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.

mvs5depth		99.30 30	99.59 9	98.44 228	99.65 64	95.35 286	99.82 3	99.94 2	99.83 4	99.42 91	99.94 2	98.13 98	99.96 12	99.63 31	99.96 27	100.00 1

test_fmvsmconf0.01_n		99.57 7	99.63 7	99.36 66	99.87 12	98.13 134	98.08 170	99.95 1	99.45 40	99.98 2	99.75 13	99.80 1	99.97 5	99.82 9	99.99 5	99.99 2

fmvsm_s_conf0.1_n_a		99.17 47	99.30 39	98.80 165	99.75 33	96.59 244	97.97 192	99.86 16	98.22 162	99.88 18	99.71 19	98.59 55	99.84 155	99.73 24	99.98 12	99.98 3

fmvsm_s_conf0.1_n_2		99.20 45	99.38 25	98.65 189	99.69 54	96.08 262	97.49 257	99.90 11	99.53 31	99.88 18	99.64 34	98.51 62	99.90 71	99.83 8	99.98 12	99.97 4

mmtdpeth		99.30 30	99.42 21	98.92 151	99.58 78	96.89 231	99.48 10	99.92 7	99.92 2	98.26 261	99.80 9	98.33 77	99.91 64	99.56 36	99.95 35	99.97 4

fmvsm_s_conf0.1_n		99.16 50	99.33 32	98.64 191	99.71 45	96.10 257	97.87 204	99.85 18	98.56 139	99.90 13	99.68 22	98.69 46	99.85 137	99.72 26	99.98 12	99.97 4

test_fmvs3		99.12 58	99.41 22	98.25 247	99.76 29	95.07 298	99.05 64	99.94 2	97.78 198	99.82 28	99.84 3	98.56 59	99.71 264	99.96 1	99.96 27	99.97 4

test_fmvsmconf0.1_n		99.49 12	99.54 11	99.34 75	99.78 23	98.11 135	97.77 217	99.90 11	99.33 55	99.97 3	99.66 29	99.71 3	99.96 12	99.79 16	99.99 5	99.96 8

test_f		98.67 127	98.87 84	98.05 265	99.72 42	95.59 274	98.51 123	99.81 28	96.30 305	99.78 34	99.82 5	96.14 222	98.63 417	99.82 9	99.93 48	99.95 9

test_fmvs2		98.70 116	98.97 76	97.89 272	99.54 100	94.05 326	98.55 114	99.92 7	96.78 283	99.72 40	99.78 10	96.60 204	99.67 284	99.91 2	99.90 74	99.94 10

PS-MVSNAJss		99.46 14	99.49 13	99.35 72	99.90 4	98.15 131	99.20 45	99.65 56	99.48 34	99.92 8	99.71 19	98.07 101	99.96 12	99.53 39	100.00 1	99.93 11

test_vis3_rt		99.14 52	99.17 52	99.07 123	99.78 23	98.38 111	98.92 79	99.94 2	97.80 196	99.91 12	99.67 27	97.15 171	98.91 410	99.76 20	99.56 225	99.92 12

fmvsm_s_conf0.5_n_2		99.14 52	99.31 36	98.63 195	99.49 117	96.08 262	97.38 265	99.81 28	99.48 34	99.84 25	99.57 46	98.46 66	99.89 84	99.82 9	99.97 20	99.91 13

MVStest1		95.86 326	95.60 322	96.63 350	95.87 428	91.70 377	97.93 193	98.94 266	98.03 177	99.56 61	99.66 29	71.83 415	98.26 421	99.35 48	99.24 283	99.91 13

fmvsm_s_conf0.5_n_a		99.10 60	99.20 50	98.78 171	99.55 95	96.59 244	97.79 214	99.82 27	98.21 163	99.81 31	99.53 60	98.46 66	99.84 155	99.70 28	99.97 20	99.90 15

fmvsm_s_conf0.5_n		99.09 61	99.26 44	98.61 200	99.55 95	96.09 260	97.74 223	99.81 28	98.55 140	99.85 22	99.55 54	98.60 54	99.84 155	99.69 30	99.98 12	99.89 16

test_fmvsmconf_n		99.44 16	99.48 15	99.31 86	99.64 70	98.10 137	97.68 229	99.84 21	99.29 61	99.92 8	99.57 46	99.60 5	99.96 12	99.74 23	99.98 12	99.89 16

test_djsdf		99.52 10	99.51 12	99.53 37	99.86 14	98.74 84	99.39 17	99.56 80	99.11 81	99.70 44	99.73 17	99.00 24	99.97 5	99.26 54	99.98 12	99.89 16

mvs_tets		99.63 5	99.67 5	99.49 51	99.88 9	98.61 94	99.34 20	99.71 42	99.27 63	99.90 13	99.74 15	99.68 4	99.97 5	99.55 38	99.99 5	99.88 19

fmvsm_s_conf0.5_n_7		98.83 94	99.04 69	98.20 251	99.30 167	94.83 302	97.23 278	99.36 153	98.64 124	99.84 25	99.43 80	98.10 100	99.91 64	99.56 36	99.96 27	99.87 20

fmvsm_l_conf0.5_n_3		99.45 15	99.48 15	99.34 75	99.59 77	98.21 128	97.82 209	99.84 21	99.41 47	99.92 8	99.41 85	99.51 8	99.95 24	99.84 7	99.97 20	99.87 20

ttmdpeth		97.91 211	98.02 200	97.58 300	98.69 299	94.10 325	98.13 162	98.90 275	97.95 183	97.32 331	99.58 44	95.95 237	98.75 415	96.41 257	99.22 287	99.87 20

jajsoiax		99.58 6	99.61 8	99.48 53	99.87 12	98.61 94	99.28 37	99.66 55	99.09 91	99.89 16	99.68 22	99.53 7	99.97 5	99.50 42	99.99 5	99.87 20

EU-MVSNet		97.66 236	98.50 135	95.13 387	99.63 74	85.84 418	98.35 142	98.21 334	98.23 161	99.54 65	99.46 73	95.02 263	99.68 281	98.24 120	99.87 85	99.87 20

fmvsm_s_conf0.5_n_3		99.22 42	99.37 27	98.78 171	99.46 129	96.58 246	97.65 235	99.72 40	99.47 37	99.86 20	99.50 64	98.94 27	99.89 84	99.75 22	99.97 20	99.86 25

UA-Net		99.47 13	99.40 23	99.70 2	99.49 117	99.29 23	99.80 4	99.72 40	99.82 5	99.04 155	99.81 6	98.05 104	99.96 12	98.85 83	99.99 5	99.86 25

MM		98.22 189	97.99 203	98.91 152	98.66 309	96.97 224	97.89 200	94.44 405	99.54 30	98.95 170	99.14 148	93.50 299	99.92 55	99.80 14	99.96 27	99.85 27

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 13	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 13	100.00 1	99.85 27

fmvsm_l_conf0.5_n_a		99.19 46	99.27 42	98.94 146	99.65 64	97.05 220	97.80 213	99.76 35	98.70 123	99.78 34	99.11 151	98.79 39	99.95 24	99.85 5	99.96 27	99.83 29

fmvsm_l_conf0.5_n		99.21 43	99.28 41	99.02 135	99.64 70	97.28 205	97.82 209	99.76 35	98.73 120	99.82 28	99.09 158	98.81 35	99.95 24	99.86 4	99.96 27	99.83 29

mvsany_test3		98.87 89	98.92 79	98.74 182	99.38 146	96.94 228	98.58 111	99.10 242	96.49 295	99.96 4	99.81 6	98.18 91	99.45 366	98.97 75	99.79 124	99.83 29

SSC-MVS		98.71 112	98.74 97	98.62 197	99.72 42	96.08 262	98.74 92	98.64 315	99.74 10	99.67 50	99.24 122	94.57 277	99.95 24	99.11 64	99.24 283	99.82 32

anonymousdsp		99.51 11	99.47 18	99.62 9	99.88 9	99.08 67	99.34 20	99.69 46	98.93 109	99.65 54	99.72 18	98.93 29	99.95 24	99.11 64	100.00 1	99.82 32

ANet_high		99.57 7	99.67 5	99.28 88	99.89 6	98.09 138	99.14 54	99.93 5	99.82 5	99.93 6	99.81 6	99.17 19	99.94 39	99.31 50	100.00 1	99.82 32

fmvsm_s_conf0.5_n_4		99.01 70	99.22 47	98.38 234	99.31 163	95.48 281	97.56 248	99.73 39	98.87 113	99.75 38	99.27 112	98.80 37	99.86 124	99.80 14	99.90 74	99.81 35

PS-CasMVS		99.40 23	99.33 32	99.62 9	99.71 45	99.10 64	99.29 33	99.53 91	99.53 31	99.46 83	99.41 85	98.23 84	99.95 24	98.89 81	99.95 35	99.81 35

FC-MVSNet-test		99.27 34	99.25 45	99.34 75	99.77 26	98.37 113	99.30 32	99.57 73	99.61 26	99.40 96	99.50 64	97.12 172	99.85 137	99.02 72	99.94 43	99.80 37

test_cas_vis1_n_1920		98.33 175	98.68 110	97.27 322	99.69 54	92.29 371	98.03 178	99.85 18	97.62 207	99.96 4	99.62 37	93.98 292	99.74 251	99.52 41	99.86 89	99.79 38

test_vis1_n_1920		98.40 165	98.92 79	96.81 345	99.74 35	90.76 396	98.15 160	99.91 9	98.33 150	99.89 16	99.55 54	95.07 262	99.88 98	99.76 20	99.93 48	99.79 38

CP-MVSNet		99.21 43	99.09 64	99.56 25	99.65 64	98.96 74	99.13 55	99.34 165	99.42 45	99.33 108	99.26 117	97.01 180	99.94 39	98.74 92	99.93 48	99.79 38

fmvsm_s_conf0.5_n_5		99.07 67	99.10 62	98.99 138	99.47 127	97.22 210	97.40 263	99.83 24	97.61 210	99.85 22	99.30 106	98.80 37	99.95 24	99.71 27	99.90 74	99.78 41

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 19	99.69 5	99.58 66	99.90 3	99.86 20	99.78 10	99.58 6	99.95 24	99.00 73	99.95 35	99.78 41

CVMVSNet		96.25 315	97.21 258	93.38 408	99.10 215	80.56 436	97.20 283	98.19 337	96.94 274	99.00 160	99.02 171	89.50 343	99.80 203	96.36 261	99.59 213	99.78 41

reproduce_monomvs		95.00 348	95.25 337	94.22 396	97.51 396	83.34 428	97.86 205	98.44 324	98.51 141	99.29 117	99.30 106	67.68 423	99.56 332	98.89 81	99.81 108	99.77 44

Anonymous20231211		99.27 34	99.27 42	99.26 93	99.29 170	98.18 129	99.49 9	99.51 95	99.70 12	99.80 32	99.68 22	96.84 187	99.83 172	99.21 59	99.91 68	99.77 44

PEN-MVS		99.41 22	99.34 31	99.62 9	99.73 36	99.14 56	99.29 33	99.54 88	99.62 24	99.56 61	99.42 81	98.16 95	99.96 12	98.78 87	99.93 48	99.77 44

WR-MVS_H		99.33 28	99.22 47	99.65 8	99.71 45	99.24 29	99.32 23	99.55 84	99.46 39	99.50 77	99.34 98	97.30 161	99.93 46	98.90 79	99.93 48	99.77 44

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 25	99.85 16	99.11 63	99.90 1	99.78 33	99.63 21	99.78 34	99.67 27	99.48 10	99.81 196	99.30 51	99.97 20	99.77 44
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
WB-MVS		98.52 154	98.55 128	98.43 229	99.65 64	95.59 274	98.52 118	98.77 301	99.65 18	99.52 71	99.00 183	94.34 283	99.93 46	98.65 99	98.83 331	99.76 49

patch_mono-2		98.51 155	98.63 117	98.17 254	99.38 146	94.78 304	97.36 268	99.69 46	98.16 173	98.49 242	99.29 109	97.06 175	99.97 5	98.29 119	99.91 68	99.76 49

nrg030		99.40 23	99.35 29	99.54 30	99.58 78	99.13 59	98.98 72	99.48 106	99.68 15	99.46 83	99.26 117	98.62 52	99.73 256	99.17 62	99.92 59	99.76 49

FIs		99.14 52	99.09 64	99.29 87	99.70 52	98.28 119	99.13 55	99.52 94	99.48 34	99.24 129	99.41 85	96.79 193	99.82 182	98.69 97	99.88 82	99.76 49

v7n		99.53 9	99.57 10	99.41 62	99.88 9	98.54 102	99.45 11	99.61 62	99.66 17	99.68 48	99.66 29	98.44 68	99.95 24	99.73 24	99.96 27	99.75 53

APDe-MVS		98.99 73	98.79 93	99.60 14	99.21 187	99.15 51	98.87 84	99.48 106	97.57 214	99.35 105	99.24 122	97.83 118	99.89 84	97.88 146	99.70 174	99.75 53
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DTE-MVSNet		99.43 20	99.35 29	99.66 7	99.71 45	99.30 21	99.31 27	99.51 95	99.64 19	99.56 61	99.46 73	98.23 84	99.97 5	98.78 87	99.93 48	99.72 55

