 186	99.28 161	99.66 121	98.38 224	99.74 45	96.85 399	97.68 198	99.79 45	99.74 144	91.39 325	99.89 132	98.83 121	99.56 142	99.57 158

CHOSEN 280x420		99.12 98	99.13 75	99.08 181	99.66 121	97.89 250	98.43 386	99.71 13	98.88 59	99.62 105	99.76 136	96.63 148	99.70 231	99.46 47	99.99 1	99.66 125

casdiffmvs_mvg		99.15 87	99.02 93	99.55 98	99.66 121	99.09 139	99.64 82	99.56 70	98.26 121	99.45 139	99.87 45	96.03 170	99.81 183	99.54 33	99.15 173	99.73 97
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		99.15 87	99.02 93	99.53 107	99.66 121	99.14 134	99.72 51	99.48 158	98.35 111	99.42 149	99.84 64	96.07 168	99.79 193	99.51 38	99.14 174	99.67 122

PLC	97.94 4	99.02 116	98.85 123	99.53 107	99.66 121	99.01 151	99.24 279	99.52 102	96.85 281	99.27 188	99.48 255	98.25 94	99.91 109	97.76 235	99.62 137	99.65 129
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
casdiffmvs		99.13 92	98.98 102	99.56 96	99.65 127	99.16 129	99.56 126	99.50 136	98.33 114	99.41 153	99.86 49	95.92 176	99.83 171	99.45 48	99.16 170	99.70 113
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPP-MVSNet		99.13 92	98.99 99	99.53 107	99.65 127	99.06 145	99.81 19	99.33 262	97.43 229	99.60 111	99.88 36	97.14 128	99.84 158	99.13 78	98.94 190	99.69 115

thres200		97.61 285	97.28 295	98.62 248	99.64 129	98.03 239	99.26 275	98.74 362	97.68 198	99.09 227	98.32 378	91.66 320	99.81 183	92.88 376	98.22 234	98.03 373

test12				99.75 58	99.64 129	99.61 67		99.29 284		99.21 202		98.38 88	99.89 132		99.74 118	99.74 92

ab-mvs		98.86 134	98.63 147	99.54 99	99.64 129	99.19 124	99.44 197	99.54 86	97.77 187	99.30 179	99.81 92	94.20 248	99.93 87	99.17 76	98.82 201	99.49 182

DPM-MVS		98.95 125	98.71 138	99.66 70	99.63 132	99.55 78	98.64 375	99.10 311	97.93 166	99.42 149	99.55 228	98.67 66	99.80 190	95.80 332	99.68 129	99.61 144

thisisatest0530		98.35 181	98.03 201	99.31 149	99.63 132	98.56 204	99.54 143	96.75 401	97.53 216	99.73 65	99.65 187	91.25 328	99.89 132	98.62 147	99.56 142	99.48 183

xiu_mvs_v1_base_debu		99.29 65	99.27 60	99.34 142	99.63 132	98.97 156	99.12 302	99.51 116	98.86 60	99.84 32	99.47 258	98.18 97	99.99 4	99.50 39	99.31 161	99.08 238

xiu_mvs_v1_base		99.29 65	99.27 60	99.34 142	99.63 132	98.97 156	99.12 302	99.51 116	98.86 60	99.84 32	99.47 258	98.18 97	99.99 4	99.50 39	99.31 161	99.08 238

xiu_mvs_v1_base_debi		99.29 65	99.27 60	99.34 142	99.63 132	98.97 156	99.12 302	99.51 116	98.86 60	99.84 32	99.47 258	98.18 97	99.99 4	99.50 39	99.31 161	99.08 238

DeepC-MVS_fast	98.69 1	99.49 22	99.39 29	99.77 55	99.63 132	99.59 70	99.36 235	99.46 187	99.07 35	99.79 45	99.82 78	98.85 39	99.92 98	98.68 140	99.87 57	99.82 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UA-Net		99.42 44	99.29 55	99.80 46	99.62 138	99.55 78	99.50 166	99.70 15	98.79 70	99.77 54	99.96 1	97.45 117	99.96 31	98.92 101	99.90 42	99.89 19

CNVR-MVS		99.42 44	99.30 51	99.78 52	99.62 138	99.71 44	99.26 275	99.52 102	98.82 65	99.39 160	99.71 155	98.96 24	99.85 151	98.59 155	99.80 100	99.77 82

WTY-MVS		99.06 110	98.88 118	99.61 86	99.62 138	99.16 129	99.37 230	99.56 70	98.04 158	99.53 126	99.62 204	96.84 140	99.94 69	98.85 115	98.49 220	99.72 103

sss		99.17 83	99.05 85	99.53 107	99.62 138	98.97 156	99.36 235	99.62 41	97.83 179	99.67 81	99.65 187	97.37 121	99.95 59	99.19 72	99.19 169	99.68 119

mvsany_test1		99.50 20	99.46 20	99.62 85	99.61 142	99.09 139	98.94 345	99.48 158	99.10 27	99.96 14	99.91 20	98.85 39	99.96 31	99.72 18	99.58 141	99.82 54

GeoE		98.85 141	98.62 152	99.53 107	99.61 142	99.08 142	99.80 24	99.51 116	97.10 261	99.31 176	99.78 124	95.23 203	99.77 200	98.21 193	99.03 185	99.75 88

diffmvs		99.14 90	99.02 93	99.51 115	99.61 142	98.96 160	99.28 261	99.49 146	98.46 98	99.72 70	99.71 155	96.50 154	99.88 137	99.31 60	99.11 176	99.67 122
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
NCCC		99.34 58	99.19 70	99.79 49	99.61 142	99.65 57	99.30 251	99.48 158	98.86 60	99.21 202	99.63 199	98.72 61	99.90 121	98.25 191	99.63 136	99.80 70

PCF-MVS	97.08 14	97.66 281	97.06 306	99.47 124	99.61 142	99.09 139	98.04 400	99.25 291	91.24 391	98.51 310	99.70 159	94.55 236	99.91 109	92.76 379	99.85 72	99.42 201
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSLP-MVS++		99.46 31	99.47 17	99.44 131	99.60 147	99.16 129	99.41 211	99.71 13	98.98 48	99.45 139	99.78 124	99.19 9	99.54 264	99.28 64	99.84 80	99.63 140

DeepPCF-MVS	98.18 3	98.81 145	99.37 33	97.12 351	99.60 147	91.75 391	98.61 376	99.44 206	99.35 12	99.83 37	99.85 54	98.70 63	99.81 183	99.02 89	99.91 34	99.81 61

tt0805		97.97 228	97.77 229	98.57 254	99.59 149	96.61 319	99.45 191	99.08 314	98.21 129	98.88 261	99.80 105	88.66 357	99.70 231	98.58 156	97.72 259	99.39 207

IterMVS-LS		98.46 169	98.42 168	98.58 253	99.59 149	98.00 241	99.37 230	99.43 212	96.94 277	99.07 229	99.59 213	97.87 107	99.03 344	98.32 187	95.62 329	98.71 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		97.83 249	97.77 229	98.02 311	99.58 151	96.27 331	99.02 325	99.48 158	97.22 249	98.71 282	99.70 159	92.75 284	99.13 330	97.46 266	96.00 317	98.67 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CNLPA		99.14 90	98.99 99	99.59 89	99.58 151	99.41 100	99.16 293	99.44 206	98.45 99	99.19 208	99.49 250	98.08 102	99.89 132	97.73 239	99.75 115	99.48 183

Anonymous202405211		98.30 185	97.98 206	99.26 163	99.57 153	98.16 232	99.41 211	98.55 376	96.03 342	99.19 208	99.74 144	91.87 311	99.92 98	99.16 77	98.29 231	99.70 113

IterMVS-SCA-FT		97.82 252	97.75 234	98.06 308	99.57 153	96.36 327	99.02 325	99.49 146	97.18 251	98.71 282	99.72 154	92.72 287	99.14 327	97.44 268	95.86 323	98.67 299

PS-MVSNAJ		99.32 61	99.32 43	99.30 154	99.57 153	98.94 166	98.97 339	99.46 187	98.92 57	99.71 71	99.24 317	99.01 18	99.98 13	99.35 53	99.66 131	98.97 253

MG-MVS		99.13 92	99.02 93	99.45 127	99.57 153	98.63 198	99.07 312	99.34 255	98.99 45	99.61 108	99.82 78	97.98 106	99.87 142	97.00 293	99.80 100	99.85 35

OPU-MVS				99.64 79	99.56 157	99.72 42	99.60 99				99.70 159	99.27 5	99.42 280	98.24 192	99.80 100	99.79 74

EC-MVSNet		99.44 39	99.39 29	99.58 92	99.56 157	99.49 89	99.88 3	99.58 62	98.38 106	99.73 65	99.69 169	98.20 96	99.70 231	99.64 26	99.82 93	99.54 163

PHI-MVS		99.30 63	99.17 72	99.70 67	99.56 157	99.52 86	99.58 113	99.80 8	97.12 257	99.62 105	99.73 150	98.58 72	99.90 121	98.61 150	99.91 34	99.68 119

AdaColmap		99.01 120	98.80 128	99.66 70	99.56 157	99.54 80	99.18 291	99.70 15	98.18 134	99.35 170	99.63 199	96.32 161	99.90 121	97.48 263	99.77 110	99.55 161

dmvs_re		98.08 206	98.16 183	97.85 324	99.55 161	94.67 366	99.70 55	98.92 336	98.15 136	99.06 234	99.35 292	93.67 269	99.25 309	97.77 234	97.25 291	99.64 136

