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.

test_fmvsmconf0.01_n		99.57 7	99.63 7	99.36 63	99.87 12	98.13 130	98.08 161	99.95 1	99.45 36	99.98 2	99.75 11	99.80 1	99.97 4	99.82 8	99.99 5	99.99 1

fmvsm_s_conf0.1_n_a		99.17 42	99.30 32	98.80 159	99.75 34	96.59 235	97.97 181	99.86 13	98.22 142	99.88 17	99.71 17	98.59 50	99.84 136	99.73 19	99.98 12	99.98 2

fmvsm_s_conf0.1_n		99.16 45	99.33 26	98.64 183	99.71 46	96.10 249	97.87 193	99.85 15	98.56 123	99.90 12	99.68 20	98.69 41	99.85 119	99.72 21	99.98 12	99.97 3

test_fmvs3		99.12 52	99.41 19	98.25 235	99.76 30	95.07 286	99.05 64	99.94 2	97.78 177	99.82 22	99.84 2	98.56 54	99.71 248	99.96 1	99.96 24	99.97 3

test_fmvsmconf0.1_n		99.49 12	99.54 10	99.34 72	99.78 24	98.11 131	97.77 204	99.90 9	99.33 50	99.97 3	99.66 27	99.71 3	99.96 11	99.79 13	99.99 5	99.96 5

test_f		98.67 114	98.87 71	98.05 252	99.72 43	95.59 264	98.51 118	99.81 23	96.30 282	99.78 27	99.82 4	96.14 207	98.63 396	99.82 8	99.93 43	99.95 6

test_fmvs2		98.70 103	98.97 66	97.89 259	99.54 98	94.05 312	98.55 109	99.92 6	96.78 260	99.72 32	99.78 8	96.60 189	99.67 268	99.91 2	99.90 68	99.94 7

PS-MVSNAJss		99.46 14	99.49 12	99.35 69	99.90 4	98.15 127	99.20 45	99.65 45	99.48 32	99.92 8	99.71 17	98.07 88	99.96 11	99.53 30	100.00 1	99.93 8

test_vis3_rt		99.14 46	99.17 44	99.07 119	99.78 24	98.38 108	98.92 77	99.94 2	97.80 175	99.91 11	99.67 25	97.15 156	98.91 391	99.76 16	99.56 211	99.92 9

fmvsm_s_conf0.5_n_a		99.10 54	99.20 42	98.78 165	99.55 93	96.59 235	97.79 201	99.82 22	98.21 143	99.81 24	99.53 54	98.46 60	99.84 136	99.70 22	99.97 19	99.90 10

fmvsm_s_conf0.5_n		99.09 55	99.26 38	98.61 191	99.55 93	96.09 252	97.74 209	99.81 23	98.55 124	99.85 19	99.55 48	98.60 49	99.84 136	99.69 24	99.98 12	99.89 11

test_fmvsmconf_n		99.44 15	99.48 14	99.31 82	99.64 69	98.10 133	97.68 215	99.84 18	99.29 55	99.92 8	99.57 42	99.60 5	99.96 11	99.74 18	99.98 12	99.89 11

test_djsdf		99.52 10	99.51 11	99.53 33	99.86 14	98.74 81	99.39 17	99.56 69	99.11 73	99.70 36	99.73 15	99.00 22	99.97 4	99.26 42	99.98 12	99.89 11

mvs_tets		99.63 5	99.67 5	99.49 47	99.88 9	98.61 91	99.34 20	99.71 33	99.27 57	99.90 12	99.74 13	99.68 4	99.97 4	99.55 29	99.99 5	99.88 14

jajsoiax		99.58 6	99.61 8	99.48 50	99.87 12	98.61 91	99.28 37	99.66 44	99.09 83	99.89 15	99.68 20	99.53 7	99.97 4	99.50 32	99.99 5	99.87 15

EU-MVSNet		97.66 224	98.50 121	95.13 367	99.63 73	85.84 397	98.35 137	98.21 317	98.23 141	99.54 56	99.46 66	95.02 252	99.68 265	98.24 106	99.87 76	99.87 15

UA-Net		99.47 13	99.40 20	99.70 2	99.49 115	99.29 19	99.80 3	99.72 32	99.82 3	99.04 142	99.81 5	98.05 91	99.96 11	98.85 69	99.99 5	99.86 17

MM		98.22 176	97.99 190	98.91 146	98.66 293	96.97 221	97.89 189	94.44 382	99.54 28	98.95 156	99.14 130	93.50 288	99.92 50	99.80 12	99.96 24	99.85 18

MVS_0304		98.10 185	97.88 202	98.76 169	98.82 258	96.50 240	97.90 187	91.35 400	99.56 27	98.32 239	99.13 131	96.06 211	99.93 40	99.84 7	99.97 19	99.85 18

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 10	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 11	100.00 1	99.85 18

fmvsm_l_conf0.5_n_a		99.19 41	99.27 36	98.94 141	99.65 64	97.05 217	97.80 200	99.76 28	98.70 111	99.78 27	99.11 134	98.79 34	99.95 23	99.85 5	99.96 24	99.83 21

fmvsm_l_conf0.5_n		99.21 39	99.28 34	99.02 131	99.64 69	97.28 202	97.82 197	99.76 28	98.73 108	99.82 22	99.09 140	98.81 32	99.95 23	99.86 4	99.96 24	99.83 21

mvsany_test3		98.87 78	98.92 68	98.74 177	99.38 140	96.94 225	98.58 106	99.10 229	96.49 272	99.96 4	99.81 5	98.18 80	99.45 347	98.97 63	99.79 115	99.83 21

SSC-MVS		98.71 99	98.74 83	98.62 188	99.72 43	96.08 254	98.74 88	98.64 299	99.74 7	99.67 42	99.24 105	94.57 266	99.95 23	99.11 52	99.24 268	99.82 24

anonymousdsp		99.51 11	99.47 16	99.62 6	99.88 9	99.08 63	99.34 20	99.69 36	98.93 98	99.65 46	99.72 16	98.93 26	99.95 23	99.11 52	100.00 1	99.82 24

ANet_high		99.57 7	99.67 5	99.28 84	99.89 6	98.09 134	99.14 54	99.93 4	99.82 3	99.93 6	99.81 5	99.17 18	99.94 35	99.31 40	100.00 1	99.82 24

PS-CasMVS		99.40 21	99.33 26	99.62 6	99.71 46	99.10 60	99.29 33	99.53 81	99.53 30	99.46 71	99.41 76	98.23 73	99.95 23	98.89 68	99.95 31	99.81 27

FC-MVSNet-test		99.27 30	99.25 39	99.34 72	99.77 27	98.37 110	99.30 32	99.57 62	99.61 23	99.40 83	99.50 59	97.12 157	99.85 119	99.02 60	99.94 39	99.80 28

test_cas_vis1_n_1920		98.33 162	98.68 95	97.27 306	99.69 55	92.29 355	98.03 169	99.85 15	97.62 186	99.96 4	99.62 34	93.98 281	99.74 235	99.52 31	99.86 79	99.79 29

test_vis1_n_1920		98.40 152	98.92 68	96.81 329	99.74 36	90.76 378	98.15 153	99.91 7	98.33 131	99.89 15	99.55 48	95.07 251	99.88 83	99.76 16	99.93 43	99.79 29

CP-MVSNet		99.21 39	99.09 56	99.56 21	99.65 64	98.96 70	99.13 55	99.34 153	99.42 41	99.33 96	99.26 100	97.01 165	99.94 35	98.74 76	99.93 43	99.79 29

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 15	99.69 5	99.58 55	99.90 2	99.86 18	99.78 8	99.58 6	99.95 23	99.00 61	99.95 31	99.78 32

CVMVSNet		96.25 299	97.21 245	93.38 385	99.10 202	80.56 412	97.20 265	98.19 320	96.94 251	99.00 147	99.02 153	89.50 328	99.80 185	96.36 241	99.59 199	99.78 32