MSC_two_6792asdad				99.32 83	98.43 338	98.37 113		98.86 286					99.89 84	97.14 188	99.60 209	99.71 56

No_MVS				99.32 83	98.43 338	98.37 113		98.86 286					99.89 84	97.14 188	99.60 209	99.71 56

PMMVS2		98.07 202	98.08 195	98.04 266	99.41 143	94.59 313	94.59 402	99.40 141	97.50 222	98.82 198	98.83 221	96.83 189	99.84 155	97.50 170	99.81 108	99.71 56

Baseline_NR-MVSNet		98.98 76	98.86 87	99.36 66	99.82 19	98.55 99	97.47 260	99.57 73	99.37 50	99.21 132	99.61 40	96.76 196	99.83 172	98.06 133	99.83 101	99.71 56

XXY-MVS		99.14 52	99.15 59	99.10 117	99.76 29	97.74 179	98.85 87	99.62 59	98.48 143	99.37 101	99.49 70	98.75 41	99.86 124	98.20 123	99.80 119	99.71 56

test_0728_THIRD									98.17 170	99.08 146	99.02 171	97.89 115	99.88 98	97.07 194	99.71 167	99.70 61

MSP-MVS		98.40 165	98.00 202	99.61 12	99.57 83	99.25 28	98.57 112	99.35 159	97.55 218	99.31 116	97.71 339	94.61 276	99.88 98	96.14 274	99.19 294	99.70 61
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
SSC-MVS3.2		98.53 150	98.79 93	97.74 286	99.46 129	93.62 348	96.45 322	99.34 165	99.33 55	98.93 178	98.70 244	97.90 114	99.90 71	99.12 63	99.92 59	99.69 63

dcpmvs_2		98.78 103	99.11 60	97.78 279	99.56 91	93.67 345	99.06 62	99.86 16	99.50 33	99.66 51	99.26 117	97.21 169	99.99 2	98.00 138	99.91 68	99.68 64

test_0728_SECOND				99.60 14	99.50 110	99.23 30	98.02 180	99.32 173					99.88 98	96.99 200	99.63 199	99.68 64

OurMVSNet-221017-0		99.37 26	99.31 36	99.53 37	99.91 3	98.98 69	99.63 7	99.58 66	99.44 42	99.78 34	99.76 12	96.39 212	99.92 55	99.44 45	99.92 59	99.68 64

fmvsm_s_conf0.5_n_6		99.08 65	99.21 49	98.69 185	99.36 153	96.51 248	97.62 239	99.68 51	98.43 145	99.85 22	99.10 154	99.12 22	99.88 98	99.77 19	99.92 59	99.67 67

CHOSEN 1792x2688		97.49 248	97.14 263	98.54 215	99.68 57	96.09 260	96.50 320	99.62 59	91.58 396	98.84 194	98.97 190	92.36 317	99.88 98	96.76 223	99.95 35	99.67 67

reproduce_model		99.15 51	98.97 76	99.67 4	99.33 161	99.44 10	98.15 160	99.47 114	99.12 80	99.52 71	99.32 104	98.31 78	99.90 71	97.78 152	99.73 154	99.66 69

IU-MVS					99.49 117	99.15 51		98.87 281	92.97 381	99.41 93				96.76 223	99.62 202	99.66 69

test_241102_TWO								99.30 186	98.03 177	99.26 124	99.02 171	97.51 149	99.88 98	96.91 206	99.60 209	99.66 69

DPE-MVS		98.59 140	98.26 173	99.57 20	99.27 173	99.15 51	97.01 292	99.39 143	97.67 203	99.44 87	98.99 184	97.53 146	99.89 84	95.40 304	99.68 182	99.66 69
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TransMVSNet (Re)		99.44 16	99.47 18	99.36 66	99.80 20	98.58 97	99.27 39	99.57 73	99.39 48	99.75 38	99.62 37	99.17 19	99.83 172	99.06 68	99.62 202	99.66 69

EI-MVSNet-UG-set		98.69 119	98.71 104	98.62 197	99.10 215	96.37 251	97.23 278	98.87 281	99.20 70	99.19 134	98.99 184	97.30 161	99.85 137	98.77 90	99.79 124	99.65 74

pmmvs6		99.67 3	99.70 3	99.60 14	99.90 4	99.27 26	99.53 8	99.76 35	99.64 19	99.84 25	99.83 4	99.50 9	99.87 116	99.36 47	99.92 59	99.64 75

EI-MVSNet-Vis-set		98.68 124	98.70 107	98.63 195	99.09 218	96.40 250	97.23 278	98.86 286	99.20 70	99.18 138	98.97 190	97.29 163	99.85 137	98.72 94	99.78 129	99.64 75

ACMH	96.65 7	99.25 37	99.24 46	99.26 93	99.72 42	98.38 111	99.07 61	99.55 84	98.30 154	99.65 54	99.45 77	99.22 16	99.76 239	98.44 111	99.77 135	99.64 75
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DP-MVS		98.93 82	98.81 92	99.28 88	99.21 187	98.45 108	98.46 131	99.33 171	99.63 21	99.48 78	99.15 145	97.23 167	99.75 246	97.17 184	99.66 193	99.63 78

reproduce-ours		99.09 61	98.90 81	99.67 4	99.27 173	99.49 6	98.00 184	99.42 134	99.05 96	99.48 78	99.27 112	98.29 80	99.89 84	97.61 162	99.71 167	99.62 79

our_new_method		99.09 61	98.90 81	99.67 4	99.27 173	99.49 6	98.00 184	99.42 134	99.05 96	99.48 78	99.27 112	98.29 80	99.89 84	97.61 162	99.71 167	99.62 79

test_fmvs1_n		98.09 200	98.28 169	97.52 308	99.68 57	93.47 350	98.63 105	99.93 5	95.41 336	99.68 48	99.64 34	91.88 324	99.48 359	99.82 9	99.87 85	99.62 79

test1111		96.49 308	96.82 282	95.52 380	99.42 141	87.08 415	99.22 42	87.14 431	99.11 81	99.46 83	99.58 44	88.69 347	99.86 124	98.80 85	99.95 35	99.62 79

VPA-MVSNet		99.30 30	99.30 39	99.28 88	99.49 117	98.36 116	99.00 69	99.45 121	99.63 21	99.52 71	99.44 78	98.25 82	99.88 98	99.09 66	99.84 94	99.62 79

LPG-MVS_test		98.71 112	98.46 144	99.47 56	99.57 83	98.97 70	98.23 150	99.48 106	96.60 290	99.10 144	99.06 159	98.71 44	99.83 172	95.58 300	99.78 129	99.62 79

LGP-MVS_train				99.47 56	99.57 83	98.97 70		99.48 106	96.60 290	99.10 144	99.06 159	98.71 44	99.83 172	95.58 300	99.78 129	99.62 79

Test_1112_low_res		96.99 289	96.55 300	98.31 243	99.35 158	95.47 282	95.84 363	99.53 91	91.51 398	96.80 356	98.48 283	91.36 328	99.83 172	96.58 239	99.53 235	99.62 79

v10		98.97 77	99.11 60	98.55 212	99.44 135	96.21 256	98.90 80	99.55 84	98.73 120	99.48 78	99.60 42	96.63 203	99.83 172	99.70 28	99.99 5	99.61 87

test_vis1_n		98.31 178	98.50 135	97.73 289	99.76 29	94.17 323	98.68 102	99.91 9	96.31 303	99.79 33	99.57 46	92.85 311	99.42 371	99.79 16	99.84 94	99.60 88

v8		99.01 70	99.16 54	98.57 207	99.47 127	96.31 254	98.90 80	99.47 114	99.03 99	99.52 71	99.57 46	96.93 183	99.81 196	99.60 32	99.98 12	99.60 88

EI-MVSNet		98.40 165	98.51 133	98.04 266	99.10 215	94.73 307	97.20 283	98.87 281	98.97 105	99.06 148	99.02 171	96.00 229	99.80 203	98.58 102	99.82 104	99.60 88

SixPastTwentyTwo		98.75 108	98.62 119	99.16 108	99.83 18	97.96 158	99.28 37	98.20 335	99.37 50	99.70 44	99.65 33	92.65 315	99.93 46	99.04 70	99.84 94	99.60 88

IterMVS-LS		98.55 146	98.70 107	98.09 258	99.48 125	94.73 307	97.22 282	99.39 143	98.97 105	99.38 99	99.31 105	96.00 229	99.93 46	98.58 102	99.97 20	99.60 88
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		97.19 274	96.60 298	98.96 143	99.62 76	97.28 205	95.17 384	99.50 97	94.21 363	99.01 159	98.32 300	86.61 359	99.99 2	97.10 192	99.84 94	99.60 88

ACMMP_NAP		98.75 108	98.48 140	99.57 20	99.58 78	99.29 23	97.82 209	99.25 206	96.94 274	98.78 201	99.12 150	98.02 105	99.84 155	97.13 190	99.67 188	99.59 94

VPNet		98.87 89	98.83 89	99.01 136	99.70 52	97.62 188	98.43 134	99.35 159	99.47 37	99.28 118	99.05 166	96.72 199	99.82 182	98.09 130	99.36 263	99.59 94

WR-MVS		98.40 165	98.19 181	99.03 133	99.00 237	97.65 185	96.85 302	98.94 266	98.57 136	98.89 184	98.50 280	95.60 247	99.85 137	97.54 167	99.85 90	99.59 94

HPM-MVS		98.79 101	98.53 131	99.59 18	99.65 64	99.29 23	99.16 51	99.43 131	96.74 285	98.61 224	98.38 292	98.62 52	99.87 116	96.47 253	99.67 188	99.59 94
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EG-PatchMatch MVS		98.99 73	99.01 71	98.94 146	99.50 110	97.47 194	98.04 177	99.59 64	98.15 174	99.40 96	99.36 93	98.58 58	99.76 239	98.78 87	99.68 182	99.59 94

Vis-MVSNet		99.34 27	99.36 28	99.27 91	99.73 36	98.26 120	99.17 50	99.78 33	99.11 81	99.27 120	99.48 71	98.82 34	99.95 24	98.94 77	99.93 48	99.59 94
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MP-MVS-pluss		98.57 141	98.23 177	99.60 14	99.69 54	99.35 16	97.16 287	99.38 145	94.87 348	98.97 166	98.99 184	98.01 106	99.88 98	97.29 178	99.70 174	99.58 100
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		98.69 119	98.40 152	99.54 30	99.53 103	99.17 43	98.52 118	99.31 178	97.46 230	98.44 246	98.51 276	97.83 118	99.88 98	96.46 254	99.58 218	99.58 100

ACMMPR		98.70 116	98.42 150	99.54 30	99.52 105	99.14 56	98.52 118	99.31 178	97.47 225	98.56 233	98.54 271	97.75 126	99.88 98	96.57 241	99.59 213	99.58 100

PGM-MVS		98.66 128	98.37 158	99.55 27	99.53 103	99.18 42	98.23 150	99.49 104	97.01 271	98.69 212	98.88 212	98.00 107	99.89 84	95.87 286	99.59 213	99.58 100

SteuartSystems-ACMMP		98.79 101	98.54 130	99.54 30	99.73 36	99.16 47	98.23 150	99.31 178	97.92 187	98.90 182	98.90 205	98.00 107	99.88 98	96.15 273	99.72 162	99.58 100
Skip Steuart: Steuart Systems R&D Blog.
SDMVSNet		99.23 41	99.32 34	98.96 143	99.68 57	97.35 201	98.84 89	99.48 106	99.69 13	99.63 57	99.68 22	99.03 23	99.96 12	97.97 140	99.92 59	99.57 105

sd_testset		99.28 33	99.31 36	99.19 104	99.68 57	98.06 147	99.41 14	99.30 186	99.69 13	99.63 57	99.68 22	99.25 15	99.96 12	97.25 181	99.92 59	99.57 105

TranMVSNet+NR-MVSNet		99.17 47	99.07 67	99.46 58	99.37 152	98.87 77	98.39 138	99.42 134	99.42 45	99.36 103	99.06 159	98.38 71	99.95 24	98.34 116	99.90 74	99.57 105

mPP-MVS		98.64 131	98.34 162	99.54 30	99.54 100	99.17 43	98.63 105	99.24 211	97.47 225	98.09 275	98.68 248	97.62 137	99.89 84	96.22 268	99.62 202	99.57 105

PVSNet_Blended_VisFu		98.17 196	98.15 187	98.22 250	99.73 36	95.15 294	97.36 268	99.68 51	94.45 358	98.99 161	99.27 112	96.87 186	99.94 39	97.13 190	99.91 68	99.57 105

1112_ss		97.29 266	96.86 278	98.58 204	99.34 160	96.32 253	96.75 308	99.58 66	93.14 379	96.89 351	97.48 353	92.11 321	99.86 124	96.91 206	99.54 231	99.57 105

MTAPA		98.88 88	98.64 116	99.61 12	99.67 61	99.36 15	98.43 134	99.20 217	98.83 119	98.89 184	98.90 205	96.98 182	99.92 55	97.16 185	99.70 174	99.56 111