FA-MVS(test-final)		98.75 152	98.53 163	99.41 133	99.55 161	99.05 147	99.80 24	99.01 325	96.59 303	99.58 115	99.59 213	95.39 194	99.90 121	97.78 231	99.49 147	99.28 221

balanced_conf03		99.46 31	99.39 29	99.67 69	99.55 161	99.58 75	99.74 45	99.51 116	98.42 103	99.87 26	99.84 64	98.05 104	99.91 109	99.58 29	99.94 23	99.52 170

FE-MVS		98.48 167	98.17 182	99.40 134	99.54 164	98.96 160	99.68 65	98.81 354	95.54 348	99.62 105	99.70 159	93.82 264	99.93 87	97.35 274	99.46 148	99.32 218

test_vis1_n		97.92 234	97.44 272	99.34 142	99.53 165	98.08 237	99.74 45	99.49 146	99.15 20	100.00 1	99.94 6	79.51 401	99.98 13	99.88 13	99.76 113	99.97 4

APD_test1		95.87 338	96.49 320	94.00 373	99.53 165	84.01 402	99.54 143	99.32 272	95.91 344	97.99 339	99.85 54	85.49 380	99.88 137	91.96 382	98.84 199	98.12 367

ET-MVSNet_ETH3D		96.49 326	95.64 340	99.05 186	99.53 165	98.82 182	98.84 355	97.51 395	97.63 203	84.77 404	99.21 322	92.09 307	98.91 363	98.98 92	92.21 381	99.41 204

xiu_mvs_v2_base		99.26 71	99.25 64	99.29 157	99.53 165	98.91 170	99.02 325	99.45 198	98.80 69	99.71 71	99.26 315	98.94 29	99.98 13	99.34 57	99.23 166	98.98 252

fmvsm_s_conf0.1_n_a		99.26 71	99.06 84	99.85 28	99.52 169	99.62 65	99.54 143	99.62 41	98.69 80	99.99 2	99.96 1	94.47 240	99.94 69	99.88 13	99.92 27	99.98 2

LFMVS		97.90 237	97.35 284	99.54 99	99.52 169	99.01 151	99.39 223	98.24 383	97.10 261	99.65 93	99.79 117	84.79 385	99.91 109	99.28 64	98.38 223	99.69 115

VNet		99.11 102	98.90 114	99.73 64	99.52 169	99.56 76	99.41 211	99.39 226	99.01 40	99.74 63	99.78 124	95.56 189	99.92 98	99.52 37	98.18 239	99.72 103

DVP-MVS++		99.59 8	99.50 13	99.88 5	99.51 172	99.88 8	99.87 7	99.51 116	98.99 45	99.88 21	99.81 92	99.27 5	99.96 31	98.85 115	99.80 100	99.81 61

MSC_two_6792asdad				99.87 11	99.51 172	99.76 37		99.33 262					99.96 31	98.87 108	99.84 80	99.89 19

No_MVS				99.87 11	99.51 172	99.76 37		99.33 262					99.96 31	98.87 108	99.84 80	99.89 19

Fast-Effi-MVS+		98.70 156	98.43 167	99.51 115	99.51 172	99.28 115	99.52 152	99.47 178	96.11 337	99.01 240	99.34 296	96.20 165	99.84 158	97.88 220	98.82 201	99.39 207

MVSFormer		99.17 83	99.12 76	99.29 157	99.51 172	98.94 166	99.88 3	99.46 187	97.55 212	99.80 43	99.65 187	97.39 118	99.28 304	99.03 87	99.85 72	99.65 129

lupinMVS		99.13 92	99.01 97	99.46 126	99.51 172	98.94 166	99.05 317	99.16 305	97.86 173	99.80 43	99.56 225	97.39 118	99.86 145	98.94 96	99.85 72	99.58 155

GBi-Net		97.68 277	97.48 261	98.29 291	99.51 172	97.26 278	99.43 201	99.48 158	96.49 307	99.07 229	99.32 303	90.26 337	98.98 351	97.10 288	96.65 301	98.62 320

test1		97.68 277	97.48 261	98.29 291	99.51 172	97.26 278	99.43 201	99.48 158	96.49 307	99.07 229	99.32 303	90.26 337	98.98 351	97.10 288	96.65 301	98.62 320

FMVSNet2		97.72 270	97.36 282	98.80 234	99.51 172	98.84 178	99.45 191	99.42 214	96.49 307	98.86 268	99.29 308	90.26 337	98.98 351	96.44 319	96.56 304	98.58 334

thisisatest0515		98.14 199	97.79 224	99.19 171	99.50 181	98.50 214	98.61 376	96.82 400	96.95 275	99.54 124	99.43 267	91.66 320	99.86 145	98.08 206	99.51 146	99.22 227

baseline1		98.31 183	97.95 210	99.38 139	99.50 181	98.74 188	99.59 105	98.93 333	98.41 104	99.14 216	99.60 211	94.59 232	99.79 193	98.48 169	93.29 370	99.61 144

hse-mvs2		97.50 293	97.14 301	98.59 250	99.49 183	97.05 291	99.28 261	99.22 296	98.94 54	99.66 86	99.42 269	94.93 208	99.65 247	99.48 44	83.80 402	99.08 238

EIA-MVS		99.18 81	99.09 81	99.45 127	99.49 183	99.18 126	99.67 68	99.53 97	97.66 201	99.40 158	99.44 265	98.10 100	99.81 183	98.94 96	99.62 137	99.35 213

test_yl		98.86 134	98.63 147	99.54 99	99.49 183	99.18 126	99.50 166	99.07 317	98.22 127	99.61 108	99.51 244	95.37 195	99.84 158	98.60 153	98.33 226	99.59 151

DCV-MVSNet		98.86 134	98.63 147	99.54 99	99.49 183	99.18 126	99.50 166	99.07 317	98.22 127	99.61 108	99.51 244	95.37 195	99.84 158	98.60 153	98.33 226	99.59 151

VDDNet		97.55 288	97.02 307	99.16 174	99.49 183	98.12 236	99.38 228	99.30 280	95.35 350	99.68 77	99.90 27	82.62 394	99.93 87	99.31 60	98.13 243	99.42 201

MVS_Test		99.10 106	98.97 103	99.48 121	99.49 183	99.14 134	99.67 68	99.34 255	97.31 240	99.58 115	99.76 136	97.65 114	99.82 178	98.87 108	99.07 182	99.46 194

BH-untuned		98.42 172	98.36 171	98.59 250	99.49 183	96.70 312	99.27 266	99.13 309	97.24 247	98.80 273	99.38 283	95.75 183	99.74 209	97.07 291	99.16 170	99.33 217

AUN-MVS		96.88 318	96.31 324	98.59 250	99.48 190	97.04 294	99.27 266	99.22 296	97.44 228	98.51 310	99.41 273	91.97 309	99.66 242	97.71 242	83.83 401	99.07 243

VDD-MVS		97.73 268	97.35 284	98.88 216	99.47 191	97.12 284	99.34 243	98.85 349	98.19 131	99.67 81	99.85 54	82.98 392	99.92 98	99.49 43	98.32 230	99.60 147

mvsmamba		99.06 110	98.96 107	99.36 140	99.47 191	98.64 197	99.70 55	99.05 320	97.61 205	99.65 93	99.83 69	96.54 152	99.92 98	99.19 72	99.62 137	99.51 177

ETV-MVS		99.26 71	99.21 68	99.40 134	99.46 193	99.30 113	99.56 126	99.52 102	98.52 94	99.44 144	99.27 313	98.41 87	99.86 145	99.10 82	99.59 140	99.04 245

Effi-MVS+		98.81 145	98.59 158	99.48 121	99.46 193	99.12 137	98.08 399	99.50 136	97.50 220	99.38 162	99.41 273	96.37 160	99.81 183	99.11 80	98.54 217	99.51 177

RRT-MVS		98.91 128	98.75 134	99.39 138	99.46 193	98.61 201	99.76 36	99.50 136	98.06 155	99.81 39	99.88 36	93.91 261	99.94 69	99.11 80	99.27 164	99.61 144

jason		99.13 92	99.03 89	99.45 127	99.46 193	98.87 173	99.12 302	99.26 289	98.03 160	99.79 45	99.65 187	97.02 135	99.85 151	99.02 89	99.90 42	99.65 129
jason: jason.
TAMVS		99.12 98	99.08 82	99.24 166	99.46 193	98.55 205	99.51 159	99.46 187	98.09 146	99.45 139	99.82 78	98.34 90	99.51 265	98.70 135	98.93 191	99.67 122

ACMH+	97.24 10	97.92 234	97.78 227	98.32 288	99.46 193	96.68 316	99.56 126	99.54 86	98.41 104	97.79 348	99.87 45	90.18 341	99.66 242	98.05 210	97.18 295	98.62 320