Anonymous20231211		99.27 30	99.27 36	99.26 89	99.29 158	98.18 125	99.49 9	99.51 86	99.70 9	99.80 25	99.68 20	96.84 172	99.83 154	99.21 47	99.91 62	99.77 34

PEN-MVS		99.41 20	99.34 25	99.62 6	99.73 37	99.14 52	99.29 33	99.54 78	99.62 21	99.56 53	99.42 73	98.16 84	99.96 11	98.78 72	99.93 43	99.77 34

WR-MVS_H		99.33 26	99.22 41	99.65 5	99.71 46	99.24 25	99.32 23	99.55 73	99.46 35	99.50 67	99.34 87	97.30 146	99.93 40	98.90 66	99.93 43	99.77 34

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 21	99.85 16	99.11 59	99.90 1	99.78 26	99.63 18	99.78 27	99.67 25	99.48 9	99.81 178	99.30 41	99.97 19	99.77 34
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		98.52 140	98.55 114	98.43 218	99.65 64	95.59 264	98.52 113	98.77 286	99.65 15	99.52 62	99.00 166	94.34 272	99.93 40	98.65 83	98.83 312	99.76 38

patch_mono-2		98.51 141	98.63 102	98.17 241	99.38 140	94.78 291	97.36 251	99.69 36	98.16 153	98.49 226	99.29 95	97.06 160	99.97 4	98.29 105	99.91 62	99.76 38

nrg030		99.40 21	99.35 23	99.54 26	99.58 77	99.13 55	98.98 71	99.48 98	99.68 12	99.46 71	99.26 100	98.62 47	99.73 240	99.17 50	99.92 54	99.76 38

FIs		99.14 46	99.09 56	99.29 83	99.70 53	98.28 116	99.13 55	99.52 84	99.48 32	99.24 116	99.41 76	96.79 178	99.82 164	98.69 81	99.88 73	99.76 38

v7n		99.53 9	99.57 9	99.41 59	99.88 9	98.54 99	99.45 10	99.61 51	99.66 14	99.68 40	99.66 27	98.44 61	99.95 23	99.73 19	99.96 24	99.75 42

APDe-MVS		98.99 62	98.79 80	99.60 11	99.21 174	99.15 47	98.87 81	99.48 98	97.57 192	99.35 93	99.24 105	97.83 104	99.89 74	97.88 132	99.70 160	99.75 42
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DTE-MVSNet		99.43 18	99.35 23	99.66 4	99.71 46	99.30 17	99.31 27	99.51 86	99.64 16	99.56 53	99.46 66	98.23 73	99.97 4	98.78 72	99.93 43	99.72 44

MSC_two_6792asdad				99.32 79	98.43 321	98.37 110		98.86 271					99.89 74	97.14 172	99.60 195	99.71 45

No_MVS				99.32 79	98.43 321	98.37 110		98.86 271					99.89 74	97.14 172	99.60 195	99.71 45

PMMVS2		98.07 191	98.08 182	98.04 253	99.41 137	94.59 300	94.59 378	99.40 129	97.50 201	98.82 184	98.83 206	96.83 174	99.84 136	97.50 153	99.81 100	99.71 45

Baseline_NR-MVSNet		98.98 65	98.86 74	99.36 63	99.82 20	98.55 96	97.47 245	99.57 62	99.37 45	99.21 119	99.61 37	96.76 181	99.83 154	98.06 119	99.83 91	99.71 45

XXY-MVS		99.14 46	99.15 51	99.10 113	99.76 30	97.74 175	98.85 84	99.62 48	98.48 126	99.37 89	99.49 63	98.75 36	99.86 107	98.20 109	99.80 110	99.71 45

test_0728_THIRD									98.17 150	99.08 133	99.02 153	97.89 101	99.88 83	97.07 178	99.71 155	99.70 50

MSP-MVS		98.40 152	98.00 189	99.61 9	99.57 81	99.25 24	98.57 107	99.35 147	97.55 198	99.31 104	97.71 319	94.61 265	99.88 83	96.14 254	99.19 277	99.70 50
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
mvsmamba		99.24 37	99.15 51	99.49 47	99.83 18	98.85 74	99.41 13	99.55 73	99.54 28	99.40 83	99.52 57	95.86 226	99.91 59	99.32 39	99.95 31	99.70 50

dcpmvs_2		98.78 90	99.11 53	97.78 266	99.56 89	93.67 330	99.06 62	99.86 13	99.50 31	99.66 43	99.26 100	97.21 154	99.99 2	98.00 124	99.91 62	99.68 53

test_0728_SECOND				99.60 11	99.50 108	99.23 26	98.02 171	99.32 160					99.88 83	96.99 184	99.63 185	99.68 53

OurMVSNet-221017-0		99.37 24	99.31 30	99.53 33	99.91 3	98.98 65	99.63 7	99.58 55	99.44 38	99.78 27	99.76 10	96.39 197	99.92 50	99.44 35	99.92 54	99.68 53

CHOSEN 1792x2688		97.49 234	97.14 250	98.54 206	99.68 57	96.09 252	96.50 300	99.62 48	91.58 372	98.84 180	98.97 173	92.36 305	99.88 83	96.76 207	99.95 31	99.67 56

IU-MVS					99.49 115	99.15 47		98.87 266	92.97 357	99.41 80				96.76 207	99.62 188	99.66 57

test_241102_TWO								99.30 173	98.03 158	99.26 111	99.02 153	97.51 134	99.88 83	96.91 190	99.60 195	99.66 57

DPE-MVS		98.59 127	98.26 160	99.57 16	99.27 161	99.15 47	97.01 273	99.39 131	97.67 182	99.44 75	98.99 167	97.53 131	99.89 74	95.40 284	99.68 168	99.66 57
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TransMVSNet (Re)		99.44 15	99.47 16	99.36 63	99.80 21	98.58 94	99.27 39	99.57 62	99.39 43	99.75 31	99.62 34	99.17 18	99.83 154	99.06 56	99.62 188	99.66 57

EI-MVSNet-UG-set		98.69 106	98.71 89	98.62 188	99.10 202	96.37 243	97.23 261	98.87 266	99.20 64	99.19 121	98.99 167	97.30 146	99.85 119	98.77 75	99.79 115	99.65 61

pmmvs6		99.67 3	99.70 3	99.60 11	99.90 4	99.27 22	99.53 8	99.76 28	99.64 16	99.84 21	99.83 3	99.50 8	99.87 100	99.36 37	99.92 54	99.64 62

EI-MVSNet-Vis-set		98.68 111	98.70 92	98.63 187	99.09 205	96.40 242	97.23 261	98.86 271	99.20 64	99.18 125	98.97 173	97.29 148	99.85 119	98.72 78	99.78 120	99.64 62

ACMH	96.65 7	99.25 33	99.24 40	99.26 89	99.72 43	98.38 108	99.07 61	99.55 73	98.30 134	99.65 46	99.45 70	99.22 15	99.76 223	98.44 96	99.77 125	99.64 62
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DP-MVS		98.93 71	98.81 79	99.28 84	99.21 174	98.45 105	98.46 126	99.33 158	99.63 18	99.48 68	99.15 127	97.23 152	99.75 230	97.17 168	99.66 179	99.63 65

test_fmvs1_n		98.09 188	98.28 156	97.52 292	99.68 57	93.47 334	98.63 100	99.93 4	95.41 313	99.68 40	99.64 31	91.88 312	99.48 340	99.82 8	99.87 76	99.62 66

bld_raw_dy_0_64		99.14 46	99.28 34	98.72 178	99.62 75	96.58 238	99.72 4	99.64 46	99.82 3	99.85 19	99.64 31	96.17 206	99.96 11	99.13 51	99.92 54	99.62 66

test1111		96.49 292	96.82 266	95.52 361	99.42 135	87.08 394	99.22 42	87.14 407	99.11 73	99.46 71	99.58 41	88.69 332	99.86 107	98.80 71	99.95 31	99.62 66