XVS		98.72 111	98.45 145	99.53 37	99.46 129	99.21 32	98.65 103	99.34 165	98.62 129	97.54 314	98.63 260	97.50 150	99.83 172	96.79 219	99.53 235	99.56 111

pm-mvs1		99.44 16	99.48 15	99.33 81	99.80 20	98.63 91	99.29 33	99.63 58	99.30 60	99.65 54	99.60 42	99.16 21	99.82 182	99.07 67	99.83 101	99.56 111

X-MVStestdata		94.32 355	92.59 374	99.53 37	99.46 129	99.21 32	98.65 103	99.34 165	98.62 129	97.54 314	45.85 433	97.50 150	99.83 172	96.79 219	99.53 235	99.56 111

HPM-MVS_fast		99.01 70	98.82 90	99.57 20	99.71 45	99.35 16	99.00 69	99.50 97	97.33 241	98.94 177	98.86 215	98.75 41	99.82 182	97.53 168	99.71 167	99.56 111

K. test v3		98.00 206	97.66 230	99.03 133	99.79 22	97.56 190	99.19 49	92.47 417	99.62 24	99.52 71	99.66 29	89.61 341	99.96 12	99.25 56	99.81 108	99.56 111

CP-MVS		98.70 116	98.42 150	99.52 42	99.36 153	99.12 61	98.72 97	99.36 153	97.54 219	98.30 255	98.40 289	97.86 117	99.89 84	96.53 250	99.72 162	99.56 111

ZNCC-MVS		98.68 124	98.40 152	99.54 30	99.57 83	99.21 32	98.46 131	99.29 194	97.28 247	98.11 273	98.39 290	98.00 107	99.87 116	96.86 216	99.64 196	99.55 118

v1192		98.60 138	98.66 113	98.41 231	99.27 173	95.88 268	97.52 253	99.36 153	97.41 234	99.33 108	99.20 130	96.37 215	99.82 182	99.57 34	99.92 59	99.55 118

v1240		98.55 146	98.62 119	98.32 241	99.22 185	95.58 276	97.51 255	99.45 121	97.16 262	99.45 86	99.24 122	96.12 224	99.85 137	99.60 32	99.88 82	99.55 118

UGNet		98.53 150	98.45 145	98.79 168	97.94 367	96.96 226	99.08 58	98.54 319	99.10 88	96.82 355	99.47 72	96.55 206	99.84 155	98.56 107	99.94 43	99.55 118
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
WBMVS		95.18 343	94.78 349	96.37 356	97.68 384	89.74 403	95.80 364	98.73 308	97.54 219	98.30 255	98.44 286	70.06 417	99.82 182	96.62 236	99.87 85	99.54 122

test2506		92.39 386	91.89 388	93.89 401	99.38 146	82.28 432	99.32 23	66.03 439	99.08 93	98.77 204	99.57 46	66.26 427	99.84 155	98.71 95	99.95 35	99.54 122

ECVR-MVS		96.42 310	96.61 296	95.85 372	99.38 146	88.18 410	99.22 42	86.00 433	99.08 93	99.36 103	99.57 46	88.47 352	99.82 182	98.52 108	99.95 35	99.54 122

v144192		98.54 148	98.57 127	98.45 226	99.21 187	95.98 265	97.63 238	99.36 153	97.15 264	99.32 114	99.18 135	95.84 241	99.84 155	99.50 42	99.91 68	99.54 122

v1921920		98.54 148	98.60 124	98.38 234	99.20 191	95.76 273	97.56 248	99.36 153	97.23 256	99.38 99	99.17 139	96.02 227	99.84 155	99.57 34	99.90 74	99.54 122

MP-MVS		98.46 159	98.09 192	99.54 30	99.57 83	99.22 31	98.50 125	99.19 221	97.61 210	97.58 310	98.66 253	97.40 157	99.88 98	94.72 319	99.60 209	99.54 122
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MIMVSNet1		99.38 25	99.32 34	99.55 27	99.86 14	99.19 41	99.41 14	99.59 64	99.59 27	99.71 42	99.57 46	97.12 172	99.90 71	99.21 59	99.87 85	99.54 122

ACMMP		98.75 108	98.50 135	99.52 42	99.56 91	99.16 47	98.87 84	99.37 149	97.16 262	98.82 198	99.01 180	97.71 128	99.87 116	96.29 265	99.69 177	99.54 122
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
SMA-MVS		98.40 165	98.03 199	99.51 46	99.16 204	99.21 32	98.05 175	99.22 214	94.16 364	98.98 162	99.10 154	97.52 148	99.79 216	96.45 255	99.64 196	99.53 130
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
HFP-MVS		98.71 112	98.44 147	99.51 46	99.49 117	99.16 47	98.52 118	99.31 178	97.47 225	98.58 230	98.50 280	97.97 111	99.85 137	96.57 241	99.59 213	99.53 130

UniMVSNet_NR-MVSNet		98.86 92	98.68 110	99.40 64	99.17 202	98.74 84	97.68 229	99.40 141	99.14 79	99.06 148	98.59 267	96.71 200	99.93 46	98.57 104	99.77 135	99.53 130

GST-MVS		98.61 137	98.30 167	99.52 42	99.51 107	99.20 38	98.26 148	99.25 206	97.44 233	98.67 215	98.39 290	97.68 129	99.85 137	96.00 278	99.51 240	99.52 133

MVS_0304		97.44 253	97.01 269	98.72 183	96.42 421	96.74 239	97.20 283	91.97 421	98.46 144	98.30 255	98.79 229	92.74 313	99.91 64	99.30 51	99.94 43	99.52 133

TDRefinement		99.42 21	99.38 25	99.55 27	99.76 29	99.33 20	99.68 6	99.71 42	99.38 49	99.53 69	99.61 40	98.64 49	99.80 203	98.24 120	99.84 94	99.52 133

v1144		98.60 138	98.66 113	98.41 231	99.36 153	95.90 267	97.58 246	99.34 165	97.51 221	99.27 120	99.15 145	96.34 217	99.80 203	99.47 44	99.93 48	99.51 136

v2v482		98.56 142	98.62 119	98.37 237	99.42 141	95.81 271	97.58 246	99.16 232	97.90 189	99.28 118	99.01 180	95.98 234	99.79 216	99.33 49	99.90 74	99.51 136

CPTT-MVS		97.84 225	97.36 249	99.27 91	99.31 163	98.46 107	98.29 145	99.27 200	94.90 347	97.83 294	98.37 293	94.90 265	99.84 155	93.85 347	99.54 231	99.51 136

DU-MVS		98.82 97	98.63 117	99.39 65	99.16 204	98.74 84	97.54 251	99.25 206	98.84 118	99.06 148	98.76 235	96.76 196	99.93 46	98.57 104	99.77 135	99.50 139

NR-MVSNet		98.95 80	98.82 90	99.36 66	99.16 204	98.72 89	99.22 42	99.20 217	99.10 88	99.72 40	98.76 235	96.38 214	99.86 124	98.00 138	99.82 104	99.50 139

casdiffmvs_mvg		99.12 58	99.16 54	98.99 138	99.43 140	97.73 181	98.00 184	99.62 59	99.22 66	99.55 64	99.22 127	98.93 29	99.75 246	98.66 98	99.81 108	99.50 139
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMH+	96.62 9	99.08 65	99.00 72	99.33 81	99.71 45	98.83 79	98.60 109	99.58 66	99.11 81	99.53 69	99.18 135	98.81 35	99.67 284	96.71 230	99.77 135	99.50 139

DVP-MVS++		98.90 86	98.70 107	99.51 46	98.43 338	99.15 51	99.43 12	99.32 173	98.17 170	99.26 124	99.02 171	98.18 91	99.88 98	97.07 194	99.45 252	99.49 143

PC_three_1452									93.27 377	99.40 96	98.54 271	98.22 87	97.00 428	95.17 307	99.45 252	99.49 143

GeoE		99.05 68	98.99 74	99.25 96	99.44 135	98.35 117	98.73 96	99.56 80	98.42 146	98.91 181	98.81 226	98.94 27	99.91 64	98.35 115	99.73 154	99.49 143

h-mvs33		97.77 228	97.33 252	99.10 117	99.21 187	97.84 167	98.35 142	98.57 318	99.11 81	98.58 230	99.02 171	88.65 350	99.96 12	98.11 128	96.34 409	99.49 143

IterMVS-SCA-FT		97.85 224	98.18 182	96.87 341	99.27 173	91.16 390	95.53 372	99.25 206	99.10 88	99.41 93	99.35 94	93.10 304	99.96 12	98.65 99	99.94 43	99.49 143

new-patchmatchnet		98.35 171	98.74 97	97.18 325	99.24 180	92.23 373	96.42 326	99.48 106	98.30 154	99.69 46	99.53 60	97.44 155	99.82 182	98.84 84	99.77 135	99.49 143

APD-MVS		98.10 198	97.67 227	99.42 60	99.11 213	98.93 75	97.76 220	99.28 197	94.97 345	98.72 210	98.77 233	97.04 176	99.85 137	93.79 348	99.54 231	99.49 143
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EPP-MVSNet		98.30 179	98.04 198	99.07 123	99.56 91	97.83 168	99.29 33	98.07 341	99.03 99	98.59 228	99.13 149	92.16 320	99.90 71	96.87 214	99.68 182	99.49 143

DeepC-MVS	97.60 4	98.97 77	98.93 78	99.10 117	99.35 158	97.98 154	98.01 183	99.46 117	97.56 216	99.54 65	99.50 64	98.97 25	99.84 155	98.06 133	99.92 59	99.49 143
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMM	96.08 12	98.91 84	98.73 99	99.48 53	99.55 95	99.14 56	98.07 172	99.37 149	97.62 207	99.04 155	98.96 193	98.84 33	99.79 216	97.43 172	99.65 194	99.49 143
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DVP-MVS		98.77 106	98.52 132	99.52 42	99.50 110	99.21 32	98.02 180	98.84 290	97.97 181	99.08 146	99.02 171	97.61 138	99.88 98	96.99 200	99.63 199	99.48 153
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
SR-MVS		98.71 112	98.43 148	99.57 20	99.18 201	99.35 16	98.36 141	99.29 194	98.29 157	98.88 187	98.85 218	97.53 146	99.87 116	96.14 274	99.31 271	99.48 153

TSAR-MVS + MP.		98.63 133	98.49 139	99.06 129	99.64 70	97.90 162	98.51 123	98.94 266	96.96 272	99.24 129	98.89 211	97.83 118	99.81 196	96.88 213	99.49 248	99.48 153
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
VDDNet		98.21 191	97.95 207	99.01 136	99.58 78	97.74 179	99.01 67	97.29 361	99.67 16	98.97 166	99.50 64	90.45 336	99.80 203	97.88 146	99.20 291	99.48 153

IterMVS		97.73 230	98.11 191	96.57 351	99.24 180	90.28 399	95.52 374	99.21 215	98.86 115	99.33 108	99.33 100	93.11 303	99.94 39	98.49 109	99.94 43	99.48 153
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IS-MVSNet		98.19 193	97.90 213	99.08 121	99.57 83	97.97 155	99.31 27	98.32 330	99.01 101	98.98 162	99.03 170	91.59 326	99.79 216	95.49 302	99.80 119	99.48 153

ACMP	95.32 15	98.41 163	98.09 192	99.36 66	99.51 107	98.79 82	97.68 229	99.38 145	95.76 323	98.81 200	98.82 224	98.36 72	99.82 182	94.75 316	99.77 135	99.48 153
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MCST-MVS		98.00 206	97.63 233	99.10 117	99.24 180	98.17 130	96.89 301	98.73 308	95.66 324	97.92 285	97.70 341	97.17 170	99.66 295	96.18 272	99.23 286	99.47 160

3Dnovator+	97.89 3	98.69 119	98.51 133	99.24 98	98.81 276	98.40 109	99.02 66	99.19 221	98.99 102	98.07 276	99.28 110	97.11 174	99.84 155	96.84 217	99.32 269	99.47 160

HPM-MVS++		98.10 198	97.64 232	99.48 53	99.09 218	99.13 59	97.52 253	98.75 305	97.46 230	96.90 350	97.83 334	96.01 228	99.84 155	95.82 290	99.35 265	99.46 162

V42		98.78 103	98.78 95	98.76 176	99.44 135	97.04 221	98.27 147	99.19 221	97.87 191	99.25 128	99.16 141	96.84 187	99.78 227	99.21 59	99.84 94	99.46 162

APD-MVS_3200maxsize		98.84 93	98.61 123	99.53 37	99.19 194	99.27 26	98.49 126	99.33 171	98.64 124	99.03 158	98.98 188	97.89 115	99.85 137	96.54 249	99.42 256	99.46 162

UniMVSNet (Re)		98.87 89	98.71 104	99.35 72	99.24 180	98.73 87	97.73 225	99.38 145	98.93 109	99.12 140	98.73 238	96.77 194	99.86 124	98.63 101	99.80 119	99.46 162

SR-MVS-dyc-post		98.81 99	98.55 128	99.57 20	99.20 191	99.38 12	98.48 129	99.30 186	98.64 124	98.95 170	98.96 193	97.49 153	99.86 124	96.56 245	99.39 259	99.45 166

RE-MVS-def			98.58 126		99.20 191	99.38 12	98.48 129	99.30 186	98.64 124	98.95 170	98.96 193	97.75 126		96.56 245	99.39 259	99.45 166