MIMVSNet		97.73 268	97.45 267	98.57 254	99.45 199	97.50 268	99.02 325	98.98 328	96.11 337	99.41 153	99.14 328	90.28 336	98.74 371	95.74 333	98.93 191	99.47 189

test_fmvsmconf0.1_n		99.55 14	99.45 21	99.86 21	99.44 200	99.65 57	99.50 166	99.61 48	99.45 5	99.87 26	99.92 14	97.31 123	99.97 21	99.95 7	99.99 1	99.97 4

test_fmvs2		97.25 308	97.30 292	97.09 352	99.43 201	93.31 383	99.73 49	98.87 347	98.83 64	99.28 183	99.80 105	84.45 387	99.66 242	97.88 220	97.45 281	98.30 357

alignmvs		98.81 145	98.56 161	99.58 92	99.43 201	99.42 98	99.51 159	98.96 331	98.61 86	99.35 170	98.92 354	94.78 218	99.77 200	99.35 53	98.11 244	99.54 163

MGCFI-Net		99.01 120	98.85 123	99.50 120	99.42 203	99.26 118	99.82 15	99.48 158	98.60 87	99.28 183	98.81 359	97.04 134	99.76 204	99.29 63	97.87 253	99.47 189

sasdasda		99.02 116	98.86 121	99.51 115	99.42 203	99.32 107	99.80 24	99.48 158	98.63 83	99.31 176	98.81 359	97.09 130	99.75 207	99.27 66	97.90 250	99.47 189

canonicalmvs		99.02 116	98.86 121	99.51 115	99.42 203	99.32 107	99.80 24	99.48 158	98.63 83	99.31 176	98.81 359	97.09 130	99.75 207	99.27 66	97.90 250	99.47 189

HY-MVS	97.30 7	98.85 141	98.64 146	99.47 124	99.42 203	99.08 142	99.62 92	99.36 243	97.39 234	99.28 183	99.68 175	96.44 158	99.92 98	98.37 180	98.22 234	99.40 206

CDS-MVSNet		99.09 107	99.03 89	99.25 164	99.42 203	98.73 189	99.45 191	99.46 187	98.11 143	99.46 138	99.77 132	98.01 105	99.37 287	98.70 135	98.92 193	99.66 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet		99.25 75	99.14 74	99.59 89	99.41 208	99.16 129	99.35 240	99.57 65	98.82 65	99.51 130	99.61 208	96.46 156	99.95 59	99.59 27	99.98 4	99.65 129

Fast-Effi-MVS+-dtu		98.77 151	98.83 127	98.60 249	99.41 208	96.99 298	99.52 152	99.49 146	98.11 143	99.24 194	99.34 296	96.96 138	99.79 193	97.95 216	99.45 149	99.02 248

BH-RMVSNet		98.41 174	98.08 195	99.40 134	99.41 208	98.83 181	99.30 251	98.77 358	97.70 196	98.94 253	99.65 187	92.91 282	99.74 209	96.52 317	99.55 144	99.64 136

ACMM	97.58 5	98.37 180	98.34 173	98.48 265	99.41 208	97.10 285	99.56 126	99.45 198	98.53 93	99.04 237	99.85 54	93.00 278	99.71 225	98.74 130	97.45 281	98.64 311
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	97.28 8	98.10 203	97.99 205	98.44 276	99.41 208	96.96 302	99.60 99	99.56 70	98.09 146	98.15 332	99.91 20	90.87 332	99.70 231	98.88 105	97.45 281	98.67 299
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_ETH3D		97.32 305	96.81 313	98.87 220	99.40 213	97.46 269	99.51 159	99.53 97	95.86 345	98.54 309	99.77 132	82.44 395	99.66 242	98.68 140	97.52 273	99.50 181

PAPR		98.63 163	98.34 173	99.51 115	99.40 213	99.03 148	98.80 359	99.36 243	96.33 318	99.00 244	99.12 332	98.46 81	99.84 158	95.23 347	99.37 160	99.66 125

API-MVS		99.04 113	99.03 89	99.06 184	99.40 213	99.31 111	99.55 139	99.56 70	98.54 92	99.33 174	99.39 281	98.76 52	99.78 198	96.98 295	99.78 107	98.07 370

dongtai		93.26 360	92.93 364	94.25 372	99.39 216	85.68 400	97.68 403	93.27 414	92.87 383	96.85 369	99.39 281	82.33 396	97.48 395	76.78 408	97.80 256	99.58 155

FMVSNet3		98.03 216	97.76 233	98.84 227	99.39 216	98.98 153	99.40 219	99.38 234	96.67 291	99.07 229	99.28 310	92.93 279	98.98 351	97.10 288	96.65 301	98.56 336

test_fmvsmvis_n_1920		99.65 6	99.61 6	99.77 55	99.38 218	99.37 102	99.58 113	99.62 41	99.41 9	99.87 26	99.92 14	98.81 44	100.00 1	99.97 1	99.93 25	99.94 11

GA-MVS		97.85 243	97.47 264	99.00 192	99.38 218	97.99 242	98.57 379	99.15 306	97.04 268	98.90 258	99.30 306	89.83 344	99.38 284	96.70 310	98.33 226	99.62 142

mvs_anonymous		99.03 115	98.99 99	99.16 174	99.38 218	98.52 211	99.51 159	99.38 234	97.79 184	99.38 162	99.81 92	97.30 124	99.45 270	99.35 53	98.99 188	99.51 177

testing3		97.28 306	96.76 315	98.82 229	99.37 221	98.07 238	99.45 191	99.36 243	97.56 211	97.89 343	98.95 349	83.70 390	98.82 367	96.03 326	98.56 215	99.58 155

ACMP	97.20 11	98.06 208	97.94 212	98.45 273	99.37 221	97.01 296	99.44 197	99.49 146	97.54 215	98.45 314	99.79 117	91.95 310	99.72 219	97.91 218	97.49 279	98.62 320
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MAR-MVS		98.86 134	98.63 147	99.54 99	99.37 221	99.66 53	99.45 191	99.54 86	96.61 298	99.01 240	99.40 277	97.09 130	99.86 145	97.68 246	99.53 145	99.10 233
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
testgi		97.65 282	97.50 259	98.13 305	99.36 224	96.45 324	99.42 208	99.48 158	97.76 188	97.87 344	99.45 264	91.09 329	98.81 368	94.53 356	98.52 218	99.13 232

EI-MVSNet		98.67 159	98.67 142	98.68 245	99.35 225	97.97 243	99.50 166	99.38 234	96.93 278	99.20 205	99.83 69	97.87 107	99.36 291	98.38 178	97.56 269	98.71 278

CVMVSNet		98.57 165	98.67 142	98.30 290	99.35 225	95.59 345	99.50 166	99.55 78	98.60 87	99.39 160	99.83 69	94.48 239	99.45 270	98.75 129	98.56 215	99.85 35

BH-w/o		98.00 223	97.89 219	98.32 288	99.35 225	96.20 334	99.01 330	98.90 342	96.42 315	98.38 317	99.00 343	95.26 201	99.72 219	96.06 325	98.61 209	99.03 246

MVSTER		98.49 166	98.32 175	99.00 192	99.35 225	99.02 149	99.54 143	99.38 234	97.41 232	99.20 205	99.73 150	93.86 263	99.36 291	98.87 108	97.56 269	98.62 320

miper_lstm_enhance		98.00 223	97.91 214	98.28 295	99.34 229	97.43 270	98.88 351	99.36 243	96.48 310	98.80 273	99.55 228	95.98 171	98.91 363	97.27 277	95.50 334	98.51 339

Effi-MVS+-dtu		98.78 149	98.89 117	98.47 270	99.33 230	96.91 304	99.57 120	99.30 280	98.47 97	99.41 153	98.99 344	96.78 142	99.74 209	98.73 132	99.38 153	98.74 274

CANet_DTU		98.97 124	98.87 119	99.25 164	99.33 230	98.42 223	99.08 311	99.30 280	99.16 19	99.43 146	99.75 139	95.27 199	99.97 21	98.56 162	99.95 17	99.36 212

ADS-MVSNet2		98.02 218	98.07 198	97.87 323	99.33 230	95.19 357	99.23 281	99.08 314	96.24 325	99.10 224	99.67 181	94.11 252	98.93 362	96.81 305	99.05 183	99.48 183

ADS-MVSNet		98.20 192	98.08 195	98.56 257	99.33 230	96.48 323	99.23 281	99.15 306	96.24 325	99.10 224	99.67 181	94.11 252	99.71 225	96.81 305	99.05 183	99.48 183

LPG-MVS_test		98.22 189	98.13 188	98.49 263	99.33 230	97.05 291	99.58 113	99.55 78	97.46 222	99.24 194	99.83 69	92.58 294	99.72 219	98.09 202	97.51 274	98.68 292

LGP-MVS_train				98.49 263	99.33 230	97.05 291		99.55 78	97.46 222	99.24 194	99.83 69	92.58 294	99.72 219	98.09 202	97.51 274	98.68 292

FMVSNet1		96.84 319	96.36 323	98.29 291	99.32 236	97.26 278	99.43 201	99.48 158	95.11 354	98.55 308	99.32 303	83.95 389	98.98 351	95.81 331	96.26 311	98.62 320

PVSNet_0	94.43 19	96.09 335	95.47 342	97.94 318	99.31 237	94.34 372	97.81 401	99.70 15	97.12 257	97.46 352	98.75 364	89.71 345	99.79 193	97.69 245	81.69 404	99.68 119

c3_l		98.12 202	98.04 200	98.38 283	99.30 238	97.69 263	98.81 358	99.33 262	96.67 291	98.83 269	99.34 296	97.11 129	98.99 350	97.58 251	95.34 336	98.48 341