VPA-MVSNet		99.30 28	99.30 32	99.28 84	99.49 115	98.36 113	99.00 68	99.45 112	99.63 18	99.52 62	99.44 71	98.25 71	99.88 83	99.09 54	99.84 84	99.62 66

LPG-MVS_test		98.71 99	98.46 130	99.47 53	99.57 81	98.97 66	98.23 144	99.48 98	96.60 267	99.10 131	99.06 141	98.71 39	99.83 154	95.58 280	99.78 120	99.62 66

LGP-MVS_train				99.47 53	99.57 81	98.97 66		99.48 98	96.60 267	99.10 131	99.06 141	98.71 39	99.83 154	95.58 280	99.78 120	99.62 66

Test_1112_low_res		96.99 273	96.55 284	98.31 230	99.35 151	95.47 271	95.84 340	99.53 81	91.51 374	96.80 336	98.48 264	91.36 315	99.83 154	96.58 220	99.53 220	99.62 66

v10		98.97 66	99.11 53	98.55 203	99.44 129	96.21 248	98.90 78	99.55 73	98.73 108	99.48 68	99.60 39	96.63 188	99.83 154	99.70 22	99.99 5	99.61 73

test_vis1_n		98.31 165	98.50 121	97.73 275	99.76 30	94.17 310	98.68 97	99.91 7	96.31 280	99.79 26	99.57 42	92.85 300	99.42 352	99.79 13	99.84 84	99.60 74

v8		99.01 60	99.16 46	98.57 198	99.47 124	96.31 246	98.90 78	99.47 106	99.03 89	99.52 62	99.57 42	96.93 168	99.81 178	99.60 25	99.98 12	99.60 74

EI-MVSNet		98.40 152	98.51 119	98.04 253	99.10 202	94.73 294	97.20 265	98.87 266	98.97 94	99.06 135	99.02 153	96.00 215	99.80 185	98.58 86	99.82 95	99.60 74

SixPastTwentyTwo		98.75 95	98.62 104	99.16 104	99.83 18	97.96 154	99.28 37	98.20 318	99.37 45	99.70 36	99.65 30	92.65 303	99.93 40	99.04 58	99.84 84	99.60 74

IterMVS-LS		98.55 133	98.70 92	98.09 245	99.48 122	94.73 294	97.22 264	99.39 131	98.97 94	99.38 87	99.31 93	96.00 215	99.93 40	98.58 86	99.97 19	99.60 74
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		97.19 258	96.60 282	98.96 138	99.62 75	97.28 202	95.17 360	99.50 89	94.21 339	99.01 146	98.32 280	86.61 344	99.99 2	97.10 176	99.84 84	99.60 74

ACMMP_NAP		98.75 95	98.48 126	99.57 16	99.58 77	99.29 19	97.82 197	99.25 193	96.94 251	98.78 187	99.12 133	98.02 92	99.84 136	97.13 174	99.67 174	99.59 80

VPNet		98.87 78	98.83 76	99.01 132	99.70 53	97.62 185	98.43 129	99.35 147	99.47 34	99.28 105	99.05 148	96.72 184	99.82 164	98.09 116	99.36 248	99.59 80

WR-MVS		98.40 152	98.19 168	99.03 129	99.00 222	97.65 181	96.85 283	98.94 253	98.57 121	98.89 170	98.50 261	95.60 232	99.85 119	97.54 150	99.85 80	99.59 80

HPM-MVS		98.79 88	98.53 117	99.59 15	99.65 64	99.29 19	99.16 51	99.43 122	96.74 262	98.61 209	98.38 272	98.62 47	99.87 100	96.47 234	99.67 174	99.59 80
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EG-PatchMatch MVS		98.99 62	99.01 62	98.94 141	99.50 108	97.47 191	98.04 168	99.59 53	98.15 154	99.40 83	99.36 82	98.58 53	99.76 223	98.78 72	99.68 168	99.59 80

Vis-MVSNet		99.34 25	99.36 22	99.27 87	99.73 37	98.26 117	99.17 50	99.78 26	99.11 73	99.27 107	99.48 64	98.82 31	99.95 23	98.94 64	99.93 43	99.59 80
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MP-MVS-pluss		98.57 128	98.23 164	99.60 11	99.69 55	99.35 12	97.16 268	99.38 133	94.87 324	98.97 153	98.99 167	98.01 93	99.88 83	97.29 162	99.70 160	99.58 86
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		98.69 106	98.40 138	99.54 26	99.53 101	99.17 39	98.52 113	99.31 165	97.46 209	98.44 230	98.51 257	97.83 104	99.88 83	96.46 235	99.58 204	99.58 86

ACMMPR		98.70 103	98.42 136	99.54 26	99.52 103	99.14 52	98.52 113	99.31 165	97.47 204	98.56 218	98.54 253	97.75 111	99.88 83	96.57 222	99.59 199	99.58 86

PGM-MVS		98.66 115	98.37 145	99.55 23	99.53 101	99.18 38	98.23 144	99.49 96	97.01 248	98.69 198	98.88 197	98.00 94	99.89 74	95.87 266	99.59 199	99.58 86

SteuartSystems-ACMMP		98.79 88	98.54 116	99.54 26	99.73 37	99.16 43	98.23 144	99.31 165	97.92 166	98.90 168	98.90 189	98.00 94	99.88 83	96.15 253	99.72 150	99.58 86
Skip Steuart: Steuart Systems R&D Blog.
SDMVSNet		99.23 38	99.32 28	98.96 138	99.68 57	97.35 198	98.84 86	99.48 98	99.69 10	99.63 49	99.68 20	99.03 21	99.96 11	97.97 126	99.92 54	99.57 91

sd_testset		99.28 29	99.31 30	99.19 100	99.68 57	98.06 143	99.41 13	99.30 173	99.69 10	99.63 49	99.68 20	99.25 14	99.96 11	97.25 165	99.92 54	99.57 91

TranMVSNet+NR-MVSNet		99.17 42	99.07 59	99.46 55	99.37 146	98.87 73	98.39 133	99.42 125	99.42 41	99.36 91	99.06 141	98.38 64	99.95 23	98.34 101	99.90 68	99.57 91

mPP-MVS		98.64 118	98.34 149	99.54 26	99.54 98	99.17 39	98.63 100	99.24 198	97.47 204	98.09 256	98.68 231	97.62 122	99.89 74	96.22 248	99.62 188	99.57 91

PVSNet_Blended_VisFu		98.17 183	98.15 174	98.22 238	99.73 37	95.15 282	97.36 251	99.68 41	94.45 334	98.99 148	99.27 98	96.87 171	99.94 35	97.13 174	99.91 62	99.57 91

1112_ss		97.29 250	96.86 262	98.58 195	99.34 153	96.32 245	96.75 289	99.58 55	93.14 355	96.89 331	97.48 333	92.11 309	99.86 107	96.91 190	99.54 216	99.57 91

MTAPA		98.88 77	98.64 101	99.61 9	99.67 61	99.36 11	98.43 129	99.20 204	98.83 107	98.89 170	98.90 189	96.98 167	99.92 50	97.16 169	99.70 160	99.56 97

XVS		98.72 98	98.45 131	99.53 33	99.46 125	99.21 28	98.65 98	99.34 153	98.62 116	97.54 295	98.63 243	97.50 135	99.83 154	96.79 203	99.53 220	99.56 97

pm-mvs1		99.44 15	99.48 14	99.33 77	99.80 21	98.63 88	99.29 33	99.63 47	99.30 54	99.65 46	99.60 39	99.16 20	99.82 164	99.07 55	99.83 91	99.56 97

X-MVStestdata		94.32 335	92.59 353	99.53 33	99.46 125	99.21 28	98.65 98	99.34 153	98.62 116	97.54 295	45.85 409	97.50 135	99.83 154	96.79 203	99.53 220	99.56 97