HQP_MVS		97.99 209	97.67 227	98.93 148	99.19 194	97.65 185	97.77 217	99.27 200	98.20 167	97.79 297	97.98 324	94.90 265	99.70 268	94.42 328	99.51 240	99.45 166

plane_prior5								99.27 200					99.70 268	94.42 328	99.51 240	99.45 166

lessismore_v0				98.97 142	99.73 36	97.53 192		86.71 432		99.37 101	99.52 63	89.93 339	99.92 55	98.99 74	99.72 162	99.44 170

TAMVS		98.24 188	98.05 197	98.80 165	99.07 222	97.18 215	97.88 201	98.81 295	96.66 289	99.17 139	99.21 128	94.81 271	99.77 233	96.96 204	99.88 82	99.44 170

DeepPCF-MVS	96.93 5	98.32 176	98.01 201	99.23 100	98.39 343	98.97 70	95.03 388	99.18 225	96.88 277	99.33 108	98.78 231	98.16 95	99.28 392	96.74 225	99.62 202	99.44 170

3Dnovator	98.27 2	98.81 99	98.73 99	99.05 130	98.76 281	97.81 174	99.25 40	99.30 186	98.57 136	98.55 235	99.33 100	97.95 112	99.90 71	97.16 185	99.67 188	99.44 170

MVSFormer		98.26 185	98.43 148	97.77 280	98.88 262	93.89 338	99.39 17	99.56 80	99.11 81	98.16 267	98.13 311	93.81 295	99.97 5	99.26 54	99.57 222	99.43 174

jason		97.45 252	97.35 250	97.76 283	99.24 180	93.93 334	95.86 360	98.42 326	94.24 362	98.50 241	98.13 311	94.82 269	99.91 64	97.22 182	99.73 154	99.43 174
jason: jason.
NCCC		97.86 219	97.47 244	99.05 130	98.61 314	98.07 144	96.98 294	98.90 275	97.63 206	97.04 340	97.93 329	95.99 233	99.66 295	95.31 305	98.82 333	99.43 174

Anonymous20240521		98.69 119	98.87 84	98.16 256	99.77 26	95.11 297	99.08 58	99.44 125	99.34 54	99.33 108	99.55 54	94.10 291	99.94 39	99.25 56	99.96 27	99.42 177

MVS_111021_HR		98.25 187	98.08 195	98.75 178	99.09 218	97.46 195	95.97 351	99.27 200	97.60 212	97.99 283	98.25 303	98.15 97	99.38 377	96.87 214	99.57 222	99.42 177

COLMAP_ROB	96.50 10	98.99 73	98.85 88	99.41 62	99.58 78	99.10 64	98.74 92	99.56 80	99.09 91	99.33 108	99.19 131	98.40 70	99.72 263	95.98 280	99.76 147	99.42 177
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SED-MVS		98.91 84	98.72 101	99.49 51	99.49 117	99.17 43	98.10 168	99.31 178	98.03 177	99.66 51	99.02 171	98.36 72	99.88 98	96.91 206	99.62 202	99.41 180

OPU-MVS				98.82 161	98.59 319	98.30 118	98.10 168				98.52 275	98.18 91	98.75 415	94.62 320	99.48 249	99.41 180

our_test_3		97.39 258	97.73 224	96.34 357	98.70 294	89.78 402	94.61 401	98.97 265	96.50 294	99.04 155	98.85 218	95.98 234	99.84 155	97.26 180	99.67 188	99.41 180

casdiffmvs		98.95 80	99.00 72	98.81 163	99.38 146	97.33 202	97.82 209	99.57 73	99.17 77	99.35 105	99.17 139	98.35 75	99.69 272	98.46 110	99.73 154	99.41 180
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
YYNet1		97.60 239	97.67 227	97.39 318	99.04 231	93.04 357	95.27 381	98.38 329	97.25 250	98.92 180	98.95 197	95.48 253	99.73 256	96.99 200	98.74 335	99.41 180

MDA-MVSNet_test_wron		97.60 239	97.66 230	97.41 317	99.04 231	93.09 353	95.27 381	98.42 326	97.26 249	98.88 187	98.95 197	95.43 254	99.73 256	97.02 197	98.72 337	99.41 180

GBi-Net		98.65 129	98.47 142	99.17 105	98.90 256	98.24 122	99.20 45	99.44 125	98.59 132	98.95 170	99.55 54	94.14 287	99.86 124	97.77 153	99.69 177	99.41 180

test1		98.65 129	98.47 142	99.17 105	98.90 256	98.24 122	99.20 45	99.44 125	98.59 132	98.95 170	99.55 54	94.14 287	99.86 124	97.77 153	99.69 177	99.41 180

FMVSNet1		99.17 47	99.17 52	99.17 105	99.55 95	98.24 122	99.20 45	99.44 125	99.21 68	99.43 88	99.55 54	97.82 121	99.86 124	98.42 113	99.89 80	99.41 180

test_fmvs1		97.72 231	97.94 209	97.07 332	98.66 309	92.39 368	97.68 229	99.81 28	95.20 341	99.54 65	99.44 78	91.56 327	99.41 372	99.78 18	99.77 135	99.40 189

KD-MVS_self_test		99.25 37	99.18 51	99.44 59	99.63 74	99.06 68	98.69 101	99.54 88	99.31 58	99.62 60	99.53 60	97.36 159	99.86 124	99.24 58	99.71 167	99.39 190

v148		98.45 160	98.60 124	98.00 268	99.44 135	94.98 299	97.44 262	99.06 247	98.30 154	99.32 114	98.97 190	96.65 202	99.62 309	98.37 114	99.85 90	99.39 190

test20.03		98.78 103	98.77 96	98.78 171	99.46 129	97.20 213	97.78 215	99.24 211	99.04 98	99.41 93	98.90 205	97.65 132	99.76 239	97.70 158	99.79 124	99.39 190

CDPH-MVS		97.26 267	96.66 294	99.07 123	99.00 237	98.15 131	96.03 349	99.01 261	91.21 402	97.79 297	97.85 333	96.89 185	99.69 272	92.75 371	99.38 262	99.39 190

EPNet		96.14 318	95.44 330	98.25 247	90.76 437	95.50 280	97.92 196	94.65 403	98.97 105	92.98 419	98.85 218	89.12 345	99.87 116	95.99 279	99.68 182	99.39 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		98.17 196	97.87 215	99.07 123	98.67 304	98.24 122	97.01 292	98.93 269	97.25 250	97.62 306	98.34 297	97.27 164	99.57 329	96.42 256	99.33 268	99.39 190

DeepC-MVS_fast	96.85 6	98.30 179	98.15 187	98.75 178	98.61 314	97.23 208	97.76 220	99.09 244	97.31 244	98.75 207	98.66 253	97.56 142	99.64 303	96.10 277	99.55 229	99.39 190
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		98.53 150	98.27 172	99.32 83	99.31 163	98.75 83	98.19 154	99.41 138	96.77 284	98.83 195	98.90 205	97.80 123	99.82 182	95.68 296	99.52 238	99.38 197

test9_res														93.28 360	99.15 299	99.38 197

BP-MVS1		97.40 257	96.97 270	98.71 184	99.07 222	96.81 234	98.34 144	97.18 363	98.58 135	98.17 264	98.61 264	84.01 382	99.94 39	98.97 75	99.78 129	99.37 199

OPM-MVS		98.56 142	98.32 166	99.25 96	99.41 143	98.73 87	97.13 289	99.18 225	97.10 265	98.75 207	98.92 201	98.18 91	99.65 300	96.68 232	99.56 225	99.37 199
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
agg_prior2														92.50 376	99.16 297	99.37 199

AllTest		98.44 161	98.20 179	99.16 108	99.50 110	98.55 99	98.25 149	99.58 66	96.80 281	98.88 187	99.06 159	97.65 132	99.57 329	94.45 326	99.61 207	99.37 199

TestCases				99.16 108	99.50 110	98.55 99		99.58 66	96.80 281	98.88 187	99.06 159	97.65 132	99.57 329	94.45 326	99.61 207	99.37 199

MDA-MVSNet-bldmvs		97.94 210	97.91 212	98.06 263	99.44 135	94.96 300	96.63 314	99.15 237	98.35 148	98.83 195	99.11 151	94.31 284	99.85 137	96.60 238	98.72 337	99.37 199

MVSTER		96.86 293	96.55 300	97.79 278	97.91 369	94.21 321	97.56 248	98.87 281	97.49 224	99.06 148	99.05 166	80.72 395	99.80 203	98.44 111	99.82 104	99.37 199

pmmvs5		97.64 237	97.49 241	98.08 261	99.14 209	95.12 296	96.70 311	99.05 250	93.77 371	98.62 222	98.83 221	93.23 300	99.75 246	98.33 118	99.76 147	99.36 206

Anonymous20231206		98.21 191	98.21 178	98.20 251	99.51 107	95.43 284	98.13 162	99.32 173	96.16 308	98.93 178	98.82 224	96.00 229	99.83 172	97.32 177	99.73 154	99.36 206

train_agg		97.10 279	96.45 304	99.07 123	98.71 290	98.08 142	95.96 353	99.03 255	91.64 394	95.85 382	97.53 349	96.47 209	99.76 239	93.67 350	99.16 297	99.36 206

PVSNet_BlendedMVS		97.55 244	97.53 238	97.60 298	98.92 252	93.77 342	96.64 313	99.43 131	94.49 354	97.62 306	99.18 135	96.82 190	99.67 284	94.73 317	99.93 48	99.36 206

Anonymous20240529		98.93 82	98.87 84	99.12 113	99.19 194	98.22 127	99.01 67	98.99 264	99.25 64	99.54 65	99.37 89	97.04 176	99.80 203	97.89 143	99.52 238	99.35 210

F-COLMAP		97.30 264	96.68 291	99.14 111	99.19 194	98.39 110	97.27 277	99.30 186	92.93 382	96.62 362	98.00 322	95.73 244	99.68 281	92.62 374	98.46 354	99.35 210

ppachtmachnet_test		97.50 245	97.74 222	96.78 347	98.70 294	91.23 389	94.55 403	99.05 250	96.36 300	99.21 132	98.79 229	96.39 212	99.78 227	96.74 225	99.82 104	99.34 212

VDD-MVS		98.56 142	98.39 155	99.07 123	99.13 211	98.07 144	98.59 110	97.01 368	99.59 27	99.11 141	99.27 112	94.82 269	99.79 216	98.34 116	99.63 199	99.34 212

testgi		98.32 176	98.39 155	98.13 257	99.57 83	95.54 277	97.78 215	99.49 104	97.37 238	99.19 134	97.65 343	98.96 26	99.49 356	96.50 252	98.99 319	99.34 212

diffmvs		98.22 189	98.24 176	98.17 254	99.00 237	95.44 283	96.38 328	99.58 66	97.79 197	98.53 238	98.50 280	96.76 196	99.74 251	97.95 142	99.64 196	99.34 212
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UnsupCasMVSNet_eth		97.89 214	97.60 235	98.75 178	99.31 163	97.17 216	97.62 239	99.35 159	98.72 122	98.76 206	98.68 248	92.57 316	99.74 251	97.76 157	95.60 417	99.34 212

baseline		98.96 79	99.02 70	98.76 176	99.38 146	97.26 207	98.49 126	99.50 97	98.86 115	99.19 134	99.06 159	98.23 84	99.69 272	98.71 95	99.76 147	99.33 217

MG-MVS		96.77 297	96.61 296	97.26 323	98.31 347	93.06 354	95.93 356	98.12 340	96.45 298	97.92 285	98.73 238	93.77 297	99.39 375	91.19 395	99.04 311	99.33 217

HQP4-MVS										95.56 387			99.54 341			99.32 219

CDS-MVSNet		97.69 233	97.35 250	98.69 185	98.73 285	97.02 223	96.92 300	98.75 305	95.89 320	98.59 228	98.67 250	92.08 322	99.74 251	96.72 228	99.81 108	99.32 219
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP-MVS		97.00 288	96.49 303	98.55 212	98.67 304	96.79 235	96.29 334	99.04 253	96.05 311	95.55 388	96.84 370	93.84 293	99.54 341	92.82 368	99.26 281	99.32 219

RPSCF		98.62 136	98.36 159	99.42 60	99.65 64	99.42 11	98.55 114	99.57 73	97.72 201	98.90 182	99.26 117	96.12 224	99.52 347	95.72 293	99.71 167	99.32 219

MVP-Stereo		98.08 201	97.92 211	98.57 207	98.96 244	96.79 235	97.90 199	99.18 225	96.41 299	98.46 244	98.95 197	95.93 238	99.60 317	96.51 251	98.98 322	99.31 223
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		98.40 165	98.68 110	97.54 306	98.96 244	97.99 151	97.88 201	99.36 153	98.20 167	99.63 57	99.04 168	98.76 40	95.33 432	96.56 245	99.74 151	99.31 223
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
VNet		98.42 162	98.30 167	98.79 168	98.79 280	97.29 204	98.23 150	98.66 312	99.31 58	98.85 192	98.80 227	94.80 272	99.78 227	98.13 127	99.13 302	99.31 223

test_prior				98.95 145	98.69 299	97.95 159		99.03 255					99.59 321			99.30 226

USDC		97.41 256	97.40 245	97.44 315	98.94 246	93.67 345	95.17 384	99.53 91	94.03 368	98.97 166	99.10 154	95.29 256	99.34 382	95.84 289	99.73 154	99.30 226

test_fmvsm_n_1920		99.33 28	99.45 20	98.99 138	99.57 83	97.73 181	97.93 193	99.83 24	99.22 66	99.93 6	99.30 106	99.42 11	99.96 12	99.85 5	99.99 5	99.29 228