SCA		98.19 193	98.16 183	98.27 296	99.30 238	95.55 346	99.07 312	98.97 329	97.57 209	99.43 146	99.57 222	92.72 287	99.74 209	97.58 251	99.20 168	99.52 170

LCM-MVSNet-Re		97.83 249	98.15 185	96.87 359	99.30 238	92.25 389	99.59 105	98.26 381	97.43 229	96.20 375	99.13 329	96.27 163	98.73 372	98.17 198	98.99 188	99.64 136

MVS-HIRNet		95.75 341	95.16 346	97.51 341	99.30 238	93.69 379	98.88 351	95.78 406	85.09 403	98.78 276	92.65 406	91.29 327	99.37 287	94.85 353	99.85 72	99.46 194

HQP_MVS		98.27 188	98.22 181	98.44 276	99.29 242	96.97 300	99.39 223	99.47 178	98.97 51	99.11 221	99.61 208	92.71 289	99.69 236	97.78 231	97.63 262	98.67 299

plane_prior7					99.29 242	97.03 295

ITE_SJBPF				98.08 307	99.29 242	96.37 326		98.92 336	98.34 112	98.83 269	99.75 139	91.09 329	99.62 256	95.82 330	97.40 287	98.25 361

DeepMVS_CX				93.34 376	99.29 242	82.27 405		99.22 296	85.15 402	96.33 373	99.05 337	90.97 331	99.73 215	93.57 368	97.77 258	98.01 374

CLD-MVS		98.16 197	98.10 191	98.33 286	99.29 242	96.82 309	98.75 364	99.44 206	97.83 179	99.13 217	99.55 228	92.92 280	99.67 239	98.32 187	97.69 260	98.48 341
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
plane_prior6					99.27 247	96.98 299						92.71 289

PMMVS		98.80 148	98.62 152	99.34 142	99.27 247	98.70 191	98.76 363	99.31 276	97.34 237	99.21 202	99.07 334	97.20 127	99.82 178	98.56 162	98.87 196	99.52 170

eth_miper_zixun_eth		98.05 213	97.96 208	98.33 286	99.26 249	97.38 272	98.56 381	99.31 276	96.65 293	98.88 261	99.52 240	96.58 150	99.12 334	97.39 271	95.53 333	98.47 343

D2MVS		98.41 174	98.50 164	98.15 304	99.26 249	96.62 318	99.40 219	99.61 48	97.71 193	98.98 246	99.36 289	96.04 169	99.67 239	98.70 135	97.41 286	98.15 366

plane_prior1					99.26 249

XXY-MVS		98.38 178	98.09 194	99.24 166	99.26 249	99.32 107	99.56 126	99.55 78	97.45 225	98.71 282	99.83 69	93.23 273	99.63 255	98.88 105	96.32 310	98.76 269

UBG		97.85 243	97.48 261	98.95 199	99.25 253	97.64 264	99.24 279	98.74 362	97.90 169	98.64 298	98.20 382	88.65 358	99.81 183	98.27 190	98.40 222	99.42 201

cl____		98.01 221	97.84 222	98.55 259	99.25 253	97.97 243	98.71 368	99.34 255	96.47 312	98.59 306	99.54 233	95.65 187	99.21 321	97.21 280	95.77 324	98.46 346

WBMVS		97.74 266	97.50 259	98.46 271	99.24 255	97.43 270	99.21 287	99.42 214	97.45 225	98.96 250	99.41 273	88.83 353	99.23 312	98.94 96	96.02 315	98.71 278

DIV-MVS_self_test		98.01 221	97.85 221	98.48 265	99.24 255	97.95 247	98.71 368	99.35 250	96.50 306	98.60 305	99.54 233	95.72 185	99.03 344	97.21 280	95.77 324	98.46 346

ETVMVS		97.50 293	96.90 311	99.29 157	99.23 257	98.78 187	99.32 246	98.90 342	97.52 218	98.56 307	98.09 388	84.72 386	99.69 236	97.86 223	97.88 252	99.39 207

miper_ehance_all_eth		98.18 195	98.10 191	98.41 279	99.23 257	97.72 259	98.72 367	99.31 276	96.60 301	98.88 261	99.29 308	97.29 125	99.13 330	97.60 249	95.99 318	98.38 354

NP-MVS					99.23 257	96.92 303					99.40 277

LTVRE_ROB	97.16 12	98.02 218	97.90 215	98.40 281	99.23 257	96.80 310	99.70 55	99.60 54	97.12 257	98.18 331	99.70 159	91.73 316	99.72 219	98.39 177	97.45 281	98.68 292
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
UGNet		98.87 131	98.69 140	99.40 134	99.22 261	98.72 190	99.44 197	99.68 20	99.24 17	99.18 212	99.42 269	92.74 286	99.96 31	99.34 57	99.94 23	99.53 169
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
VPNet		97.84 247	97.44 272	99.01 190	99.21 262	98.94 166	99.48 181	99.57 65	98.38 106	99.28 183	99.73 150	88.89 352	99.39 282	99.19 72	93.27 371	98.71 278

IB-MVS	95.67 18	96.22 330	95.44 344	98.57 254	99.21 262	96.70 312	98.65 374	97.74 393	96.71 288	97.27 358	98.54 370	86.03 376	99.92 98	98.47 172	86.30 398	99.10 233
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
testing11		97.50 293	97.10 304	98.71 242	99.20 264	96.91 304	99.29 256	98.82 352	97.89 170	98.21 329	98.40 374	85.63 379	99.83 171	98.45 174	98.04 246	99.37 211

tfpnnormal		97.84 247	97.47 264	98.98 194	99.20 264	99.22 123	99.64 82	99.61 48	96.32 319	98.27 325	99.70 159	93.35 272	99.44 275	95.69 335	95.40 335	98.27 359

QAPM		98.67 159	98.30 177	99.80 46	99.20 264	99.67 51	99.77 33	99.72 11	94.74 364	98.73 280	99.90 27	95.78 182	99.98 13	96.96 297	99.88 54	99.76 87

HQP-NCC					99.19 267		98.98 336		98.24 123	98.66 291

ACMP_Plane					99.19 267		98.98 336		98.24 123	98.66 291

HQP-MVS		98.02 218	97.90 215	98.37 284	99.19 267	96.83 307	98.98 336	99.39 226	98.24 123	98.66 291	99.40 277	92.47 298	99.64 250	97.19 284	97.58 267	98.64 311

testing91		97.44 300	97.02 307	98.71 242	99.18 270	96.89 306	99.19 289	99.04 321	97.78 186	98.31 321	98.29 379	85.41 381	99.85 151	98.01 212	97.95 248	99.39 207

testing99		97.36 303	96.94 310	98.63 247	99.18 270	96.70 312	99.30 251	98.93 333	97.71 193	98.23 326	98.26 380	84.92 384	99.84 158	98.04 211	97.85 255	99.35 213

Patchmatch-test		97.93 231	97.65 243	98.77 237	99.18 270	97.07 289	99.03 322	99.14 308	96.16 332	98.74 279	99.57 222	94.56 234	99.72 219	93.36 370	99.11 176	99.52 170

FIs		98.78 149	98.63 147	99.23 168	99.18 270	99.54 80	99.83 14	99.59 58	98.28 117	98.79 275	99.81 92	96.75 144	99.37 287	99.08 84	96.38 308	98.78 264

baseline2		97.87 240	97.55 252	98.82 229	99.18 270	98.02 240	99.41 211	96.58 405	96.97 272	96.51 371	99.17 324	93.43 270	99.57 260	97.71 242	99.03 185	98.86 259

CR-MVSNet		98.17 196	97.93 213	98.87 220	99.18 270	98.49 215	99.22 285	99.33 262	96.96 273	99.56 119	99.38 283	94.33 244	99.00 349	94.83 354	98.58 212	99.14 230

RPMNet		96.72 321	95.90 334	99.19 171	99.18 270	98.49 215	99.22 285	99.52 102	88.72 400	99.56 119	97.38 394	94.08 254	99.95 59	86.87 402	98.58 212	99.14 230

LS3D		99.27 69	99.12 76	99.74 61	99.18 270	99.75 39	99.56 126	99.57 65	98.45 99	99.49 134	99.85 54	97.77 111	99.94 69	98.33 185	99.84 80	99.52 170

tpm cat1		97.39 302	97.36 282	97.50 342	99.17 278	93.73 377	99.43 201	99.31 276	91.27 390	98.71 282	99.08 333	94.31 246	99.77 200	96.41 321	98.50 219	99.00 249

3Dnovator+	97.12 13	99.18 81	98.97 103	99.82 41	99.17 278	99.68 48	99.81 19	99.51 116	99.20 18	98.72 281	99.89 31	95.68 186	99.97 21	98.86 113	99.86 65	99.81 61

testing222		97.16 311	96.50 319	99.16 174	99.16 280	98.47 219	99.27 266	98.66 372	97.71 193	98.23 326	98.15 383	82.28 397	99.84 158	97.36 273	97.66 261	99.18 229

VPA-MVSNet		98.29 186	97.95 210	99.30 154	99.16 280	99.54 80	99.50 166	99.58 62	98.27 119	99.35 170	99.37 286	92.53 296	99.65 247	99.35 53	94.46 352	98.72 276

tpmrst		98.33 182	98.48 165	97.90 321	99.16 280	94.78 363	99.31 249	99.11 310	97.27 243	99.45 139	99.59 213	95.33 197	99.84 158	98.48 169	98.61 209	99.09 237