HPM-MVS_fast		99.01 60	98.82 77	99.57 16	99.71 46	99.35 12	99.00 68	99.50 89	97.33 220	98.94 164	98.86 200	98.75 36	99.82 164	97.53 151	99.71 155	99.56 97

K. test v3		98.00 196	97.66 218	99.03 129	99.79 23	97.56 187	99.19 49	92.47 394	99.62 21	99.52 62	99.66 27	89.61 326	99.96 11	99.25 44	99.81 100	99.56 97

CP-MVS		98.70 103	98.42 136	99.52 38	99.36 147	99.12 57	98.72 92	99.36 142	97.54 199	98.30 240	98.40 269	97.86 103	99.89 74	96.53 231	99.72 150	99.56 97

ZNCC-MVS		98.68 111	98.40 138	99.54 26	99.57 81	99.21 28	98.46 126	99.29 181	97.28 226	98.11 254	98.39 270	98.00 94	99.87 100	96.86 200	99.64 182	99.55 104

v1192		98.60 125	98.66 98	98.41 220	99.27 161	95.88 258	97.52 238	99.36 142	97.41 213	99.33 96	99.20 112	96.37 200	99.82 164	99.57 27	99.92 54	99.55 104

v1240		98.55 133	98.62 104	98.32 227	99.22 172	95.58 266	97.51 240	99.45 112	97.16 241	99.45 74	99.24 105	96.12 209	99.85 119	99.60 25	99.88 73	99.55 104

UGNet		98.53 137	98.45 131	98.79 162	97.94 351	96.96 223	99.08 58	98.54 303	99.10 80	96.82 335	99.47 65	96.55 191	99.84 136	98.56 92	99.94 39	99.55 104
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
test2506		92.39 363	91.89 365	93.89 379	99.38 140	82.28 409	99.32 23	66.03 415	99.08 85	98.77 190	99.57 42	66.26 405	99.84 136	98.71 79	99.95 31	99.54 108

ECVR-MVS		96.42 294	96.61 280	95.85 353	99.38 140	88.18 390	99.22 42	86.00 409	99.08 85	99.36 91	99.57 42	88.47 337	99.82 164	98.52 93	99.95 31	99.54 108

v144192		98.54 135	98.57 112	98.45 216	99.21 174	95.98 255	97.63 223	99.36 142	97.15 243	99.32 102	99.18 117	95.84 227	99.84 136	99.50 32	99.91 62	99.54 108

v1921920		98.54 135	98.60 109	98.38 223	99.20 178	95.76 263	97.56 233	99.36 142	97.23 235	99.38 87	99.17 121	96.02 213	99.84 136	99.57 27	99.90 68	99.54 108

MP-MVS		98.46 145	98.09 179	99.54 26	99.57 81	99.22 27	98.50 120	99.19 208	97.61 189	97.58 291	98.66 236	97.40 142	99.88 83	94.72 298	99.60 195	99.54 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MIMVSNet1		99.38 23	99.32 28	99.55 23	99.86 14	99.19 37	99.41 13	99.59 53	99.59 24	99.71 34	99.57 42	97.12 157	99.90 64	99.21 47	99.87 76	99.54 108

ACMMP		98.75 95	98.50 121	99.52 38	99.56 89	99.16 43	98.87 81	99.37 138	97.16 241	98.82 184	99.01 162	97.71 113	99.87 100	96.29 245	99.69 163	99.54 108
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		98.40 152	98.03 186	99.51 42	99.16 191	99.21 28	98.05 166	99.22 201	94.16 340	98.98 149	99.10 137	97.52 133	99.79 200	96.45 236	99.64 182	99.53 115
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 99	98.44 133	99.51 42	99.49 115	99.16 43	98.52 113	99.31 165	97.47 204	98.58 215	98.50 261	97.97 98	99.85 119	96.57 222	99.59 199	99.53 115

UniMVSNet_NR-MVSNet		98.86 81	98.68 95	99.40 61	99.17 189	98.74 81	97.68 215	99.40 129	99.14 72	99.06 135	98.59 249	96.71 185	99.93 40	98.57 89	99.77 125	99.53 115

GST-MVS		98.61 124	98.30 154	99.52 38	99.51 105	99.20 34	98.26 142	99.25 193	97.44 212	98.67 200	98.39 270	97.68 114	99.85 119	96.00 258	99.51 225	99.52 118

TDRefinement		99.42 19	99.38 21	99.55 23	99.76 30	99.33 16	99.68 6	99.71 33	99.38 44	99.53 60	99.61 37	98.64 44	99.80 185	98.24 106	99.84 84	99.52 118

v1144		98.60 125	98.66 98	98.41 220	99.36 147	95.90 257	97.58 230	99.34 153	97.51 200	99.27 107	99.15 127	96.34 202	99.80 185	99.47 34	99.93 43	99.51 120

v2v482		98.56 129	98.62 104	98.37 224	99.42 135	95.81 261	97.58 230	99.16 219	97.90 168	99.28 105	99.01 162	95.98 220	99.79 200	99.33 38	99.90 68	99.51 120

CPTT-MVS		97.84 213	97.36 237	99.27 87	99.31 154	98.46 104	98.29 139	99.27 187	94.90 323	97.83 275	98.37 273	94.90 254	99.84 136	93.85 326	99.54 216	99.51 120

DU-MVS		98.82 84	98.63 102	99.39 62	99.16 191	98.74 81	97.54 235	99.25 193	98.84 106	99.06 135	98.76 219	96.76 181	99.93 40	98.57 89	99.77 125	99.50 123

NR-MVSNet		98.95 69	98.82 77	99.36 63	99.16 191	98.72 86	99.22 42	99.20 204	99.10 80	99.72 32	98.76 219	96.38 199	99.86 107	98.00 124	99.82 95	99.50 123

casdiffmvs_mvg		99.12 52	99.16 46	98.99 134	99.43 134	97.73 177	98.00 175	99.62 48	99.22 60	99.55 55	99.22 109	98.93 26	99.75 230	98.66 82	99.81 100	99.50 123
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 57	99.00 63	99.33 77	99.71 46	98.83 76	98.60 104	99.58 55	99.11 73	99.53 60	99.18 117	98.81 32	99.67 268	96.71 214	99.77 125	99.50 123

DVP-MVS++		98.90 75	98.70 92	99.51 42	98.43 321	99.15 47	99.43 11	99.32 160	98.17 150	99.26 111	99.02 153	98.18 80	99.88 83	97.07 178	99.45 237	99.49 127

PC_three_1452									93.27 353	99.40 83	98.54 253	98.22 76	97.00 404	95.17 287	99.45 237	99.49 127

GeoE		99.05 58	98.99 65	99.25 92	99.44 129	98.35 114	98.73 91	99.56 69	98.42 127	98.91 167	98.81 211	98.94 25	99.91 59	98.35 100	99.73 143	99.49 127

h-mvs33		97.77 216	97.33 240	99.10 113	99.21 174	97.84 163	98.35 137	98.57 302	99.11 73	98.58 215	99.02 153	88.65 335	99.96 11	98.11 114	96.34 386	99.49 127

IterMVS-SCA-FT		97.85 212	98.18 169	96.87 325	99.27 161	91.16 373	95.53 348	99.25 193	99.10 80	99.41 80	99.35 83	93.10 293	99.96 11	98.65 83	99.94 39	99.49 127

new-patchmatchnet		98.35 158	98.74 83	97.18 309	99.24 167	92.23 357	96.42 305	99.48 98	98.30 134	99.69 38	99.53 54	97.44 140	99.82 164	98.84 70	99.77 125	99.49 127

APD-MVS		98.10 185	97.67 215	99.42 57	99.11 200	98.93 71	97.76 207	99.28 184	94.97 321	98.72 196	98.77 217	97.04 161	99.85 119	93.79 327	99.54 216	99.49 127
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EPP-MVSNet		98.30 166	98.04 185	99.07 119	99.56 89	97.83 164	99.29 33	98.07 324	99.03 89	98.59 213	99.13 131	92.16 308	99.90 64	96.87 198	99.68 168	99.49 127