FMVSNet2		98.49 156	98.40 152	98.75 178	98.90 256	97.14 219	98.61 108	99.13 238	98.59 132	99.19 134	99.28 110	94.14 287	99.82 182	97.97 140	99.80 119	99.29 228

XVG-OURS-SEG-HR		98.49 156	98.28 169	99.14 111	99.49 117	98.83 79	96.54 316	99.48 106	97.32 243	99.11 141	98.61 264	99.33 14	99.30 388	96.23 267	98.38 355	99.28 230

test12				98.93 148	98.58 321	97.83 168		98.66 312		96.53 366		95.51 251	99.69 272		99.13 302	99.27 231

DSMNet-mixed		97.42 255	97.60 235	96.87 341	99.15 208	91.46 380	98.54 116	99.12 239	92.87 384	97.58 310	99.63 36	96.21 220	99.90 71	95.74 292	99.54 231	99.27 231

N_pmnet		97.63 238	97.17 259	98.99 138	99.27 173	97.86 165	95.98 350	93.41 414	95.25 338	99.47 82	98.90 205	95.63 246	99.85 137	96.91 206	99.73 154	99.27 231

ambc				98.24 249	98.82 273	95.97 266	98.62 107	99.00 263		99.27 120	99.21 128	96.99 181	99.50 353	96.55 248	99.50 247	99.26 234

LFMVS		97.20 273	96.72 288	98.64 191	98.72 287	96.95 227	98.93 78	94.14 411	99.74 10	98.78 201	99.01 180	84.45 377	99.73 256	97.44 171	99.27 278	99.25 235

FMVSNet5		96.01 321	95.20 340	98.41 231	97.53 391	96.10 257	98.74 92	99.50 97	97.22 259	98.03 281	99.04 168	69.80 418	99.88 98	97.27 179	99.71 167	99.25 235

BH-RMVSNet		96.83 294	96.58 299	97.58 300	98.47 332	94.05 326	96.67 312	97.36 357	96.70 288	97.87 290	97.98 324	95.14 260	99.44 368	90.47 403	98.58 351	99.25 235

testf1		99.25 37	99.16 54	99.51 46	99.89 6	99.63 4	98.71 99	99.69 46	98.90 111	99.43 88	99.35 94	98.86 31	99.67 284	97.81 149	99.81 108	99.24 238

APD_test2		99.25 37	99.16 54	99.51 46	99.89 6	99.63 4	98.71 99	99.69 46	98.90 111	99.43 88	99.35 94	98.86 31	99.67 284	97.81 149	99.81 108	99.24 238

旧先验1					98.82 273	97.45 196		98.76 302			98.34 297	95.50 252			99.01 316	99.23 240

test222					98.92 252	96.93 229	95.54 371	98.78 300	85.72 422	96.86 353	98.11 314	94.43 279			99.10 307	99.23 240

XVG-ACMP-BASELINE		98.56 142	98.34 162	99.22 101	99.54 100	98.59 96	97.71 226	99.46 117	97.25 250	98.98 162	98.99 184	97.54 144	99.84 155	95.88 283	99.74 151	99.23 240

FMVSNet3		97.50 245	97.24 256	98.29 245	98.08 362	95.83 270	97.86 205	98.91 274	97.89 190	98.95 170	98.95 197	87.06 356	99.81 196	97.77 153	99.69 177	99.23 240

无先验							95.74 366	98.74 307	89.38 413				99.73 256	92.38 378		99.22 244

tttt0517		95.64 334	94.98 344	97.64 295	99.36 153	93.81 340	98.72 97	90.47 425	98.08 176	98.67 215	98.34 297	73.88 413	99.92 55	97.77 153	99.51 240	99.20 245

pmmvs-eth3d		98.47 158	98.34 162	98.86 157	99.30 167	97.76 177	97.16 287	99.28 197	95.54 329	99.42 91	99.19 131	97.27 164	99.63 306	97.89 143	99.97 20	99.20 245

MS-PatchMatch		97.68 234	97.75 221	97.45 314	98.23 353	93.78 341	97.29 274	98.84 290	96.10 310	98.64 219	98.65 255	96.04 226	99.36 378	96.84 217	99.14 300	99.20 245

新几何1				98.91 152	98.94 246	97.76 177		98.76 302	87.58 419	96.75 358	98.10 315	94.80 272	99.78 227	92.73 372	99.00 317	99.20 245

PHI-MVS		98.29 182	97.95 207	99.34 75	98.44 337	99.16 47	98.12 165	99.38 145	96.01 315	98.06 277	98.43 287	97.80 123	99.67 284	95.69 295	99.58 218	99.20 245

GDP-MVS		97.50 245	97.11 264	98.67 188	99.02 235	96.85 232	98.16 159	99.71 42	98.32 152	98.52 240	98.54 271	83.39 386	99.95 24	98.79 86	99.56 225	99.19 250

Anonymous202405211		97.90 212	97.50 240	99.08 121	98.90 256	98.25 121	98.53 117	96.16 385	98.87 113	99.11 141	98.86 215	90.40 337	99.78 227	97.36 175	99.31 271	99.19 250

CANet		97.87 218	97.76 220	98.19 253	97.75 375	95.51 279	96.76 307	99.05 250	97.74 199	96.93 344	98.21 307	95.59 248	99.89 84	97.86 148	99.93 48	99.19 250

XVG-OURS		98.53 150	98.34 162	99.11 115	99.50 110	98.82 81	95.97 351	99.50 97	97.30 245	99.05 153	98.98 188	99.35 13	99.32 385	95.72 293	99.68 182	99.18 253

WTY-MVS		96.67 300	96.27 310	97.87 273	98.81 276	94.61 312	96.77 306	97.92 345	94.94 346	97.12 335	97.74 338	91.11 330	99.82 182	93.89 344	98.15 367	99.18 253

Vis-MVSNet (Re-imp)		97.46 250	97.16 260	98.34 240	99.55 95	96.10 257	98.94 77	98.44 324	98.32 152	98.16 267	98.62 262	88.76 346	99.73 256	93.88 345	99.79 124	99.18 253

TinyColmap		97.89 214	97.98 204	97.60 298	98.86 264	94.35 318	96.21 338	99.44 125	97.45 232	99.06 148	98.88 212	97.99 110	99.28 392	94.38 332	99.58 218	99.18 253

testdata				98.09 258	98.93 248	95.40 285		98.80 297	90.08 410	97.45 323	98.37 293	95.26 257	99.70 268	93.58 353	98.95 325	99.17 257

lupinMVS		97.06 282	96.86 278	97.65 293	98.88 262	93.89 338	95.48 375	97.97 343	93.53 374	98.16 267	97.58 347	93.81 295	99.91 64	96.77 222	99.57 222	99.17 257

Patchmtry		97.35 260	96.97 270	98.50 222	97.31 402	96.47 249	98.18 155	98.92 272	98.95 108	98.78 201	99.37 89	85.44 371	99.85 137	95.96 281	99.83 101	99.17 257

RRT-MVS		97.88 216	97.98 204	97.61 297	98.15 357	93.77 342	98.97 73	99.64 57	99.16 78	98.69 212	99.42 81	91.60 325	99.89 84	97.63 161	98.52 353	99.16 260

sss		97.21 272	96.93 272	98.06 263	98.83 270	95.22 292	96.75 308	98.48 323	94.49 354	97.27 332	97.90 330	92.77 312	99.80 203	96.57 241	99.32 269	99.16 260

CSCG		98.68 124	98.50 135	99.20 102	99.45 134	98.63 91	98.56 113	99.57 73	97.87 191	98.85 192	98.04 321	97.66 131	99.84 155	96.72 228	99.81 108	99.13 262

MVS_111021_LR		98.30 179	98.12 190	98.83 160	99.16 204	98.03 149	96.09 347	99.30 186	97.58 213	98.10 274	98.24 304	98.25 82	99.34 382	96.69 231	99.65 194	99.12 263

miper_lstm_enhance		97.18 275	97.16 260	97.25 324	98.16 356	92.85 359	95.15 386	99.31 178	97.25 250	98.74 209	98.78 231	90.07 338	99.78 227	97.19 183	99.80 119	99.11 264

testing3		93.51 370	92.09 381	97.75 284	98.60 316	94.40 316	97.32 271	95.26 400	97.56 216	96.79 357	95.50 398	53.57 438	99.77 233	95.26 306	98.97 323	99.08 265

原ACMM1				98.35 239	98.90 256	96.25 255		98.83 294	92.48 388	96.07 379	98.10 315	95.39 255	99.71 264	92.61 375	98.99 319	99.08 265

QAPM		97.31 263	96.81 284	98.82 161	98.80 279	97.49 193	99.06 62	99.19 221	90.22 408	97.69 303	99.16 141	96.91 184	99.90 71	90.89 400	99.41 257	99.07 267

PAPM_NR		96.82 296	96.32 307	98.30 244	99.07 222	96.69 242	97.48 258	98.76 302	95.81 322	96.61 363	96.47 379	94.12 290	99.17 399	90.82 401	97.78 380	99.06 268

eth_miper_zixun_eth		97.23 271	97.25 255	97.17 327	98.00 365	92.77 361	94.71 395	99.18 225	97.27 248	98.56 233	98.74 237	91.89 323	99.69 272	97.06 196	99.81 108	99.05 269

D2MVS		97.84 225	97.84 217	97.83 275	99.14 209	94.74 306	96.94 296	98.88 279	95.84 321	98.89 184	98.96 193	94.40 281	99.69 272	97.55 165	99.95 35	99.05 269

c3_l		97.36 259	97.37 248	97.31 319	98.09 361	93.25 352	95.01 389	99.16 232	97.05 267	98.77 204	98.72 240	92.88 309	99.64 303	96.93 205	99.76 147	99.05 269

PLC	94.65 16	96.51 305	95.73 317	98.85 158	98.75 283	97.91 161	96.42 326	99.06 247	90.94 405	95.59 385	97.38 359	94.41 280	99.59 321	90.93 398	98.04 376	99.05 269
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpnnormal		98.90 86	98.90 81	98.91 152	99.67 61	97.82 171	99.00 69	99.44 125	99.45 40	99.51 76	99.24 122	98.20 90	99.86 124	95.92 282	99.69 177	99.04 273

CANet_DTU		97.26 267	97.06 266	97.84 274	97.57 386	94.65 311	96.19 340	98.79 298	97.23 256	95.14 397	98.24 304	93.22 301	99.84 155	97.34 176	99.84 94	99.04 273

PM-MVS		98.82 97	98.72 101	99.12 113	99.64 70	98.54 102	97.98 189	99.68 51	97.62 207	99.34 107	99.18 135	97.54 144	99.77 233	97.79 151	99.74 151	99.04 273

TSAR-MVS + GP.		98.18 194	97.98 204	98.77 175	98.71 290	97.88 163	96.32 332	98.66 312	96.33 301	99.23 131	98.51 276	97.48 154	99.40 373	97.16 185	99.46 250	99.02 276

DIV-MVS_self_test		97.02 285	96.84 280	97.58 300	97.82 373	94.03 329	94.66 398	99.16 232	97.04 268	98.63 220	98.71 241	88.69 347	99.69 272	97.00 198	99.81 108	99.01 277

mamv4		99.44 16	99.39 24	99.58 19	99.30 167	99.74 2	99.04 65	99.81 28	99.77 7	99.82 28	99.57 46	97.82 121	99.98 4	99.53 39	99.89 80	99.01 277

GA-MVS		95.86 326	95.32 336	97.49 311	98.60 316	94.15 324	93.83 415	97.93 344	95.49 331	96.68 359	97.42 357	83.21 387	99.30 388	96.22 268	98.55 352	99.01 277

OMC-MVS		97.88 216	97.49 241	99.04 132	98.89 261	98.63 91	96.94 296	99.25 206	95.02 343	98.53 238	98.51 276	97.27 164	99.47 362	93.50 356	99.51 240	99.01 277

cl____		97.02 285	96.83 281	97.58 300	97.82 373	94.04 328	94.66 398	99.16 232	97.04 268	98.63 220	98.71 241	88.68 349	99.69 272	97.00 198	99.81 108	99.00 281

pmmvs4		97.58 242	97.28 253	98.51 218	98.84 268	96.93 229	95.40 379	98.52 321	93.60 373	98.61 224	98.65 255	95.10 261	99.60 317	96.97 203	99.79 124	98.99 282

EPNet_dtu		94.93 349	94.78 349	95.38 385	93.58 433	87.68 412	96.78 305	95.69 397	97.35 240	89.14 430	98.09 317	88.15 354	99.49 356	94.95 313	99.30 274	98.98 283
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		96.50 307	95.77 315	98.69 185	99.48 125	97.43 198	97.84 208	99.55 84	81.42 428	96.51 368	98.58 268	95.53 249	99.67 284	93.41 358	99.58 218	98.98 283