PatchmatchNet		98.31 183	98.36 171	98.19 299	99.16 280	95.32 354	99.27 266	98.92 336	97.37 235	99.37 164	99.58 217	94.90 211	99.70 231	97.43 269	99.21 167	99.54 163
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm2		97.44 300	97.34 287	97.74 333	99.15 284	94.36 371	99.45 191	98.94 332	93.45 379	98.90 258	99.44 265	91.35 326	99.59 259	97.31 275	98.07 245	99.29 220

CostFormer		97.72 270	97.73 236	97.71 334	99.15 284	94.02 374	99.54 143	99.02 324	94.67 365	99.04 237	99.35 292	92.35 304	99.77 200	98.50 168	97.94 249	99.34 216

TransMVSNet (Re)		97.15 312	96.58 317	98.86 223	99.12 286	98.85 177	99.49 177	98.91 340	95.48 349	97.16 362	99.80 105	93.38 271	99.11 335	94.16 363	91.73 382	98.62 320

3Dnovator	97.25 9	99.24 76	99.05 85	99.81 44	99.12 286	99.66 53	99.84 11	99.74 10	99.09 32	98.92 255	99.90 27	95.94 175	99.98 13	98.95 95	99.92 27	99.79 74

XVG-ACMP-BASELINE		97.83 249	97.71 238	98.20 298	99.11 288	96.33 328	99.41 211	99.52 102	98.06 155	99.05 236	99.50 247	89.64 347	99.73 215	97.73 239	97.38 288	98.53 337

FMVSNet5		96.43 328	96.19 327	97.15 348	99.11 288	95.89 340	99.32 246	99.52 102	94.47 369	98.34 320	99.07 334	87.54 371	97.07 398	92.61 380	95.72 327	98.47 343

MDTV_nov1_ep13			98.32 175		99.11 288	94.44 369	99.27 266	98.74 362	97.51 219	99.40 158	99.62 204	94.78 218	99.76 204	97.59 250	98.81 203

dmvs_testset		95.02 347	96.12 328	91.72 382	99.10 291	80.43 410	99.58 113	97.87 390	97.47 221	95.22 382	98.82 358	93.99 256	95.18 407	88.09 397	94.91 347	99.56 160

Patchmtry		97.75 264	97.40 279	98.81 232	99.10 291	98.87 173	99.11 308	99.33 262	94.83 362	98.81 271	99.38 283	94.33 244	99.02 346	96.10 324	95.57 331	98.53 337

dp		97.75 264	97.80 223	97.59 339	99.10 291	93.71 378	99.32 246	98.88 345	96.48 310	99.08 228	99.55 228	92.67 292	99.82 178	96.52 317	98.58 212	99.24 226

UWE-MVS		97.58 287	97.29 294	98.48 265	99.09 294	96.25 332	99.01 330	96.61 404	97.86 173	99.19 208	99.01 342	88.72 354	99.90 121	97.38 272	98.69 207	99.28 221

cl22		97.85 243	97.64 246	98.48 265	99.09 294	97.87 251	98.60 378	99.33 262	97.11 260	98.87 264	99.22 319	92.38 303	99.17 325	98.21 193	95.99 318	98.42 349

Baseline_NR-MVSNet		97.76 260	97.45 267	98.68 245	99.09 294	98.29 226	99.41 211	98.85 349	95.65 347	98.63 300	99.67 181	94.82 214	99.10 337	98.07 209	92.89 375	98.64 311

FC-MVSNet-test		98.75 152	98.62 152	99.15 178	99.08 297	99.45 95	99.86 10	99.60 54	98.23 126	98.70 288	99.82 78	96.80 141	99.22 316	99.07 85	96.38 308	98.79 263

USDC		97.34 304	97.20 299	97.75 332	99.07 298	95.20 356	98.51 383	99.04 321	97.99 162	98.31 321	99.86 49	89.02 350	99.55 263	95.67 337	97.36 289	98.49 340

TinyColmap		97.12 313	96.89 312	97.83 327	99.07 298	95.52 349	98.57 379	98.74 362	97.58 208	97.81 347	99.79 117	88.16 365	99.56 261	95.10 348	97.21 293	98.39 353

pm-mvs1		97.68 277	97.28 295	98.88 216	99.06 300	98.62 199	99.50 166	99.45 198	96.32 319	97.87 344	99.79 117	92.47 298	99.35 294	97.54 258	93.54 368	98.67 299

TR-MVS		97.76 260	97.41 278	98.82 229	99.06 300	97.87 251	98.87 353	98.56 375	96.63 297	98.68 290	99.22 319	92.49 297	99.65 247	95.40 343	97.79 257	98.95 257

PAPM		97.59 286	97.09 305	99.07 182	99.06 300	98.26 228	98.30 393	99.10 311	94.88 360	98.08 334	99.34 296	96.27 163	99.64 250	89.87 390	98.92 193	99.31 219

nrg030		98.64 162	98.42 168	99.28 161	99.05 303	99.69 47	99.81 19	99.46 187	98.04 158	99.01 240	99.82 78	96.69 146	99.38 284	99.34 57	94.59 351	98.78 264

tpmvs		97.98 225	98.02 203	97.84 326	99.04 304	94.73 364	99.31 249	99.20 300	96.10 341	98.76 278	99.42 269	94.94 207	99.81 183	96.97 296	98.45 221	98.97 253

OpenMVS	96.50 16	98.47 168	98.12 189	99.52 113	99.04 304	99.53 83	99.82 15	99.72 11	94.56 367	98.08 334	99.88 36	94.73 224	99.98 13	97.47 265	99.76 113	99.06 244

WR-MVS_H		98.13 200	97.87 220	98.90 211	99.02 306	98.84 178	99.70 55	99.59 58	97.27 243	98.40 316	99.19 323	95.53 190	99.23 312	98.34 184	93.78 366	98.61 329

tpm		97.67 280	97.55 252	98.03 309	99.02 306	95.01 360	99.43 201	98.54 377	96.44 313	99.12 219	99.34 296	91.83 313	99.60 258	97.75 237	96.46 306	99.48 183

Syy-MVS		97.09 315	97.14 301	96.95 356	99.00 308	92.73 387	99.29 256	99.39 226	97.06 265	97.41 353	98.15 383	93.92 260	98.68 373	91.71 383	98.34 224	99.45 197

myMVS_eth3d		96.89 317	96.37 322	98.43 278	99.00 308	97.16 282	99.29 256	99.39 226	97.06 265	97.41 353	98.15 383	83.46 391	98.68 373	95.27 346	98.34 224	99.45 197

UniMVSNet (Re)		98.29 186	98.00 204	99.13 179	99.00 308	99.36 105	99.49 177	99.51 116	97.95 164	98.97 248	99.13 329	96.30 162	99.38 284	98.36 182	93.34 369	98.66 307

v10		97.85 243	97.52 256	98.86 223	98.99 311	98.67 193	99.75 41	99.41 217	95.70 346	98.98 246	99.41 273	94.75 223	99.23 312	96.01 328	94.63 350	98.67 299

PS-CasMVS		97.93 231	97.59 251	98.95 199	98.99 311	99.06 145	99.68 65	99.52 102	97.13 255	98.31 321	99.68 175	92.44 302	99.05 341	98.51 167	94.08 361	98.75 271

PatchT		97.03 316	96.44 321	98.79 235	98.99 311	98.34 225	99.16 293	99.07 317	92.13 387	99.52 128	97.31 397	94.54 237	98.98 351	88.54 395	98.73 206	99.03 246

V42		98.06 208	97.79 224	98.86 223	98.98 314	98.84 178	99.69 59	99.34 255	96.53 305	99.30 179	99.37 286	94.67 229	99.32 299	97.57 255	94.66 349	98.42 349

LF4IMVS		97.52 290	97.46 266	97.70 335	98.98 314	95.55 346	99.29 256	98.82 352	98.07 151	98.66 291	99.64 193	89.97 342	99.61 257	97.01 292	96.68 300	97.94 381

CP-MVSNet		98.09 204	97.78 227	99.01 190	98.97 316	99.24 121	99.67 68	99.46 187	97.25 245	98.48 313	99.64 193	93.79 265	99.06 340	98.63 146	94.10 360	98.74 274

miper_enhance_ethall		98.16 197	98.08 195	98.41 279	98.96 317	97.72 259	98.45 385	99.32 272	96.95 275	98.97 248	99.17 324	97.06 133	99.22 316	97.86 223	95.99 318	98.29 358

v8		97.95 230	97.63 247	98.93 203	98.95 318	98.81 184	99.80 24	99.41 217	96.03 342	99.10 224	99.42 269	94.92 210	99.30 302	96.94 299	94.08 361	98.66 307

MVStest1		96.08 336	95.48 341	97.89 322	98.93 319	96.70 312	99.56 126	99.35 250	92.69 385	91.81 399	99.46 262	89.90 343	98.96 360	95.00 351	92.61 379	98.00 377

TESTMET0.1,1		97.55 288	97.27 298	98.40 281	98.93 319	96.53 321	98.67 370	97.61 394	96.96 273	98.64 298	99.28 310	88.63 360	99.45 270	97.30 276	99.38 153	99.21 228

MVS_0304		99.15 87	98.96 107	99.73 64	98.92 321	99.37 102	99.37 230	96.92 398	99.51 2	99.66 86	99.78 124	96.69 146	99.97 21	99.84 15	99.97 7	99.84 39