DeepC-MVS	97.60 4	98.97 66	98.93 67	99.10 113	99.35 151	97.98 150	98.01 174	99.46 108	97.56 196	99.54 56	99.50 59	98.97 23	99.84 136	98.06 119	99.92 54	99.49 127
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 73	98.73 85	99.48 50	99.55 93	99.14 52	98.07 163	99.37 138	97.62 186	99.04 142	98.96 176	98.84 30	99.79 200	97.43 156	99.65 180	99.49 127
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DVP-MVS		98.77 93	98.52 118	99.52 38	99.50 108	99.21 28	98.02 171	98.84 275	97.97 161	99.08 133	99.02 153	97.61 123	99.88 83	96.99 184	99.63 185	99.48 137
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 99	98.43 134	99.57 16	99.18 188	99.35 12	98.36 136	99.29 181	98.29 137	98.88 173	98.85 203	97.53 131	99.87 100	96.14 254	99.31 256	99.48 137

TSAR-MVS + MP.		98.63 120	98.49 125	99.06 125	99.64 69	97.90 158	98.51 118	98.94 253	96.96 249	99.24 116	98.89 195	97.83 104	99.81 178	96.88 197	99.49 233	99.48 137
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
VDDNet		98.21 178	97.95 193	99.01 132	99.58 77	97.74 175	99.01 66	97.29 343	99.67 13	98.97 153	99.50 59	90.45 321	99.80 185	97.88 132	99.20 274	99.48 137

IterMVS		97.73 218	98.11 178	96.57 334	99.24 167	90.28 381	95.52 350	99.21 202	98.86 103	99.33 96	99.33 89	93.11 292	99.94 35	98.49 94	99.94 39	99.48 137
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IS-MVSNet		98.19 180	97.90 199	99.08 117	99.57 81	97.97 151	99.31 27	98.32 313	99.01 91	98.98 149	99.03 152	91.59 313	99.79 200	95.49 282	99.80 110	99.48 137

ACMP	95.32 15	98.41 150	98.09 179	99.36 63	99.51 105	98.79 79	97.68 215	99.38 133	95.76 300	98.81 186	98.82 209	98.36 65	99.82 164	94.75 295	99.77 125	99.48 137
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MCST-MVS		98.00 196	97.63 221	99.10 113	99.24 167	98.17 126	96.89 282	98.73 293	95.66 301	97.92 266	97.70 321	97.17 155	99.66 279	96.18 252	99.23 270	99.47 144

3Dnovator+	97.89 3	98.69 106	98.51 119	99.24 94	98.81 261	98.40 106	99.02 65	99.19 208	98.99 92	98.07 257	99.28 96	97.11 159	99.84 136	96.84 201	99.32 254	99.47 144

HPM-MVS++		98.10 185	97.64 220	99.48 50	99.09 205	99.13 55	97.52 238	98.75 290	97.46 209	96.90 330	97.83 314	96.01 214	99.84 136	95.82 270	99.35 250	99.46 146

V42		98.78 90	98.78 81	98.76 169	99.44 129	97.04 218	98.27 141	99.19 208	97.87 170	99.25 115	99.16 123	96.84 172	99.78 211	99.21 47	99.84 84	99.46 146

APD-MVS_3200maxsize		98.84 82	98.61 108	99.53 33	99.19 181	99.27 22	98.49 121	99.33 158	98.64 112	99.03 145	98.98 171	97.89 101	99.85 119	96.54 230	99.42 241	99.46 146

UniMVSNet (Re)		98.87 78	98.71 89	99.35 69	99.24 167	98.73 84	97.73 211	99.38 133	98.93 98	99.12 127	98.73 222	96.77 179	99.86 107	98.63 85	99.80 110	99.46 146

SR-MVS-dyc-post		98.81 86	98.55 114	99.57 16	99.20 178	99.38 8	98.48 124	99.30 173	98.64 112	98.95 156	98.96 176	97.49 138	99.86 107	96.56 226	99.39 244	99.45 150

RE-MVS-def			98.58 111		99.20 178	99.38 8	98.48 124	99.30 173	98.64 112	98.95 156	98.96 176	97.75 111		96.56 226	99.39 244	99.45 150

HQP_MVS		97.99 199	97.67 215	98.93 143	99.19 181	97.65 181	97.77 204	99.27 187	98.20 147	97.79 278	97.98 304	94.90 254	99.70 252	94.42 307	99.51 225	99.45 150

plane_prior5								99.27 187					99.70 252	94.42 307	99.51 225	99.45 150

lessismore_v0				98.97 137	99.73 37	97.53 189		86.71 408		99.37 89	99.52 57	89.93 324	99.92 50	98.99 62	99.72 150	99.44 154

TAMVS		98.24 175	98.05 184	98.80 159	99.07 209	97.18 211	97.88 190	98.81 280	96.66 266	99.17 126	99.21 110	94.81 260	99.77 217	96.96 188	99.88 73	99.44 154

DeepPCF-MVS	96.93 5	98.32 163	98.01 187	99.23 96	98.39 326	98.97 66	95.03 364	99.18 212	96.88 254	99.33 96	98.78 215	98.16 84	99.28 373	96.74 209	99.62 188	99.44 154

3Dnovator	98.27 2	98.81 86	98.73 85	99.05 126	98.76 266	97.81 170	99.25 40	99.30 173	98.57 121	98.55 220	99.33 89	97.95 99	99.90 64	97.16 169	99.67 174	99.44 154

MVSFormer		98.26 172	98.43 134	97.77 267	98.88 247	93.89 324	99.39 17	99.56 69	99.11 73	98.16 248	98.13 291	93.81 284	99.97 4	99.26 42	99.57 208	99.43 158

jason		97.45 238	97.35 238	97.76 270	99.24 167	93.93 320	95.86 337	98.42 309	94.24 338	98.50 225	98.13 291	94.82 258	99.91 59	97.22 166	99.73 143	99.43 158
jason: jason.
NCCC		97.86 207	97.47 232	99.05 126	98.61 297	98.07 140	96.98 275	98.90 261	97.63 185	97.04 320	97.93 309	95.99 219	99.66 279	95.31 285	98.82 314	99.43 158

Anonymous20240521		98.69 106	98.87 71	98.16 243	99.77 27	95.11 285	99.08 58	99.44 116	99.34 49	99.33 96	99.55 48	94.10 280	99.94 35	99.25 44	99.96 24	99.42 161

MVS_111021_HR		98.25 174	98.08 182	98.75 173	99.09 205	97.46 192	95.97 329	99.27 187	97.60 190	97.99 263	98.25 283	98.15 86	99.38 358	96.87 198	99.57 208	99.42 161

COLMAP_ROB	96.50 10	98.99 62	98.85 75	99.41 59	99.58 77	99.10 60	98.74 88	99.56 69	99.09 83	99.33 96	99.19 114	98.40 63	99.72 247	95.98 260	99.76 136	99.42 161
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 73	98.72 87	99.49 47	99.49 115	99.17 39	98.10 159	99.31 165	98.03 158	99.66 43	99.02 153	98.36 65	99.88 83	96.91 190	99.62 188	99.41 164

OPU-MVS				98.82 155	98.59 302	98.30 115	98.10 159				98.52 256	98.18 80	98.75 395	94.62 299	99.48 234	99.41 164

our_test_3		97.39 242	97.73 212	96.34 339	98.70 279	89.78 383	94.61 377	98.97 252	96.50 271	99.04 142	98.85 203	95.98 220	99.84 136	97.26 164	99.67 174	99.41 164

casdiffmvs		98.95 69	99.00 63	98.81 157	99.38 140	97.33 199	97.82 197	99.57 62	99.17 71	99.35 93	99.17 121	98.35 68	99.69 256	98.46 95	99.73 143	99.41 164
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		97.60 227	97.67 215	97.39 302	99.04 217	93.04 341	95.27 357	98.38 312	97.25 229	98.92 166	98.95 181	95.48 240	99.73 240	96.99 184	98.74 316	99.41 164