PVSNet_Blended		96.88 292	96.68 291	97.47 313	98.92 252	93.77 342	94.71 395	99.43 131	90.98 404	97.62 306	97.36 361	96.82 190	99.67 284	94.73 317	99.56 225	98.98 283

APD_test1		98.83 94	98.66 113	99.34 75	99.78 23	99.47 9	98.42 136	99.45 121	98.28 159	98.98 162	99.19 131	97.76 125	99.58 327	96.57 241	99.55 229	98.97 286

PAPR		95.29 340	94.47 351	97.75 284	97.50 397	95.14 295	94.89 392	98.71 310	91.39 400	95.35 395	95.48 400	94.57 277	99.14 402	84.95 419	97.37 393	98.97 286

EGC-MVSNET		85.24 397	80.54 400	99.34 75	99.77 26	99.20 38	99.08 58	99.29 194	12.08 435	20.84 436	99.42 81	97.55 143	99.85 137	97.08 193	99.72 162	98.96 288

thisisatest0530		95.27 341	94.45 352	97.74 286	99.19 194	94.37 317	97.86 205	90.20 426	97.17 261	98.22 262	97.65 343	73.53 414	99.90 71	96.90 211	99.35 265	98.95 289

mvs_anonymous		97.83 227	98.16 186	96.87 341	98.18 355	91.89 375	97.31 272	98.90 275	97.37 238	98.83 195	99.46 73	96.28 218	99.79 216	98.90 79	98.16 366	98.95 289

baseline1		95.96 324	95.44 330	97.52 308	98.51 330	93.99 332	98.39 138	96.09 388	98.21 163	98.40 253	97.76 337	86.88 357	99.63 306	95.42 303	89.27 430	98.95 289

CLD-MVS		97.49 248	97.16 260	98.48 223	99.07 222	97.03 222	94.71 395	99.21 215	94.46 356	98.06 277	97.16 365	97.57 141	99.48 359	94.46 325	99.78 129	98.95 289
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MSLP-MVS++		98.02 204	98.14 189	97.64 295	98.58 321	95.19 293	97.48 258	99.23 213	97.47 225	97.90 287	98.62 262	97.04 176	98.81 413	97.55 165	99.41 257	98.94 293

DELS-MVS		98.27 183	98.20 179	98.48 223	98.86 264	96.70 241	95.60 370	99.20 217	97.73 200	98.45 245	98.71 241	97.50 150	99.82 182	98.21 122	99.59 213	98.93 294
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
cl22		95.79 329	95.39 333	96.98 335	96.77 414	92.79 360	94.40 406	98.53 320	94.59 353	97.89 288	98.17 310	82.82 391	99.24 394	96.37 259	99.03 312	98.92 295

LS3D		98.63 133	98.38 157	99.36 66	97.25 403	99.38 12	99.12 57	99.32 173	99.21 68	98.44 246	98.88 212	97.31 160	99.80 203	96.58 239	99.34 267	98.92 295

CMPMVS	75.91 23	96.29 313	95.44 330	98.84 159	96.25 424	98.69 90	97.02 291	99.12 239	88.90 415	97.83 294	98.86 215	89.51 342	98.90 411	91.92 379	99.51 240	98.92 295
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet-Re		98.64 131	98.48 140	99.11 115	98.85 267	98.51 104	98.49 126	99.83 24	98.37 147	99.69 46	99.46 73	98.21 89	99.92 55	94.13 338	99.30 274	98.91 298

mvsmamba		97.57 243	97.26 254	98.51 218	98.69 299	96.73 240	98.74 92	97.25 362	97.03 270	97.88 289	99.23 126	90.95 331	99.87 116	96.61 237	99.00 317	98.91 298

DPM-MVS		96.32 312	95.59 324	98.51 218	98.76 281	97.21 212	94.54 404	98.26 332	91.94 393	96.37 372	97.25 363	93.06 306	99.43 369	91.42 390	98.74 335	98.89 300

test_yl		96.69 298	96.29 308	97.90 270	98.28 348	95.24 290	97.29 274	97.36 357	98.21 163	98.17 264	97.86 331	86.27 361	99.55 336	94.87 314	98.32 356	98.89 300

DCV-MVSNet		96.69 298	96.29 308	97.90 270	98.28 348	95.24 290	97.29 274	97.36 357	98.21 163	98.17 264	97.86 331	86.27 361	99.55 336	94.87 314	98.32 356	98.89 300

SPE-MVS-test		99.13 56	99.09 64	99.26 93	99.13 211	98.97 70	99.31 27	99.88 14	99.44 42	98.16 267	98.51 276	98.64 49	99.93 46	98.91 78	99.85 90	98.88 303

UnsupCasMVSNet_bld		97.30 264	96.92 274	98.45 226	99.28 171	96.78 238	96.20 339	99.27 200	95.42 333	98.28 259	98.30 301	93.16 302	99.71 264	94.99 310	97.37 393	98.87 304

Effi-MVS+		98.02 204	97.82 218	98.62 197	98.53 328	97.19 214	97.33 270	99.68 51	97.30 245	96.68 359	97.46 355	98.56 59	99.80 203	96.63 235	98.20 362	98.86 305

test_0402		98.76 107	98.71 104	98.93 148	99.56 91	98.14 133	98.45 133	99.34 165	99.28 62	98.95 170	98.91 202	98.34 76	99.79 216	95.63 297	99.91 68	98.86 305

PatchmatchNet		95.58 335	95.67 320	95.30 386	97.34 401	87.32 414	97.65 235	96.65 378	95.30 337	97.07 338	98.69 246	84.77 374	99.75 246	94.97 312	98.64 346	98.83 307
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing3-2		93.78 366	93.91 358	93.39 407	98.82 273	81.72 434	97.76 220	95.28 399	98.60 131	96.54 365	96.66 374	65.85 430	99.62 309	96.65 234	98.99 319	98.82 308

test_vis1_rt		97.75 229	97.72 225	97.83 275	98.81 276	96.35 252	97.30 273	99.69 46	94.61 352	97.87 290	98.05 320	96.26 219	98.32 420	98.74 92	98.18 363	98.82 308

CL-MVSNet_self_test		97.44 253	97.22 257	98.08 261	98.57 323	95.78 272	94.30 408	98.79 298	96.58 292	98.60 226	98.19 309	94.74 275	99.64 303	96.41 257	98.84 330	98.82 308

miper_ehance_all_eth		97.06 282	97.03 267	97.16 329	97.83 372	93.06 354	94.66 398	99.09 244	95.99 316	98.69 212	98.45 285	92.73 314	99.61 316	96.79 219	99.03 312	98.82 308

MIMVSNet		96.62 303	96.25 311	97.71 290	99.04 231	94.66 310	99.16 51	96.92 374	97.23 256	97.87 290	99.10 154	86.11 365	99.65 300	91.65 385	99.21 290	98.82 308

hse-mvs2		97.46 250	97.07 265	98.64 191	98.73 285	97.33 202	97.45 261	97.64 354	99.11 81	98.58 230	97.98 324	88.65 350	99.79 216	98.11 128	97.39 392	98.81 313

GSMVS																98.81 313

sam_mvs1												84.74 375				98.81 313

SCA		96.41 311	96.66 294	95.67 376	98.24 351	88.35 408	95.85 362	96.88 375	96.11 309	97.67 304	98.67 250	93.10 304	99.85 137	94.16 334	99.22 287	98.81 313

Patchmatch-RL test		97.26 267	97.02 268	97.99 269	99.52 105	95.53 278	96.13 345	99.71 42	97.47 225	99.27 120	99.16 141	84.30 380	99.62 309	97.89 143	99.77 135	98.81 313

AUN-MVS		96.24 317	95.45 329	98.60 202	98.70 294	97.22 210	97.38 265	97.65 352	95.95 318	95.53 392	97.96 328	82.11 394	99.79 216	96.31 263	97.44 389	98.80 318

ITE_SJBPF				98.87 156	99.22 185	98.48 106		99.35 159	97.50 222	98.28 259	98.60 266	97.64 135	99.35 381	93.86 346	99.27 278	98.79 319

tpm		94.67 351	94.34 355	95.66 377	97.68 384	88.42 407	97.88 201	94.90 401	94.46 356	96.03 381	98.56 270	78.66 405	99.79 216	95.88 283	95.01 420	98.78 320

Patchmatch-test		96.55 304	96.34 306	97.17 327	98.35 344	93.06 354	98.40 137	97.79 346	97.33 241	98.41 249	98.67 250	83.68 385	99.69 272	95.16 308	99.31 271	98.77 321

EC-MVSNet		99.09 61	99.05 68	99.20 102	99.28 171	98.93 75	99.24 41	99.84 21	99.08 93	98.12 272	98.37 293	98.72 43	99.90 71	99.05 69	99.77 135	98.77 321

PMMVS		96.51 305	95.98 312	98.09 258	97.53 391	95.84 269	94.92 391	98.84 290	91.58 396	96.05 380	95.58 395	95.68 245	99.66 295	95.59 299	98.09 370	98.76 323

test_method		79.78 398	79.50 401	80.62 414	80.21 439	45.76 442	70.82 430	98.41 328	31.08 434	80.89 434	97.71 339	84.85 373	97.37 427	91.51 389	80.03 431	98.75 324

ab-mvs		98.41 163	98.36 159	98.59 203	99.19 194	97.23 208	99.32 23	98.81 295	97.66 204	98.62 222	99.40 88	96.82 190	99.80 203	95.88 283	99.51 240	98.75 324

CHOSEN 280x420		95.51 338	95.47 327	95.65 378	98.25 350	88.27 409	93.25 419	98.88 279	93.53 374	94.65 403	97.15 366	86.17 363	99.93 46	97.41 173	99.93 48	98.73 326

test_fmvsmvis_n_1920		99.26 36	99.49 13	98.54 215	99.66 63	96.97 224	98.00 184	99.85 18	99.24 65	99.92 8	99.50 64	99.39 12	99.95 24	99.89 3	99.98 12	98.71 327

MVS_Test		98.18 194	98.36 159	97.67 291	98.48 331	94.73 307	98.18 155	99.02 258	97.69 202	98.04 280	99.11 151	97.22 168	99.56 332	98.57 104	98.90 329	98.71 327

PVSNet	93.40 17	95.67 332	95.70 318	95.57 379	98.83 270	88.57 406	92.50 422	97.72 348	92.69 386	96.49 371	96.44 380	93.72 298	99.43 369	93.61 351	99.28 277	98.71 327

alignmvs		97.35 260	96.88 277	98.78 171	98.54 326	98.09 138	97.71 226	97.69 350	99.20 70	97.59 309	95.90 390	88.12 355	99.55 336	98.18 124	98.96 324	98.70 330

ADS-MVSNet2		95.43 339	94.98 344	96.76 348	98.14 358	91.74 376	97.92 196	97.76 347	90.23 406	96.51 368	98.91 202	85.61 368	99.85 137	92.88 366	96.90 402	98.69 331

ADS-MVSNet		95.24 342	94.93 347	96.18 365	98.14 358	90.10 401	97.92 196	97.32 360	90.23 406	96.51 368	98.91 202	85.61 368	99.74 251	92.88 366	96.90 402	98.69 331

MDTV_nov1_ep13_2view						74.92 438	97.69 228		90.06 411	97.75 300		85.78 367		93.52 354		98.69 331

MSDG		97.71 232	97.52 239	98.28 246	98.91 255	96.82 233	94.42 405	99.37 149	97.65 205	98.37 254	98.29 302	97.40 157	99.33 384	94.09 339	99.22 287	98.68 334

mvsany_test1		97.60 239	97.54 237	97.77 280	97.72 376	95.35 286	95.36 380	97.13 366	94.13 365	99.71 42	99.33 100	97.93 113	99.30 388	97.60 164	98.94 326	98.67 335

CS-MVS		99.13 56	99.10 62	99.24 98	99.06 227	99.15 51	99.36 19	99.88 14	99.36 53	98.21 263	98.46 284	98.68 47	99.93 46	99.03 71	99.85 90	98.64 336

Syy-MVS		96.04 320	95.56 326	97.49 311	97.10 407	94.48 314	96.18 342	96.58 380	95.65 325	94.77 400	92.29 429	91.27 329	99.36 378	98.17 126	98.05 374	98.63 337

myMVS_eth3d		91.92 393	90.45 395	96.30 358	97.10 407	90.90 393	96.18 342	96.58 380	95.65 325	94.77 400	92.29 429	53.88 437	99.36 378	89.59 407	98.05 374	98.63 337

balanced_conf03		98.63 133	98.72 101	98.38 234	98.66 309	96.68 243	98.90 80	99.42 134	98.99 102	98.97 166	99.19 131	95.81 242	99.85 137	98.77 90	99.77 135	98.60 339

miper_enhance_ethall		96.01 321	95.74 316	96.81 345	96.41 422	92.27 372	93.69 417	98.89 278	91.14 403	98.30 255	97.35 362	90.58 335	99.58 327	96.31 263	99.03 312	98.60 339

Effi-MVS+-dtu		98.26 185	97.90 213	99.35 72	98.02 364	99.49 6	98.02 180	99.16 232	98.29 157	97.64 305	97.99 323	96.44 211	99.95 24	96.66 233	98.93 327	98.60 339

new_pmnet		96.99 289	96.76 286	97.67 291	98.72 287	94.89 301	95.95 355	98.20 335	92.62 387	98.55 235	98.54 271	94.88 268	99.52 347	93.96 342	99.44 255	98.59 342