UniMVSNet_NR-MVSNet		98.22 189	97.97 207	98.96 197	98.92 321	98.98 153	99.48 181	99.53 97	97.76 188	98.71 282	99.46 262	96.43 159	99.22 316	98.57 159	92.87 376	98.69 287

v2v482		98.06 208	97.77 229	98.92 205	98.90 323	98.82 182	99.57 120	99.36 243	96.65 293	99.19 208	99.35 292	94.20 248	99.25 309	97.72 241	94.97 344	98.69 287

1314		98.68 158	98.54 162	99.11 180	98.89 324	98.65 195	99.27 266	99.49 146	96.89 279	97.99 339	99.56 225	97.72 113	99.83 171	97.74 238	99.27 164	98.84 261

OPM-MVS		98.19 193	98.10 191	98.45 273	98.88 325	97.07 289	99.28 261	99.38 234	98.57 89	99.22 199	99.81 92	92.12 306	99.66 242	98.08 206	97.54 271	98.61 329
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v1192		97.81 254	97.44 272	98.91 209	98.88 325	98.68 192	99.51 159	99.34 255	96.18 330	99.20 205	99.34 296	94.03 255	99.36 291	95.32 345	95.18 339	98.69 287

EPMVS		97.82 252	97.65 243	98.35 285	98.88 325	95.98 338	99.49 177	94.71 411	97.57 209	99.26 192	99.48 255	92.46 301	99.71 225	97.87 222	99.08 181	99.35 213

v1144		97.98 225	97.69 239	98.85 226	98.87 328	98.66 194	99.54 143	99.35 250	96.27 323	99.23 198	99.35 292	94.67 229	99.23 312	96.73 308	95.16 340	98.68 292

DU-MVS		98.08 206	97.79 224	98.96 197	98.87 328	98.98 153	99.41 211	99.45 198	97.87 172	98.71 282	99.50 247	94.82 214	99.22 316	98.57 159	92.87 376	98.68 292

NR-MVSNet		97.97 228	97.61 249	99.02 189	98.87 328	99.26 118	99.47 187	99.42 214	97.63 203	97.08 364	99.50 247	95.07 206	99.13 330	97.86 223	93.59 367	98.68 292

WR-MVS		98.06 208	97.73 236	99.06 184	98.86 331	99.25 120	99.19 289	99.35 250	97.30 241	98.66 291	99.43 267	93.94 258	99.21 321	98.58 156	94.28 356	98.71 278

v1240		97.69 275	97.32 290	98.79 235	98.85 332	98.43 221	99.48 181	99.36 243	96.11 337	99.27 188	99.36 289	93.76 267	99.24 311	94.46 357	95.23 338	98.70 283

test_0402		96.64 323	96.24 325	97.85 324	98.85 332	96.43 325	99.44 197	99.26 289	93.52 376	96.98 366	99.52 240	88.52 361	99.20 323	92.58 381	97.50 276	97.93 382

v144192		97.92 234	97.60 250	98.87 220	98.83 334	98.65 195	99.55 139	99.34 255	96.20 328	99.32 175	99.40 277	94.36 243	99.26 308	96.37 322	95.03 343	98.70 283

v1921920		97.80 256	97.45 267	98.84 227	98.80 335	98.53 207	99.52 152	99.34 255	96.15 334	99.24 194	99.47 258	93.98 257	99.29 303	95.40 343	95.13 341	98.69 287

gg-mvs-nofinetune		96.17 333	95.32 345	98.73 239	98.79 336	98.14 234	99.38 228	94.09 412	91.07 393	98.07 337	91.04 410	89.62 348	99.35 294	96.75 307	99.09 180	98.68 292

test-LLR		98.06 208	97.90 215	98.55 259	98.79 336	97.10 285	98.67 370	97.75 391	97.34 237	98.61 303	98.85 356	94.45 241	99.45 270	97.25 278	99.38 153	99.10 233

test-mter		97.49 298	97.13 303	98.55 259	98.79 336	97.10 285	98.67 370	97.75 391	96.65 293	98.61 303	98.85 356	88.23 364	99.45 270	97.25 278	99.38 153	99.10 233

kuosan		90.92 368	90.11 373	93.34 376	98.78 339	85.59 401	98.15 398	93.16 416	89.37 397	92.07 397	98.38 375	81.48 399	95.19 406	62.54 415	97.04 297	99.25 225

WB-MVSnew		97.65 282	97.65 243	97.63 336	98.78 339	97.62 265	99.13 299	98.33 380	97.36 236	99.07 229	98.94 350	95.64 188	99.15 326	92.95 375	98.68 208	96.12 402

PS-MVSNAJss		98.92 127	98.92 111	98.90 211	98.78 339	98.53 207	99.78 31	99.54 86	98.07 151	99.00 244	99.76 136	99.01 18	99.37 287	99.13 78	97.23 292	98.81 262

MVS		97.28 306	96.55 318	99.48 121	98.78 339	98.95 163	99.27 266	99.39 226	83.53 404	98.08 334	99.54 233	96.97 137	99.87 142	94.23 361	99.16 170	99.63 140

TranMVSNet+NR-MVSNet		97.93 231	97.66 242	98.76 238	98.78 339	98.62 199	99.65 79	99.49 146	97.76 188	98.49 312	99.60 211	94.23 247	98.97 358	98.00 213	92.90 374	98.70 283

m2depth		97.80 256	97.63 247	98.29 291	98.77 344	97.38 272	99.64 82	99.36 243	98.78 73	96.30 374	99.58 217	92.34 305	99.39 282	98.36 182	95.58 330	98.10 368

PEN-MVS		97.76 260	97.44 272	98.72 240	98.77 344	98.54 206	99.78 31	99.51 116	97.06 265	98.29 324	99.64 193	92.63 293	98.89 366	98.09 202	93.16 372	98.72 276

v7n		97.87 240	97.52 256	98.92 205	98.76 346	98.58 203	99.84 11	99.46 187	96.20 328	98.91 256	99.70 159	94.89 212	99.44 275	96.03 326	93.89 364	98.75 271

v148		97.79 258	97.55 252	98.50 262	98.74 347	97.72 259	99.54 143	99.33 262	96.26 324	98.90 258	99.51 244	94.68 228	99.14 327	97.83 227	93.15 373	98.63 318

JIA-IIPM		97.50 293	97.02 307	98.93 203	98.73 348	97.80 255	99.30 251	98.97 329	91.73 389	98.91 256	94.86 404	95.10 205	99.71 225	97.58 251	97.98 247	99.28 221

Gipuma		90.99 367	90.15 372	93.51 375	98.73 348	90.12 395	93.98 408	99.45 198	79.32 406	92.28 396	94.91 403	69.61 404	97.98 387	87.42 399	95.67 328	92.45 406
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EU-MVSNet		97.98 225	98.03 201	97.81 330	98.72 350	96.65 317	99.66 73	99.66 28	98.09 146	98.35 319	99.82 78	95.25 202	98.01 386	97.41 270	95.30 337	98.78 264

K. test v3		97.10 314	96.79 314	98.01 312	98.72 350	96.33 328	99.87 7	97.05 397	97.59 206	96.16 376	99.80 105	88.71 355	99.04 342	96.69 311	96.55 305	98.65 309

OurMVSNet-221017-0		97.88 238	97.77 229	98.19 299	98.71 352	96.53 321	99.88 3	99.00 326	97.79 184	98.78 276	99.94 6	91.68 317	99.35 294	97.21 280	96.99 299	98.69 287

test_djsdf		98.67 159	98.57 159	98.98 194	98.70 353	98.91 170	99.88 3	99.46 187	97.55 212	99.22 199	99.88 36	95.73 184	99.28 304	99.03 87	97.62 264	98.75 271

pmmvs6		96.53 325	96.09 330	97.82 329	98.69 354	95.47 350	99.37 230	99.47 178	93.46 378	97.41 353	99.78 124	87.06 374	99.33 297	96.92 302	92.70 378	98.65 309

lessismore_v0				97.79 331	98.69 354	95.44 352		94.75 410		95.71 380	99.87 45	88.69 356	99.32 299	95.89 329	94.93 346	98.62 320

mvs_tets		98.40 177	98.23 180	98.91 209	98.67 356	98.51 213	99.66 73	99.53 97	98.19 131	98.65 297	99.81 92	92.75 284	99.44 275	99.31 60	97.48 280	98.77 267

SixPastTwentyTwo		97.50 293	97.33 289	98.03 309	98.65 357	96.23 333	99.77 33	98.68 371	97.14 254	97.90 342	99.93 9	90.45 335	99.18 324	97.00 293	96.43 307	98.67 299

UnsupCasMVSNet_eth		96.44 327	96.12 328	97.40 344	98.65 357	95.65 343	99.36 235	99.51 116	97.13 255	96.04 378	98.99 344	88.40 362	98.17 382	96.71 309	90.27 390	98.40 352