MDA-MVSNet_test_wron		97.60 227	97.66 218	97.41 301	99.04 217	93.09 337	95.27 357	98.42 309	97.26 228	98.88 173	98.95 181	95.43 243	99.73 240	97.02 181	98.72 318	99.41 164

GBi-Net		98.65 116	98.47 128	99.17 101	98.90 241	98.24 119	99.20 45	99.44 116	98.59 118	98.95 156	99.55 48	94.14 276	99.86 107	97.77 138	99.69 163	99.41 164

test1		98.65 116	98.47 128	99.17 101	98.90 241	98.24 119	99.20 45	99.44 116	98.59 118	98.95 156	99.55 48	94.14 276	99.86 107	97.77 138	99.69 163	99.41 164

FMVSNet1		99.17 42	99.17 44	99.17 101	99.55 93	98.24 119	99.20 45	99.44 116	99.21 62	99.43 76	99.55 48	97.82 107	99.86 107	98.42 98	99.89 72	99.41 164

test_fmvs1		97.72 219	97.94 195	97.07 316	98.66 293	92.39 352	97.68 215	99.81 23	95.20 317	99.54 56	99.44 71	91.56 314	99.41 353	99.78 15	99.77 125	99.40 173

KD-MVS_self_test		99.25 33	99.18 43	99.44 56	99.63 73	99.06 64	98.69 96	99.54 78	99.31 52	99.62 52	99.53 54	97.36 144	99.86 107	99.24 46	99.71 155	99.39 174

v148		98.45 147	98.60 109	98.00 255	99.44 129	94.98 287	97.44 247	99.06 234	98.30 134	99.32 102	98.97 173	96.65 187	99.62 293	98.37 99	99.85 80	99.39 174

test20.03		98.78 90	98.77 82	98.78 165	99.46 125	97.20 209	97.78 202	99.24 198	99.04 88	99.41 80	98.90 189	97.65 117	99.76 223	97.70 144	99.79 115	99.39 174

CDPH-MVS		97.26 251	96.66 278	99.07 119	99.00 222	98.15 127	96.03 327	99.01 248	91.21 378	97.79 278	97.85 313	96.89 170	99.69 256	92.75 350	99.38 247	99.39 174

EPNet		96.14 301	95.44 312	98.25 235	90.76 413	95.50 270	97.92 184	94.65 380	98.97 94	92.98 396	98.85 203	89.12 330	99.87 100	95.99 259	99.68 168	99.39 174
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		98.17 183	97.87 203	99.07 119	98.67 288	98.24 119	97.01 273	98.93 255	97.25 229	97.62 287	98.34 277	97.27 149	99.57 312	96.42 237	99.33 253	99.39 174

DeepC-MVS_fast	96.85 6	98.30 166	98.15 174	98.75 173	98.61 297	97.23 205	97.76 207	99.09 231	97.31 223	98.75 193	98.66 236	97.56 127	99.64 287	96.10 257	99.55 214	99.39 174
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		98.53 137	98.27 159	99.32 79	99.31 154	98.75 80	98.19 148	99.41 126	96.77 261	98.83 181	98.90 189	97.80 108	99.82 164	95.68 276	99.52 223	99.38 181

test9_res														93.28 339	99.15 282	99.38 181

OPM-MVS		98.56 129	98.32 153	99.25 92	99.41 137	98.73 84	97.13 270	99.18 212	97.10 244	98.75 193	98.92 185	98.18 80	99.65 284	96.68 216	99.56 211	99.37 183
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
agg_prior2														92.50 355	99.16 280	99.37 183

AllTest		98.44 148	98.20 166	99.16 104	99.50 108	98.55 96	98.25 143	99.58 55	96.80 258	98.88 173	99.06 141	97.65 117	99.57 312	94.45 305	99.61 193	99.37 183

TestCases				99.16 104	99.50 108	98.55 96		99.58 55	96.80 258	98.88 173	99.06 141	97.65 117	99.57 312	94.45 305	99.61 193	99.37 183

MDA-MVSNet-bldmvs		97.94 200	97.91 198	98.06 250	99.44 129	94.96 288	96.63 295	99.15 224	98.35 129	98.83 181	99.11 134	94.31 273	99.85 119	96.60 219	98.72 318	99.37 183

MVSTER		96.86 277	96.55 284	97.79 265	97.91 353	94.21 308	97.56 233	98.87 266	97.49 203	99.06 135	99.05 148	80.72 378	99.80 185	98.44 96	99.82 95	99.37 183

pmmvs5		97.64 225	97.49 229	98.08 248	99.14 196	95.12 284	96.70 292	99.05 237	93.77 347	98.62 207	98.83 206	93.23 289	99.75 230	98.33 103	99.76 136	99.36 189

Anonymous20231206		98.21 178	98.21 165	98.20 239	99.51 105	95.43 273	98.13 154	99.32 160	96.16 285	98.93 165	98.82 209	96.00 215	99.83 154	97.32 161	99.73 143	99.36 189

train_agg		97.10 263	96.45 287	99.07 119	98.71 275	98.08 138	95.96 331	99.03 242	91.64 370	95.85 361	97.53 329	96.47 194	99.76 223	93.67 329	99.16 280	99.36 189

PVSNet_BlendedMVS		97.55 231	97.53 226	97.60 283	98.92 237	93.77 328	96.64 294	99.43 122	94.49 330	97.62 287	99.18 117	96.82 175	99.67 268	94.73 296	99.93 43	99.36 189

Anonymous20240529		98.93 71	98.87 71	99.12 109	99.19 181	98.22 124	99.01 66	98.99 251	99.25 58	99.54 56	99.37 79	97.04 161	99.80 185	97.89 129	99.52 223	99.35 193

F-COLMAP		97.30 248	96.68 275	99.14 107	99.19 181	98.39 107	97.27 260	99.30 173	92.93 358	96.62 342	98.00 302	95.73 229	99.68 265	92.62 353	98.46 334	99.35 193

ppachtmachnet_test		97.50 232	97.74 210	96.78 331	98.70 279	91.23 372	94.55 379	99.05 237	96.36 277	99.21 119	98.79 214	96.39 197	99.78 211	96.74 209	99.82 95	99.34 195

VDD-MVS		98.56 129	98.39 141	99.07 119	99.13 198	98.07 140	98.59 105	97.01 348	99.59 24	99.11 128	99.27 98	94.82 258	99.79 200	98.34 101	99.63 185	99.34 195

testgi		98.32 163	98.39 141	98.13 244	99.57 81	95.54 267	97.78 202	99.49 96	97.37 217	99.19 121	97.65 323	98.96 24	99.49 337	96.50 233	98.99 301	99.34 195

diffmvs		98.22 176	98.24 163	98.17 241	99.00 222	95.44 272	96.38 307	99.58 55	97.79 176	98.53 223	98.50 261	96.76 181	99.74 235	97.95 128	99.64 182	99.34 195
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		97.89 203	97.60 223	98.75 173	99.31 154	97.17 212	97.62 224	99.35 147	98.72 110	98.76 192	98.68 231	92.57 304	99.74 235	97.76 142	95.60 394	99.34 195

baseline		98.96 68	99.02 61	98.76 169	99.38 140	97.26 204	98.49 121	99.50 89	98.86 103	99.19 121	99.06 141	98.23 73	99.69 256	98.71 79	99.76 136	99.33 200

MG-MVS		96.77 281	96.61 280	97.26 307	98.31 330	93.06 338	95.93 334	98.12 323	96.45 275	97.92 266	98.73 222	93.77 286	99.39 356	91.19 373	99.04 294	99.33 200