MVSMamba_PlusPlus		98.83 94	98.98 75	98.36 238	99.32 162	96.58 246	98.90 80	99.41 138	99.75 8	98.72 210	99.50 64	96.17 221	99.94 39	99.27 53	99.78 129	98.57 343

testing91		93.32 373	92.27 378	96.47 354	97.54 389	91.25 387	96.17 344	96.76 377	97.18 260	93.65 417	93.50 421	65.11 432	99.63 306	93.04 363	97.45 388	98.53 344

EIA-MVS		98.00 206	97.74 222	98.80 165	98.72 287	98.09 138	98.05 175	99.60 63	97.39 236	96.63 361	95.55 396	97.68 129	99.80 203	96.73 227	99.27 278	98.52 345

PatchMatch-RL		97.24 270	96.78 285	98.61 200	99.03 234	97.83 168	96.36 329	99.06 247	93.49 376	97.36 330	97.78 335	95.75 243	99.49 356	93.44 357	98.77 334	98.52 345

sasdasda		98.34 172	98.26 173	98.58 204	98.46 334	97.82 171	98.96 74	99.46 117	99.19 74	97.46 321	95.46 401	98.59 55	99.46 364	98.08 131	98.71 339	98.46 347

ET-MVSNet_ETH3D		94.30 357	93.21 368	97.58 300	98.14 358	94.47 315	94.78 394	93.24 416	94.72 350	89.56 428	95.87 391	78.57 407	99.81 196	96.91 206	97.11 401	98.46 347

canonicalmvs		98.34 172	98.26 173	98.58 204	98.46 334	97.82 171	98.96 74	99.46 117	99.19 74	97.46 321	95.46 401	98.59 55	99.46 364	98.08 131	98.71 339	98.46 347

UBG		93.25 375	92.32 376	96.04 370	97.72 376	90.16 400	95.92 358	95.91 392	96.03 314	93.95 414	93.04 425	69.60 419	99.52 347	90.72 402	97.98 377	98.45 350

tt0805		98.69 119	98.62 119	98.90 155	99.75 33	99.30 21	99.15 53	96.97 370	98.86 115	98.87 191	97.62 346	98.63 51	98.96 407	99.41 46	98.29 359	98.45 350

TAPA-MVS	96.21 11	96.63 302	95.95 313	98.65 189	98.93 248	98.09 138	96.93 298	99.28 197	83.58 425	98.13 271	97.78 335	96.13 223	99.40 373	93.52 354	99.29 276	98.45 350
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MGCFI-Net		98.34 172	98.28 169	98.51 218	98.47 332	97.59 189	98.96 74	99.48 106	99.18 76	97.40 326	95.50 398	98.66 48	99.50 353	98.18 124	98.71 339	98.44 353

BH-untuned		96.83 294	96.75 287	97.08 330	98.74 284	93.33 351	96.71 310	98.26 332	96.72 286	98.44 246	97.37 360	95.20 258	99.47 362	91.89 380	97.43 390	98.44 353

WB-MVSnew		95.73 331	95.57 325	96.23 363	96.70 415	90.70 397	96.07 348	93.86 412	95.60 327	97.04 340	95.45 404	96.00 229	99.55 336	91.04 396	98.31 358	98.43 355

pmmvs3		95.03 346	94.40 353	96.93 337	97.70 381	92.53 365	95.08 387	97.71 349	88.57 416	97.71 301	98.08 318	79.39 402	99.82 182	96.19 270	99.11 306	98.43 355

DP-MVS Recon		97.33 262	96.92 274	98.57 207	99.09 218	97.99 151	96.79 304	99.35 159	93.18 378	97.71 301	98.07 319	95.00 264	99.31 386	93.97 341	99.13 302	98.42 357

testing99		93.04 379	91.98 386	96.23 363	97.53 391	90.70 397	96.35 330	95.94 391	96.87 278	93.41 418	93.43 423	63.84 434	99.59 321	93.24 361	97.19 398	98.40 358

ETVMVS		92.60 384	91.08 393	97.18 325	97.70 381	93.65 347	96.54 316	95.70 395	96.51 293	94.68 402	92.39 428	61.80 435	99.50 353	86.97 414	97.41 391	98.40 358

Fast-Effi-MVS+-dtu		98.27 183	98.09 192	98.81 163	98.43 338	98.11 135	97.61 242	99.50 97	98.64 124	97.39 328	97.52 351	98.12 99	99.95 24	96.90 211	98.71 339	98.38 360

LF4IMVS		97.90 212	97.69 226	98.52 217	99.17 202	97.66 184	97.19 286	99.47 114	96.31 303	97.85 293	98.20 308	96.71 200	99.52 347	94.62 320	99.72 162	98.38 360

testing11		93.08 378	92.02 383	96.26 361	97.56 387	90.83 395	96.32 332	95.70 395	96.47 297	92.66 421	93.73 418	64.36 433	99.59 321	93.77 349	97.57 384	98.37 362

Fast-Effi-MVS+		97.67 235	97.38 247	98.57 207	98.71 290	97.43 198	97.23 278	99.45 121	94.82 349	96.13 376	96.51 376	98.52 61	99.91 64	96.19 270	98.83 331	98.37 362

test0.0.03 1		94.51 352	93.69 362	96.99 334	96.05 425	93.61 349	94.97 390	93.49 413	96.17 306	97.57 312	94.88 411	82.30 392	99.01 406	93.60 352	94.17 424	98.37 362

UWE-MVS		92.38 387	91.76 390	94.21 397	97.16 405	84.65 423	95.42 378	88.45 429	95.96 317	96.17 375	95.84 393	66.36 426	99.71 264	91.87 381	98.64 346	98.28 365

FE-MVS		95.66 333	94.95 346	97.77 280	98.53 328	95.28 289	99.40 16	96.09 388	93.11 380	97.96 284	99.26 117	79.10 404	99.77 233	92.40 377	98.71 339	98.27 366

baseline2		93.73 367	92.83 373	96.42 355	97.70 381	91.28 386	96.84 303	89.77 427	93.96 370	92.44 422	95.93 389	79.14 403	99.77 233	92.94 364	96.76 406	98.21 367

thisisatest0515		94.12 361	93.16 369	96.97 336	98.60 316	92.90 358	93.77 416	90.61 424	94.10 366	96.91 347	95.87 391	74.99 412	99.80 203	94.52 323	99.12 305	98.20 368

EPMVS		93.72 368	93.27 367	95.09 389	96.04 426	87.76 411	98.13 162	85.01 434	94.69 351	96.92 345	98.64 258	78.47 409	99.31 386	95.04 309	96.46 408	98.20 368

dp		93.47 371	93.59 364	93.13 410	96.64 416	81.62 435	97.66 233	96.42 383	92.80 385	96.11 377	98.64 258	78.55 408	99.59 321	93.31 359	92.18 429	98.16 370

CNLPA		97.17 276	96.71 289	98.55 212	98.56 324	98.05 148	96.33 331	98.93 269	96.91 276	97.06 339	97.39 358	94.38 282	99.45 366	91.66 384	99.18 296	98.14 371

dmvs_re		95.98 323	95.39 333	97.74 286	98.86 264	97.45 196	98.37 140	95.69 397	97.95 183	96.56 364	95.95 388	90.70 334	97.68 426	88.32 410	96.13 413	98.11 372

HY-MVS	95.94 13	95.90 325	95.35 335	97.55 305	97.95 366	94.79 303	98.81 91	96.94 373	92.28 391	95.17 396	98.57 269	89.90 340	99.75 246	91.20 394	97.33 397	98.10 373

CostFormer		93.97 363	93.78 361	94.51 393	97.53 391	85.83 419	97.98 189	95.96 390	89.29 414	94.99 399	98.63 260	78.63 406	99.62 309	94.54 322	96.50 407	98.09 374

FA-MVS(test-final)		96.99 289	96.82 282	97.50 310	98.70 294	94.78 304	99.34 20	96.99 369	95.07 342	98.48 243	99.33 100	88.41 353	99.65 300	96.13 276	98.92 328	98.07 375

AdaColmap		97.14 278	96.71 289	98.46 225	98.34 345	97.80 175	96.95 295	98.93 269	95.58 328	96.92 345	97.66 342	95.87 240	99.53 343	90.97 397	99.14 300	98.04 376

KD-MVS_2432*1600		92.87 382	91.99 384	95.51 381	91.37 435	89.27 404	94.07 410	98.14 338	95.42 333	97.25 333	96.44 380	67.86 421	99.24 394	91.28 392	96.08 414	98.02 377

miper_refine_blended		92.87 382	91.99 384	95.51 381	91.37 435	89.27 404	94.07 410	98.14 338	95.42 333	97.25 333	96.44 380	67.86 421	99.24 394	91.28 392	96.08 414	98.02 377

TESTMET0.1,1		92.19 391	91.77 389	93.46 405	96.48 420	82.80 431	94.05 412	91.52 423	94.45 358	94.00 412	94.88 411	66.65 425	99.56 332	95.78 291	98.11 369	98.02 377

testing222		91.96 392	90.37 396	96.72 349	97.47 398	92.59 363	96.11 346	94.76 402	96.83 280	92.90 420	92.87 426	57.92 436	99.55 336	86.93 415	97.52 385	98.00 380

PCF-MVS	92.86 18	94.36 354	93.00 372	98.42 230	98.70 294	97.56 190	93.16 420	99.11 241	79.59 429	97.55 313	97.43 356	92.19 319	99.73 256	79.85 428	99.45 252	97.97 381
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		90.22 396	89.28 399	93.02 411	94.50 432	82.87 430	96.52 319	87.51 430	95.21 340	92.36 423	96.04 385	71.57 416	98.25 422	72.04 432	97.77 381	97.94 382

myMVS_eth3d28		92.92 381	92.31 377	94.77 390	97.84 371	87.59 413	96.19 340	96.11 387	97.08 266	94.27 406	93.49 422	66.07 429	98.78 414	91.78 382	97.93 379	97.92 383

OpenMVS	96.65 7	97.09 280	96.68 291	98.32 241	98.32 346	97.16 217	98.86 86	99.37 149	89.48 412	96.29 374	99.15 145	96.56 205	99.90 71	92.90 365	99.20 291	97.89 384

Gipuma		99.03 69	99.16 54	98.64 191	99.94 2	98.51 104	99.32 23	99.75 38	99.58 29	98.60 226	99.62 37	98.22 87	99.51 352	97.70 158	99.73 154	97.89 384
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PVSNet_0	89.98 21	91.15 395	90.30 398	93.70 403	97.72 376	84.34 427	90.24 426	97.42 355	90.20 409	93.79 415	93.09 424	90.90 333	98.89 412	86.57 417	72.76 433	97.87 386

test-LLR		93.90 364	93.85 359	94.04 398	96.53 418	84.62 424	94.05 412	92.39 418	96.17 306	94.12 409	95.07 405	82.30 392	99.67 284	95.87 286	98.18 363	97.82 387

test-mter		92.33 389	91.76 390	94.04 398	96.53 418	84.62 424	94.05 412	92.39 418	94.00 369	94.12 409	95.07 405	65.63 431	99.67 284	95.87 286	98.18 363	97.82 387

tpm2		93.09 377	92.58 375	94.62 392	97.56 387	86.53 416	97.66 233	95.79 394	86.15 421	94.07 411	98.23 306	75.95 410	99.53 343	90.91 399	96.86 405	97.81 389

CR-MVSNet		96.28 314	95.95 313	97.28 321	97.71 379	94.22 319	98.11 166	98.92 272	92.31 390	96.91 347	99.37 89	85.44 371	99.81 196	97.39 174	97.36 395	97.81 389

RPMNet		97.02 285	96.93 272	97.30 320	97.71 379	94.22 319	98.11 166	99.30 186	99.37 50	96.91 347	99.34 98	86.72 358	99.87 116	97.53 168	97.36 395	97.81 389

tpmrst		95.07 345	95.46 328	93.91 400	97.11 406	84.36 426	97.62 239	96.96 371	94.98 344	96.35 373	98.80 227	85.46 370	99.59 321	95.60 298	96.23 411	97.79 392

PAPM		91.88 394	90.34 397	96.51 352	98.06 363	92.56 364	92.44 423	97.17 364	86.35 420	90.38 427	96.01 386	86.61 359	99.21 397	70.65 433	95.43 418	97.75 393

FPMVS		93.44 372	92.23 379	97.08 330	99.25 179	97.86 165	95.61 369	97.16 365	92.90 383	93.76 416	98.65 255	75.94 411	95.66 430	79.30 429	97.49 386	97.73 394

MAR-MVS		96.47 309	95.70 318	98.79 168	97.92 368	99.12 61	98.28 146	98.60 317	92.16 392	95.54 391	96.17 384	94.77 274	99.52 347	89.62 406	98.23 360	97.72 395
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
ETV-MVS		98.03 203	97.86 216	98.56 211	98.69 299	98.07 144	97.51 255	99.50 97	98.10 175	97.50 318	95.51 397	98.41 69	99.88 98	96.27 266	99.24 283	97.71 396

thres600view7		94.45 353	93.83 360	96.29 359	99.06 227	91.53 379	97.99 188	94.24 409	98.34 149	97.44 324	95.01 407	79.84 398	99.67 284	84.33 420	98.23 360	97.66 397

thres400		94.14 360	93.44 365	96.24 362	98.93 248	91.44 381	97.60 243	94.29 407	97.94 185	97.10 336	94.31 416	79.67 400	99.62 309	83.05 422	98.08 371	97.66 397