DTE-MVSNet		97.51 292	97.19 300	98.46 271	98.63 359	98.13 235	99.84 11	99.48 158	96.68 290	97.97 341	99.67 181	92.92 280	98.56 375	96.88 304	92.60 380	98.70 283

our_test_3		97.65 282	97.68 240	97.55 340	98.62 360	94.97 361	98.84 355	99.30 280	96.83 284	98.19 330	99.34 296	97.01 136	99.02 346	95.00 351	96.01 316	98.64 311

ppachtmachnet_test		97.49 298	97.45 267	97.61 338	98.62 360	95.24 355	98.80 359	99.46 187	96.11 337	98.22 328	99.62 204	96.45 157	98.97 358	93.77 365	95.97 321	98.61 329

pmmvs4		98.13 200	97.90 215	98.81 232	98.61 362	98.87 173	98.99 333	99.21 299	96.44 313	99.06 234	99.58 217	95.90 178	99.11 335	97.18 286	96.11 314	98.46 346

jajsoiax		98.43 171	98.28 178	98.88 216	98.60 363	98.43 221	99.82 15	99.53 97	98.19 131	98.63 300	99.80 105	93.22 275	99.44 275	99.22 70	97.50 276	98.77 267

cascas		97.69 275	97.43 276	98.48 265	98.60 363	97.30 274	98.18 397	99.39 226	92.96 382	98.41 315	98.78 363	93.77 266	99.27 307	98.16 199	98.61 209	98.86 259

MonoMVSNet		98.38 178	98.47 166	98.12 306	98.59 365	96.19 335	99.72 51	98.79 357	97.89 170	99.44 144	99.52 240	96.13 166	98.90 365	98.64 144	97.54 271	99.28 221

pmmvs5		97.52 290	97.30 292	98.16 301	98.57 366	96.73 311	99.27 266	98.90 342	96.14 335	98.37 318	99.53 237	91.54 323	99.14 327	97.51 260	95.87 322	98.63 318

GG-mvs-BLEND				98.45 273	98.55 367	98.16 232	99.43 201	93.68 413		97.23 359	98.46 371	89.30 349	99.22 316	95.43 342	98.22 234	97.98 379

gm-plane-assit					98.54 368	92.96 385			94.65 366		99.15 327		99.64 250	97.56 256

anonymousdsp		98.44 170	98.28 178	98.94 201	98.50 369	98.96 160	99.77 33	99.50 136	97.07 263	98.87 264	99.77 132	94.76 222	99.28 304	98.66 142	97.60 265	98.57 335

N_pmnet		94.95 350	95.83 336	92.31 380	98.47 370	79.33 412	99.12 302	92.81 418	93.87 372	97.68 349	99.13 329	93.87 262	99.01 348	91.38 385	96.19 312	98.59 333

MS-PatchMatch		97.24 310	97.32 290	96.99 353	98.45 371	93.51 382	98.82 357	99.32 272	97.41 232	98.13 333	99.30 306	88.99 351	99.56 261	95.68 336	99.80 100	97.90 384

test_fmvsmconf0.01_n		99.22 78	99.03 89	99.79 49	98.42 372	99.48 91	99.55 139	99.51 116	99.39 10	99.78 50	99.93 9	94.80 216	99.95 59	99.93 10	99.95 17	99.94 11

test0.0.03 1		97.71 273	97.42 277	98.56 257	98.41 373	97.82 254	98.78 361	98.63 373	97.34 237	98.05 338	98.98 346	94.45 241	98.98 351	95.04 350	97.15 296	98.89 258

EPNet_dtu		98.03 216	97.96 208	98.23 297	98.27 374	95.54 348	99.23 281	98.75 359	99.02 38	97.82 346	99.71 155	96.11 167	99.48 266	93.04 374	99.65 133	99.69 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MDA-MVSNet-bldmvs		94.96 349	93.98 356	97.92 319	98.24 375	97.27 276	99.15 296	99.33 262	93.80 373	80.09 411	99.03 339	88.31 363	97.86 390	93.49 369	94.36 355	98.62 320

MDA-MVSNet_test_wron		95.45 343	94.60 350	98.01 312	98.16 376	97.21 281	99.11 308	99.24 293	93.49 377	80.73 410	98.98 346	93.02 277	98.18 381	94.22 362	94.45 353	98.64 311

new_pmnet		96.38 329	96.03 331	97.41 343	98.13 377	95.16 359	99.05 317	99.20 300	93.94 371	97.39 356	98.79 362	91.61 322	99.04 342	90.43 388	95.77 324	98.05 372

EGC-MVSNET		82.80 375	77.86 381	97.62 337	97.91 378	96.12 336	99.33 245	99.28 286	8.40 418	25.05 419	99.27 313	84.11 388	99.33 297	89.20 392	98.22 234	97.42 392

YYNet1		95.36 345	94.51 352	97.92 319	97.89 379	97.10 285	99.10 310	99.23 294	93.26 380	80.77 409	99.04 338	92.81 283	98.02 385	94.30 358	94.18 358	98.64 311

DSMNet-mixed		97.25 308	97.35 284	96.95 356	97.84 380	93.61 381	99.57 120	96.63 403	96.13 336	98.87 264	98.61 369	94.59 232	97.70 393	95.08 349	98.86 197	99.55 161

testf1		90.42 369	90.68 370	89.65 389	97.78 381	73.97 417	99.13 299	98.81 354	89.62 395	91.80 400	98.93 351	62.23 409	98.80 369	86.61 403	91.17 384	96.19 400

APD_test2		90.42 369	90.68 370	89.65 389	97.78 381	73.97 417	99.13 299	98.81 354	89.62 395	91.80 400	98.93 351	62.23 409	98.80 369	86.61 403	91.17 384	96.19 400

EG-PatchMatch MVS		95.97 337	95.69 338	96.81 360	97.78 381	92.79 386	99.16 293	98.93 333	96.16 332	94.08 389	99.22 319	82.72 393	99.47 267	95.67 337	97.50 276	98.17 364

Anonymous20240521		96.20 332	95.89 335	97.13 350	97.72 384	94.96 362	99.79 30	99.29 284	93.01 381	97.20 361	99.03 339	89.69 346	98.36 379	91.16 386	96.13 313	98.07 370

MVP-Stereo		97.81 254	97.75 234	97.99 315	97.53 385	96.60 320	98.96 340	98.85 349	97.22 249	97.23 359	99.36 289	95.28 198	99.46 269	95.51 339	99.78 107	97.92 383
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test20.03		96.12 334	95.96 333	96.63 362	97.44 386	95.45 351	99.51 159	99.38 234	96.55 304	96.16 376	99.25 316	93.76 267	96.17 403	87.35 400	94.22 357	98.27 359

UnsupCasMVSNet_bld		93.53 359	92.51 365	96.58 364	97.38 387	93.82 375	98.24 394	99.48 158	91.10 392	93.10 393	96.66 399	74.89 403	98.37 378	94.03 364	87.71 396	97.56 390

MIMVSNet1		95.51 342	95.04 347	96.92 358	97.38 387	95.60 344	99.52 152	99.50 136	93.65 375	96.97 367	99.17 324	85.28 383	96.56 402	88.36 396	95.55 332	98.60 332

OpenMVS_ROB	92.34 20	94.38 355	93.70 361	96.41 365	97.38 387	93.17 384	99.06 315	98.75 359	86.58 401	94.84 387	98.26 380	81.53 398	99.32 299	89.01 393	97.87 253	96.76 395

Anonymous20231206		96.22 330	96.03 331	96.79 361	97.31 390	94.14 373	99.63 87	99.08 314	96.17 331	97.04 365	99.06 336	93.94 258	97.76 392	86.96 401	95.06 342	98.47 343

CMPMVS	69.68 23	94.13 356	94.90 348	91.84 381	97.24 391	80.01 411	98.52 382	99.48 158	89.01 398	91.99 398	99.67 181	85.67 378	99.13 330	95.44 341	97.03 298	96.39 399
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EPNet		98.86 134	98.71 138	99.30 154	97.20 392	98.18 231	99.62 92	98.91 340	99.28 16	98.63 300	99.81 92	95.96 172	99.99 4	99.24 69	99.72 121	99.73 97
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
KD-MVS_2432*1600		94.62 351	93.72 359	97.31 345	97.19 393	95.82 341	98.34 389	99.20 300	95.00 358	97.57 350	98.35 376	87.95 367	98.10 383	92.87 377	77.00 408	98.01 374

miper_refine_blended		94.62 351	93.72 359	97.31 345	97.19 393	95.82 341	98.34 389	99.20 300	95.00 358	97.57 350	98.35 376	87.95 367	98.10 383	92.87 377	77.00 408	98.01 374

KD-MVS_self_test		95.00 348	94.34 353	96.96 355	97.07 395	95.39 353	99.56 126	99.44 206	95.11 354	97.13 363	97.32 396	91.86 312	97.27 397	90.35 389	81.23 405	98.23 363

mvs5depth		96.66 322	96.22 326	97.97 316	97.00 396	96.28 330	98.66 373	99.03 323	96.61 298	96.93 368	99.79 117	87.20 373	99.47 267	96.65 315	94.13 359	98.16 365

test_fmvs3		92.10 364	91.77 367	93.08 378	96.19 397	86.25 398	99.82 15	98.62 374	96.65 293	95.19 384	96.90 398	55.05 413	95.93 405	96.63 316	90.92 388	97.06 394