HQP4-MVS										95.56 366			99.54 323			99.32 202

CDS-MVSNet		97.69 221	97.35 238	98.69 180	98.73 270	97.02 220	96.92 281	98.75 290	95.89 297	98.59 213	98.67 233	92.08 310	99.74 235	96.72 212	99.81 100	99.32 202
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP-MVS		97.00 272	96.49 286	98.55 203	98.67 288	96.79 229	96.29 313	99.04 240	96.05 289	95.55 367	96.84 350	93.84 282	99.54 323	92.82 347	99.26 266	99.32 202

RPSCF		98.62 122	98.36 146	99.42 57	99.65 64	99.42 7	98.55 109	99.57 62	97.72 180	98.90 168	99.26 100	96.12 209	99.52 329	95.72 273	99.71 155	99.32 202

MVP-Stereo		98.08 190	97.92 197	98.57 198	98.96 229	96.79 229	97.90 187	99.18 212	96.41 276	98.46 228	98.95 181	95.93 223	99.60 300	96.51 232	98.98 303	99.31 206
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		98.40 152	98.68 95	97.54 290	98.96 229	97.99 147	97.88 190	99.36 142	98.20 147	99.63 49	99.04 150	98.76 35	95.33 408	96.56 226	99.74 140	99.31 206
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		98.42 149	98.30 154	98.79 162	98.79 265	97.29 201	98.23 144	98.66 296	99.31 52	98.85 178	98.80 212	94.80 261	99.78 211	98.13 113	99.13 285	99.31 206

test_prior				98.95 140	98.69 284	97.95 155		99.03 242					99.59 304			99.30 209

USDC		97.41 241	97.40 233	97.44 299	98.94 231	93.67 330	95.17 360	99.53 81	94.03 344	98.97 153	99.10 137	95.29 245	99.34 363	95.84 269	99.73 143	99.30 209

test_fmvsm_n_1920		99.33 26	99.45 18	98.99 134	99.57 81	97.73 177	97.93 182	99.83 20	99.22 60	99.93 6	99.30 94	99.42 10	99.96 11	99.85 5	99.99 5	99.29 211

FMVSNet2		98.49 142	98.40 138	98.75 173	98.90 241	97.14 216	98.61 103	99.13 225	98.59 118	99.19 121	99.28 96	94.14 276	99.82 164	97.97 126	99.80 110	99.29 211

XVG-OURS-SEG-HR		98.49 142	98.28 156	99.14 107	99.49 115	98.83 76	96.54 297	99.48 98	97.32 222	99.11 128	98.61 247	99.33 13	99.30 369	96.23 247	98.38 335	99.28 213

test12				98.93 143	98.58 304	97.83 164		98.66 296		96.53 345		95.51 238	99.69 256		99.13 285	99.27 214

DSMNet-mixed		97.42 240	97.60 223	96.87 325	99.15 195	91.46 363	98.54 111	99.12 226	92.87 360	97.58 291	99.63 33	96.21 205	99.90 64	95.74 272	99.54 216	99.27 214

N_pmnet		97.63 226	97.17 246	98.99 134	99.27 161	97.86 161	95.98 328	93.41 391	95.25 315	99.47 70	98.90 189	95.63 231	99.85 119	96.91 190	99.73 143	99.27 214

ambc				98.24 237	98.82 258	95.97 256	98.62 102	99.00 250		99.27 107	99.21 110	96.99 166	99.50 334	96.55 229	99.50 232	99.26 217

LFMVS		97.20 257	96.72 272	98.64 183	98.72 272	96.95 224	98.93 76	94.14 388	99.74 7	98.78 187	99.01 162	84.45 362	99.73 240	97.44 155	99.27 263	99.25 218

FMVSNet5		96.01 304	95.20 321	98.41 220	97.53 372	96.10 249	98.74 88	99.50 89	97.22 238	98.03 262	99.04 150	69.80 398	99.88 83	97.27 163	99.71 155	99.25 218

BH-RMVSNet		96.83 278	96.58 283	97.58 285	98.47 315	94.05 312	96.67 293	97.36 339	96.70 265	97.87 271	97.98 304	95.14 249	99.44 349	90.47 380	98.58 332	99.25 218

testf1		99.25 33	99.16 46	99.51 42	99.89 6	99.63 3	98.71 94	99.69 36	98.90 100	99.43 76	99.35 83	98.86 28	99.67 268	97.81 135	99.81 100	99.24 221

APD_test2		99.25 33	99.16 46	99.51 42	99.89 6	99.63 3	98.71 94	99.69 36	98.90 100	99.43 76	99.35 83	98.86 28	99.67 268	97.81 135	99.81 100	99.24 221

旧先验1					98.82 258	97.45 193		98.76 287			98.34 277	95.50 239			99.01 299	99.23 223

test222					98.92 237	96.93 226	95.54 347	98.78 285	85.72 398	96.86 333	98.11 294	94.43 268			99.10 290	99.23 223

XVG-ACMP-BASELINE		98.56 129	98.34 149	99.22 97	99.54 98	98.59 93	97.71 212	99.46 108	97.25 229	98.98 149	98.99 167	97.54 129	99.84 136	95.88 263	99.74 140	99.23 223

FMVSNet3		97.50 232	97.24 243	98.29 233	98.08 344	95.83 260	97.86 194	98.91 260	97.89 169	98.95 156	98.95 181	87.06 341	99.81 178	97.77 138	99.69 163	99.23 223

无先验							95.74 342	98.74 292	89.38 389				99.73 240	92.38 357		99.22 227

tttt0517		95.64 316	94.98 325	97.64 281	99.36 147	93.81 326	98.72 92	90.47 402	98.08 157	98.67 200	98.34 277	73.88 396	99.92 50	97.77 138	99.51 225	99.20 228

pmmvs-eth3d		98.47 144	98.34 149	98.86 151	99.30 157	97.76 173	97.16 268	99.28 184	95.54 306	99.42 79	99.19 114	97.27 149	99.63 290	97.89 129	99.97 19	99.20 228

MS-PatchMatch		97.68 222	97.75 209	97.45 298	98.23 336	93.78 327	97.29 257	98.84 275	96.10 287	98.64 204	98.65 238	96.04 212	99.36 359	96.84 201	99.14 283	99.20 228

新几何1				98.91 146	98.94 231	97.76 173		98.76 287	87.58 395	96.75 338	98.10 295	94.80 261	99.78 211	92.73 351	99.00 300	99.20 228

PHI-MVS		98.29 169	97.95 193	99.34 72	98.44 320	99.16 43	98.12 156	99.38 133	96.01 292	98.06 258	98.43 267	97.80 108	99.67 268	95.69 275	99.58 204	99.20 228

Anonymous202405211		97.90 201	97.50 228	99.08 117	98.90 241	98.25 118	98.53 112	96.16 365	98.87 102	99.11 128	98.86 200	90.40 322	99.78 211	97.36 159	99.31 256	99.19 233

CANet		97.87 206	97.76 208	98.19 240	97.75 358	95.51 269	96.76 288	99.05 237	97.74 178	96.93 324	98.21 287	95.59 233	99.89 74	97.86 134	99.93 43	99.19 233

XVG-OURS		98.53 137	98.34 149	99.11 111	99.50 108	98.82 78	95.97 329	99.50 89	97.30 224	99.05 140	98.98 171	99.35 12	99.32 366	95.72 273	99.68 168	99.18 235

WTY-MVS		96.67 284	96.27 293	97.87 260	98.81 261	94.61 299	96.77 287	97.92 328	94.94 322	97.12 315	97.74 318	91.11 317	99.82 164	93.89 323	98.15 347	99.18 235

Vis-MVSNet (Re-imp)		97.46 236	97.16 247	98.34 226	99.55 93	96.10 249	98.94 75	98.44 308	98.32 133	98.16 248	98.62 245	88.76 331	99.73 240	93.88 324	99.79 115	99.18 235