IB-MVS	91.63 19	92.24 390	90.90 394	96.27 360	97.22 404	91.24 388	94.36 407	93.33 415	92.37 389	92.24 424	94.58 415	66.20 428	99.89 84	93.16 362	94.63 422	97.66 397
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
tpmvs		95.02 347	95.25 337	94.33 394	96.39 423	85.87 417	98.08 170	96.83 376	95.46 332	95.51 393	98.69 246	85.91 366	99.53 343	94.16 334	96.23 411	97.58 400

cascas		94.79 350	94.33 356	96.15 369	96.02 427	92.36 370	92.34 424	99.26 205	85.34 423	95.08 398	94.96 410	92.96 308	98.53 418	94.41 331	98.59 350	97.56 401

PatchT		96.65 301	96.35 305	97.54 306	97.40 399	95.32 288	97.98 189	96.64 379	99.33 55	96.89 351	99.42 81	84.32 379	99.81 196	97.69 160	97.49 386	97.48 402

TR-MVS		95.55 336	95.12 342	96.86 344	97.54 389	93.94 333	96.49 321	96.53 382	94.36 361	97.03 342	96.61 375	94.26 286	99.16 400	86.91 416	96.31 410	97.47 403

dmvs_testset		92.94 380	92.21 380	95.13 387	98.59 319	90.99 392	97.65 235	92.09 420	96.95 273	94.00 412	93.55 420	92.34 318	96.97 429	72.20 431	92.52 427	97.43 404

MonoMVSNet		96.25 315	96.53 302	95.39 384	96.57 417	91.01 391	98.82 90	97.68 351	98.57 136	98.03 281	99.37 89	90.92 332	97.78 425	94.99 310	93.88 425	97.38 405

JIA-IIPM		95.52 337	95.03 343	97.00 333	96.85 412	94.03 329	96.93 298	95.82 393	99.20 70	94.63 404	99.71 19	83.09 388	99.60 317	94.42 328	94.64 421	97.36 406

BH-w/o		95.13 344	94.89 348	95.86 371	98.20 354	91.31 384	95.65 368	97.37 356	93.64 372	96.52 367	95.70 394	93.04 307	99.02 404	88.10 411	95.82 416	97.24 407

tpm cat1		93.29 374	93.13 371	93.75 402	97.39 400	84.74 422	97.39 264	97.65 352	83.39 426	94.16 408	98.41 288	82.86 390	99.39 375	91.56 388	95.35 419	97.14 408

xiu_mvs_v1_base_debu		97.86 219	98.17 183	96.92 338	98.98 241	93.91 335	96.45 322	99.17 229	97.85 193	98.41 249	97.14 367	98.47 63	99.92 55	98.02 135	99.05 308	96.92 409

xiu_mvs_v1_base		97.86 219	98.17 183	96.92 338	98.98 241	93.91 335	96.45 322	99.17 229	97.85 193	98.41 249	97.14 367	98.47 63	99.92 55	98.02 135	99.05 308	96.92 409

xiu_mvs_v1_base_debi		97.86 219	98.17 183	96.92 338	98.98 241	93.91 335	96.45 322	99.17 229	97.85 193	98.41 249	97.14 367	98.47 63	99.92 55	98.02 135	99.05 308	96.92 409

PMVS	91.26 20	97.86 219	97.94 209	97.65 293	99.71 45	97.94 160	98.52 118	98.68 311	98.99 102	97.52 316	99.35 94	97.41 156	98.18 423	91.59 387	99.67 188	96.82 412
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1314		95.74 330	95.60 322	96.17 366	97.53 391	92.75 362	98.07 172	98.31 331	91.22 401	94.25 407	96.68 373	95.53 249	99.03 403	91.64 386	97.18 399	96.74 413

MVS-HIRNet		94.32 355	95.62 321	90.42 413	98.46 334	75.36 437	96.29 334	89.13 428	95.25 338	95.38 394	99.75 13	92.88 309	99.19 398	94.07 340	99.39 259	96.72 414

OpenMVS_ROB	95.38 14	95.84 328	95.18 341	97.81 277	98.41 342	97.15 218	97.37 267	98.62 316	83.86 424	98.65 218	98.37 293	94.29 285	99.68 281	88.41 409	98.62 349	96.60 415

thres100view900		94.19 358	93.67 363	95.75 375	99.06 227	91.35 383	98.03 178	94.24 409	98.33 150	97.40 326	94.98 409	79.84 398	99.62 309	83.05 422	98.08 371	96.29 416

tfpn200view9		94.03 362	93.44 365	95.78 374	98.93 248	91.44 381	97.60 243	94.29 407	97.94 185	97.10 336	94.31 416	79.67 400	99.62 309	83.05 422	98.08 371	96.29 416

MVS		93.19 376	92.09 381	96.50 353	96.91 410	94.03 329	98.07 172	98.06 342	68.01 431	94.56 405	96.48 378	95.96 236	99.30 388	83.84 421	96.89 404	96.17 418

gg-mvs-nofinetune		92.37 388	91.20 392	95.85 372	95.80 429	92.38 369	99.31 27	81.84 436	99.75 8	91.83 425	99.74 15	68.29 420	99.02 404	87.15 413	97.12 400	96.16 419

xiu_mvs_v2_base		97.16 277	97.49 241	96.17 366	98.54 326	92.46 366	95.45 376	98.84 290	97.25 250	97.48 320	96.49 377	98.31 78	99.90 71	96.34 262	98.68 344	96.15 420

PS-MVSNAJ		97.08 281	97.39 246	96.16 368	98.56 324	92.46 366	95.24 383	98.85 289	97.25 250	97.49 319	95.99 387	98.07 101	99.90 71	96.37 259	98.67 345	96.12 421

E-PMN		94.17 359	94.37 354	93.58 404	96.86 411	85.71 420	90.11 428	97.07 367	98.17 170	97.82 296	97.19 364	84.62 376	98.94 408	89.77 405	97.68 383	96.09 422

EMVS		93.83 365	94.02 357	93.23 409	96.83 413	84.96 421	89.77 429	96.32 384	97.92 187	97.43 325	96.36 383	86.17 363	98.93 409	87.68 412	97.73 382	95.81 423

MVE	83.40 22	92.50 385	91.92 387	94.25 395	98.83 270	91.64 378	92.71 421	83.52 435	95.92 319	86.46 433	95.46 401	95.20 258	95.40 431	80.51 427	98.64 346	95.73 424
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres200		93.72 368	93.14 370	95.46 383	98.66 309	91.29 385	96.61 315	94.63 404	97.39 236	96.83 354	93.71 419	79.88 397	99.56 332	82.40 425	98.13 368	95.54 425

API-MVS		97.04 284	96.91 276	97.42 316	97.88 370	98.23 126	98.18 155	98.50 322	97.57 214	97.39 328	96.75 372	96.77 194	99.15 401	90.16 404	99.02 315	94.88 426

GG-mvs-BLEND				94.76 391	94.54 431	92.13 374	99.31 27	80.47 437		88.73 431	91.01 431	67.59 424	98.16 424	82.30 426	94.53 423	93.98 427

DeepMVS_CX				93.44 406	98.24 351	94.21 321		94.34 406	64.28 432	91.34 426	94.87 413	89.45 344	92.77 433	77.54 430	93.14 426	93.35 428

tmp_tt		78.77 399	78.73 402	78.90 415	58.45 440	74.76 439	94.20 409	78.26 438	39.16 433	86.71 432	92.82 427	80.50 396	75.19 435	86.16 418	92.29 428	86.74 429

dongtai		76.24 400	75.95 403	77.12 416	92.39 434	67.91 440	90.16 427	59.44 441	82.04 427	89.42 429	94.67 414	49.68 439	81.74 434	48.06 434	77.66 432	81.72 430

kuosan		69.30 401	68.95 404	70.34 417	87.68 438	65.00 441	91.11 425	59.90 440	69.02 430	74.46 435	88.89 432	48.58 440	68.03 436	28.61 435	72.33 434	77.99 431

wuyk23d		96.06 319	97.62 234	91.38 412	98.65 313	98.57 98	98.85 87	96.95 372	96.86 279	99.90 13	99.16 141	99.18 18	98.40 419	89.23 408	99.77 135	77.18 432

test123		17.04 404	20.11 407	7.82 418	10.25 442	4.91 443	94.80 393	4.47 443	4.93 436	10.00 438	24.28 435	9.69 441	3.64 437	10.14 436	12.43 436	14.92 433

testmvs		17.12 403	20.53 406	6.87 419	12.05 441	4.20 444	93.62 418	6.73 442	4.62 437	10.41 437	24.33 434	8.28 442	3.56 438	9.69 437	15.07 435	12.86 434

mmdepth		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

monomultidepth		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

test_blank		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

uanet_test		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

DCPMVS		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

cdsmvs_eth3d_5k		24.66 402	32.88 405	0.00 420	0.00 443	0.00 445	0.00 431	99.10 242	0.00 438	0.00 439	97.58 347	99.21 17	0.00 439	0.00 438	0.00 437	0.00 435

pcd_1.5k_mvsjas		8.17 405	10.90 408	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	98.07 101	0.00 439	0.00 438	0.00 437	0.00 435

sosnet-low-res		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

sosnet		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

uncertanet		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

Regformer		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

ab-mvs-re		8.12 406	10.83 409	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	97.48 353	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

uanet		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

WAC-MVS						90.90 393								91.37 391

FOURS1					99.73 36	99.67 3	99.43 12	99.54 88	99.43 44	99.26 124

test_one_0601					99.39 145	99.20 38		99.31 178	98.49 142	98.66 217	99.02 171	97.64 135

eth-test2					0.00 443

eth-test					0.00 443

ZD-MVS					99.01 236	98.84 78		99.07 246	94.10 366	98.05 279	98.12 313	96.36 216	99.86 124	92.70 373	99.19 294

test_241102_ONE					99.49 117	99.17 43		99.31 178	97.98 180	99.66 51	98.90 205	98.36 72	99.48 359

9.14			97.78 219		99.07 222		97.53 252	99.32 173	95.53 330	98.54 237	98.70 244	97.58 140	99.76 239	94.32 333	99.46 250

save fliter					99.11 213	97.97 155	96.53 318	99.02 258	98.24 160

test0726					99.50 110	99.21 32	98.17 158	99.35 159	97.97 181	99.26 124	99.06 159	97.61 138

test_part2					99.36 153	99.10 64				99.05 153

sam_mvs												84.29 381

MTGPA								99.20 217

test_post1							97.59 245				20.48 437	83.07 389	99.66 295	94.16 334

test_post											21.25 436	83.86 384	99.70 268

patchmatchnet-post											98.77 233	84.37 378	99.85 137

MTMP							97.93 193	91.91 422

gm-plane-assit					94.83 430	81.97 433			88.07 418		94.99 408		99.60 317	91.76 383

TEST9					98.71 290	98.08 142	95.96 353	99.03 255	91.40 399	95.85 382	97.53 349	96.52 207	99.76 239

test_8					98.67 304	98.01 150	95.91 359	99.02 258	91.64 394	95.79 384	97.50 352	96.47 209	99.76 239

agg_prior					98.68 303	97.99 151		99.01 261		95.59 385			99.77 233

test_prior4						97.97 155	95.86 360

test_prior2							95.74 366		96.48 296	96.11 377	97.63 345	95.92 239		94.16 334	99.20 291

旧先验2							95.76 365		88.56 417	97.52 316			99.66 295	94.48 324

新几何2							95.93 356

原ACMM2							95.53 372

testdata2													99.79 216	92.80 370

segment_acmp												97.02 179

testdata1							95.44 377		96.32 302

plane_prior7					99.19 194	97.87 164

plane_prior6					98.99 240	97.70 183						94.90 265

plane_prior4											97.98 324

plane_prior3						97.78 176			97.41 234	97.79 297

plane_prior2							97.77 217		98.20 167

plane_prior1					99.05 230

plane_prior						97.65 185	97.07 290		96.72 286						99.36 263

n2								0.00 444

nn								0.00 444

door-mid								99.57 73

test11								98.87 281

door								99.41 138

HQP5-MVS						96.79 235

HQP-NCC					98.67 304		96.29 334		96.05 311	95.55 388

ACMP_Plane					98.67 304		96.29 334		96.05 311	95.55 388

BP-MVS														92.82 368

HQP3-MVS								99.04 253							99.26 281

HQP2-MVS												93.84 293

NP-MVS					98.84 268	97.39 200					96.84 370

MDTV_nov1_ep13			95.22 339		97.06 409	83.20 429	97.74 223	96.16 385	94.37 360	96.99 343	98.83 221	83.95 383	99.53 343	93.90 343	97.95 378

ACMMP++_ref															99.77 135

ACMMP++															99.68 182

Test By Simon												96.52 207

High-resolution multi-view benchmark