CL-MVSNet_self_test		94.49 353	93.97 357	96.08 367	96.16 398	93.67 380	98.33 391	99.38 234	95.13 352	97.33 357	98.15 383	92.69 291	96.57 401	88.67 394	79.87 406	97.99 378

test_method		91.10 366	91.36 368	90.31 386	95.85 399	73.72 419	94.89 407	99.25 291	68.39 410	95.82 379	99.02 341	80.50 400	98.95 361	93.64 367	94.89 348	98.25 361

mvsany_test3		93.77 358	93.45 362	94.74 371	95.78 400	88.01 397	99.64 82	98.25 382	98.28 117	94.31 388	97.97 390	68.89 405	98.51 377	97.50 261	90.37 389	97.71 385

Patchmatch-RL test		95.84 339	95.81 337	95.95 368	95.61 401	90.57 394	98.24 394	98.39 379	95.10 356	95.20 383	98.67 366	94.78 218	97.77 391	96.28 323	90.02 391	99.51 177

PM-MVS		92.96 362	92.23 366	95.14 370	95.61 401	89.98 396	99.37 230	98.21 384	94.80 363	95.04 386	97.69 391	65.06 406	97.90 389	94.30 358	89.98 392	97.54 391

pmmvs-eth3d		95.34 346	94.73 349	97.15 348	95.53 403	95.94 339	99.35 240	99.10 311	95.13 352	93.55 391	97.54 392	88.15 366	97.91 388	94.58 355	89.69 393	97.61 388

test_f		91.90 365	91.26 369	93.84 374	95.52 404	85.92 399	99.69 59	98.53 378	95.31 351	93.87 390	96.37 401	55.33 412	98.27 380	95.70 334	90.98 387	97.32 393

WB-MVS		93.10 361	94.10 354	90.12 387	95.51 405	81.88 407	99.73 49	99.27 288	95.05 357	93.09 394	98.91 355	94.70 227	91.89 411	76.62 409	94.02 363	96.58 397

new-patchmatchnet		94.48 354	94.08 355	95.67 369	95.08 406	92.41 388	99.18 291	99.28 286	94.55 368	93.49 392	97.37 395	87.86 369	97.01 399	91.57 384	88.36 394	97.61 388

SSC-MVS		92.73 363	93.73 358	89.72 388	95.02 407	81.38 408	99.76 36	99.23 294	94.87 361	92.80 395	98.93 351	94.71 226	91.37 412	74.49 411	93.80 365	96.42 398

pmmvs3		94.09 357	93.25 363	96.60 363	94.76 408	94.49 368	98.92 347	98.18 386	89.66 394	96.48 372	98.06 389	86.28 375	97.33 396	89.68 391	87.20 397	97.97 380

test_vis3_rt		87.04 371	85.81 374	90.73 385	93.99 409	81.96 406	99.76 36	90.23 420	92.81 384	81.35 408	91.56 408	40.06 417	99.07 339	94.27 360	88.23 395	91.15 408

ambc				93.06 379	92.68 410	82.36 404	98.47 384	98.73 368		95.09 385	97.41 393	55.55 411	99.10 337	96.42 320	91.32 383	97.71 385

EMVS		80.02 378	79.22 380	82.43 396	91.19 411	76.40 414	97.55 405	92.49 419	66.36 413	83.01 407	91.27 409	64.63 407	85.79 415	65.82 414	60.65 412	85.08 411

E-PMN		80.61 377	79.88 379	82.81 394	90.75 412	76.38 415	97.69 402	95.76 407	66.44 412	83.52 405	92.25 407	62.54 408	87.16 414	68.53 413	61.40 411	84.89 412

PMMVS2		86.87 372	85.37 376	91.35 384	90.21 413	83.80 403	98.89 350	97.45 396	83.13 405	91.67 402	95.03 402	48.49 415	94.70 408	85.86 405	77.62 407	95.54 403

TDRefinement		95.42 344	94.57 351	97.97 316	89.83 414	96.11 337	99.48 181	98.75 359	96.74 286	96.68 370	99.88 36	88.65 358	99.71 225	98.37 180	82.74 403	98.09 369

LCM-MVSNet		86.80 373	85.22 377	91.53 383	87.81 415	80.96 409	98.23 396	98.99 327	71.05 408	90.13 403	96.51 400	48.45 416	96.88 400	90.51 387	85.30 399	96.76 395

FPMVS		84.93 374	85.65 375	82.75 395	86.77 416	63.39 421	98.35 388	98.92 336	74.11 407	83.39 406	98.98 346	50.85 414	92.40 410	84.54 406	94.97 344	92.46 405

wuyk23d		40.18 382	41.29 387	36.84 398	86.18 417	49.12 423	79.73 411	22.81 423	27.64 415	25.46 418	28.45 418	21.98 421	48.89 417	55.80 416	23.56 417	12.51 415

MVE	76.82 21	76.91 380	74.31 384	84.70 392	85.38 418	76.05 416	96.88 406	93.17 415	67.39 411	71.28 413	89.01 412	21.66 423	87.69 413	71.74 412	72.29 410	90.35 409
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		77.30 379	74.86 383	84.62 393	75.88 419	77.61 413	97.63 404	93.15 417	88.81 399	64.27 414	89.29 411	36.51 418	83.93 416	75.89 410	52.31 413	92.33 407

PMVS	70.75 22	75.98 381	74.97 382	79.01 397	70.98 420	55.18 422	93.37 409	98.21 384	65.08 414	61.78 415	93.83 405	21.74 422	92.53 409	78.59 407	91.12 386	89.34 410
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		82.80 375	81.52 378	86.66 391	66.61 421	68.44 420	92.79 410	97.92 388	68.96 409	80.04 412	99.85 54	85.77 377	96.15 404	97.86 223	43.89 414	95.39 404

test123		39.01 384	42.50 386	28.53 399	39.17 422	20.91 424	98.75 364	19.17 424	19.83 417	38.57 416	66.67 414	33.16 419	15.42 418	37.50 418	29.66 416	49.26 413

testmvs		39.17 383	43.78 385	25.37 400	36.04 423	16.84 425	98.36 387	26.56 422	20.06 416	38.51 417	67.32 413	29.64 420	15.30 419	37.59 417	39.90 415	43.98 414

test_blank		0.13 388	0.17 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	1.57 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

eth-test2					0.00 424

eth-test					0.00 424

uanet_test		0.02 389	0.03 392	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.27 420	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

DCPMVS		0.02 389	0.03 392	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.27 420	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

cdsmvs_eth3d_5k		24.64 385	32.85 388	0.00 401	0.00 424	0.00 426	0.00 412	99.51 116	0.00 419	0.00 420	99.56 225	96.58 150	0.00 420	0.00 419	0.00 418	0.00 416

pcd_1.5k_mvsjas		8.27 387	11.03 390	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.27 420	99.01 18	0.00 420	0.00 419	0.00 418	0.00 416

sosnet-low-res		0.02 389	0.03 392	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.27 420	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

sosnet		0.02 389	0.03 392	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.27 420	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

uncertanet		0.02 389	0.03 392	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.27 420	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

Regformer		0.02 389	0.03 392	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.27 420	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

ab-mvs-re		8.30 386	11.06 389	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	99.58 217	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

uanet		0.02 389	0.03 392	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.27 420	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

WAC-MVS						97.16 282								95.47 340

PC_three_1452									98.18 134	99.84 32	99.70 159	99.31 3	98.52 376	98.30 189	99.80 100	99.81 61

test_241102_TWO								99.48 158	99.08 33	99.88 21	99.81 92	98.94 29	99.96 31	98.91 102	99.84 80	99.88 25

test_0728_THIRD									98.99 45	99.81 39	99.80 105	99.09 14	99.96 31	98.85 115	99.90 42	99.88 25

GSMVS																99.52 170

sam_mvs1												94.86 213				99.52 170

sam_mvs												94.72 225

MTGPA								99.47 178

test_post1							99.23 281				65.14 416	94.18 251	99.71 225	97.58 251

test_post											65.99 415	94.65 231	99.73 215

patchmatchnet-post											98.70 365	94.79 217	99.74 209

MTMP							99.54 143	98.88 345

test9_res														97.49 262	99.72 121	99.75 88

agg_prior2														97.21 280	99.73 120	99.75 88

test_prior4						99.56 76	98.99 333

test_prior2							98.96 340		98.34 112	99.01 240	99.52 240	98.68 64		97.96 215	99.74 118

旧先验2							98.96 340		96.70 289	99.47 136			99.94 69	98.19 195

新几何2							99.01 330

无先验							98.99 333	99.51 116	96.89 279				99.93 87	97.53 259		99.72 103

原ACMM2							98.95 343

testdata2													99.95 59	96.67 312

segment_acmp												98.96 24

testdata1							98.85 354		98.32 115

plane_prior5								99.47 178					99.69 236	97.78 231	97.63 262	98.67 299

plane_prior4											99.61 208

plane_prior3						97.00 297			98.69 80	99.11 221

plane_prior2							99.39 223		98.97 51

plane_prior						96.97 300	99.21 287		98.45 99						97.60 265

n2								0.00 425

nn								0.00 425

door-mid								98.05 387

test11								99.35 250

door								97.92 388

HQP5-MVS						96.83 307

BP-MVS														97.19 284

HQP4-MVS										98.66 291			99.64 250			98.64 311

HQP3-MVS								99.39 226							97.58 267

HQP2-MVS												92.47 298

MDTV_nov1_ep13_2view						95.18 358	99.35 240		96.84 282	99.58 115		95.19 204		97.82 228		99.46 194

ACMMP++_ref															97.19 294

ACMMP++															97.43 285

Test By Simon												98.75 55

High-resolution multi-view benchmark