TinyColmap		97.89 203	97.98 191	97.60 283	98.86 249	94.35 305	96.21 317	99.44 116	97.45 211	99.06 135	98.88 197	97.99 97	99.28 373	94.38 311	99.58 204	99.18 235

testdata				98.09 245	98.93 233	95.40 274		98.80 282	90.08 386	97.45 304	98.37 273	95.26 246	99.70 252	93.58 332	98.95 306	99.17 239

lupinMVS		97.06 266	96.86 262	97.65 279	98.88 247	93.89 324	95.48 351	97.97 326	93.53 350	98.16 248	97.58 327	93.81 284	99.91 59	96.77 206	99.57 208	99.17 239

Patchmtry		97.35 244	96.97 255	98.50 212	97.31 382	96.47 241	98.18 149	98.92 258	98.95 97	98.78 187	99.37 79	85.44 356	99.85 119	95.96 261	99.83 91	99.17 239

sss		97.21 256	96.93 256	98.06 250	98.83 255	95.22 280	96.75 289	98.48 307	94.49 330	97.27 312	97.90 310	92.77 301	99.80 185	96.57 222	99.32 254	99.16 242

iter_conf05		98.46 145	98.38 143	98.70 179	99.27 161	97.15 214	97.51 240	99.51 86	97.57 192	98.95 156	98.89 195	95.48 240	99.82 164	98.30 104	99.96 24	99.14 243

CSCG		98.68 111	98.50 121	99.20 98	99.45 128	98.63 88	98.56 108	99.57 62	97.87 170	98.85 178	98.04 301	97.66 116	99.84 136	96.72 212	99.81 100	99.13 244

MVS_111021_LR		98.30 166	98.12 177	98.83 154	99.16 191	98.03 145	96.09 325	99.30 173	97.58 191	98.10 255	98.24 284	98.25 71	99.34 363	96.69 215	99.65 180	99.12 245

miper_lstm_enhance		97.18 259	97.16 247	97.25 308	98.16 339	92.85 343	95.15 362	99.31 165	97.25 229	98.74 195	98.78 215	90.07 323	99.78 211	97.19 167	99.80 110	99.11 246

testing3		93.51 349	92.09 358	97.75 271	98.60 299	94.40 303	97.32 254	95.26 377	97.56 196	96.79 337	95.50 376	53.57 414	99.77 217	95.26 286	98.97 304	99.08 247

原ACMM1				98.35 225	98.90 241	96.25 247		98.83 279	92.48 364	96.07 358	98.10 295	95.39 244	99.71 248	92.61 354	98.99 301	99.08 247

QAPM		97.31 247	96.81 268	98.82 155	98.80 264	97.49 190	99.06 62	99.19 208	90.22 384	97.69 284	99.16 123	96.91 169	99.90 64	90.89 378	99.41 242	99.07 249

PAPM_NR		96.82 280	96.32 290	98.30 232	99.07 209	96.69 234	97.48 243	98.76 287	95.81 299	96.61 343	96.47 358	94.12 279	99.17 380	90.82 379	97.78 358	99.06 250

eth_miper_zixun_eth		97.23 255	97.25 242	97.17 311	98.00 348	92.77 345	94.71 371	99.18 212	97.27 227	98.56 218	98.74 221	91.89 311	99.69 256	97.06 180	99.81 100	99.05 251

D2MVS		97.84 213	97.84 205	97.83 262	99.14 196	94.74 293	96.94 277	98.88 264	95.84 298	98.89 170	98.96 176	94.40 270	99.69 256	97.55 148	99.95 31	99.05 251

c3_l		97.36 243	97.37 236	97.31 303	98.09 343	93.25 336	95.01 365	99.16 219	97.05 245	98.77 190	98.72 224	92.88 298	99.64 287	96.93 189	99.76 136	99.05 251

PLC	94.65 16	96.51 289	95.73 300	98.85 152	98.75 268	97.91 157	96.42 305	99.06 234	90.94 381	95.59 364	97.38 339	94.41 269	99.59 304	90.93 376	98.04 356	99.05 251
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpnnormal		98.90 75	98.90 70	98.91 146	99.67 61	97.82 167	99.00 68	99.44 116	99.45 36	99.51 66	99.24 105	98.20 79	99.86 107	95.92 262	99.69 163	99.04 255

CANet_DTU		97.26 251	97.06 252	97.84 261	97.57 367	94.65 298	96.19 319	98.79 283	97.23 235	95.14 376	98.24 284	93.22 290	99.84 136	97.34 160	99.84 84	99.04 255

PM-MVS		98.82 84	98.72 87	99.12 109	99.64 69	98.54 99	97.98 178	99.68 41	97.62 186	99.34 95	99.18 117	97.54 129	99.77 217	97.79 137	99.74 140	99.04 255

TSAR-MVS + GP.		98.18 181	97.98 191	98.77 168	98.71 275	97.88 159	96.32 311	98.66 296	96.33 278	99.23 118	98.51 257	97.48 139	99.40 354	97.16 169	99.46 235	99.02 258

DIV-MVS_self_test		97.02 269	96.84 264	97.58 285	97.82 356	94.03 315	94.66 374	99.16 219	97.04 246	98.63 205	98.71 225	88.69 332	99.69 256	97.00 182	99.81 100	99.01 259

GA-MVS		95.86 309	95.32 318	97.49 295	98.60 299	94.15 311	93.83 391	97.93 327	95.49 308	96.68 339	97.42 337	83.21 370	99.30 369	96.22 248	98.55 333	99.01 259

OMC-MVS		97.88 205	97.49 229	99.04 128	98.89 246	98.63 88	96.94 277	99.25 193	95.02 319	98.53 223	98.51 257	97.27 149	99.47 343	93.50 335	99.51 225	99.01 259

cl____		97.02 269	96.83 265	97.58 285	97.82 356	94.04 314	94.66 374	99.16 219	97.04 246	98.63 205	98.71 225	88.68 334	99.69 256	97.00 182	99.81 100	99.00 262

pmmvs4		97.58 230	97.28 241	98.51 209	98.84 253	96.93 226	95.40 355	98.52 305	93.60 349	98.61 209	98.65 238	95.10 250	99.60 300	96.97 187	99.79 115	98.99 263

EPNet_dtu		94.93 329	94.78 330	95.38 365	93.58 409	87.68 392	96.78 286	95.69 375	97.35 219	89.14 406	98.09 297	88.15 339	99.49 337	94.95 292	99.30 259	98.98 264
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		96.50 291	95.77 298	98.69 180	99.48 122	97.43 195	97.84 196	99.55 73	81.42 404	96.51 347	98.58 250	95.53 236	99.67 268	93.41 337	99.58 204	98.98 264

PVSNet_Blended		96.88 276	96.68 275	97.47 297	98.92 237	93.77 328	94.71 371	99.43 122	90.98 380	97.62 287	97.36 341	96.82 175	99.67 268	94.73 296	99.56 211	98.98 264

APD_test1		98.83 83	98.66 98	99.34 72	99.78 24	99.47 6	98.42 131	99.45 112	98.28 139	98.98 149	99.19 114	97.76 110	99.58 310	96.57 222	99.55 214	98.97 267

PAPR		95.29 322	94.47 331	97.75 271	97.50 377	95.14 283	94.89 368	98.71 294	91.39 376	95.35 374	95.48 378	94.57 266	99.14 383	84.95 396	97.37 370	98.97 267

EGC-MVSNET		85.24 373	80.54 376	99.34 72	99.77 27	99.20 34	99.08 58	99.29 181	12.08 411	20.84 412	99.42 73	97.55 128	99.85 119	97.08 177	99.72 150	98.96 269

thisisatest0530		95.27 323	94.45 332	97.74 273	99.19 181	94.37 304	97.86 194	90.20 403	97.17 240	98.22 244	97.65 323	73.53 397	99.90 64	96.90 195	99.35 250	98.95 270

mvs_anonymous		97.83 215	98.16 173	96.87 325	98.18 338	91.89 359	97.31 255	98.90 261	97.37 217	98.83 181	99.46 66	96.28 203	99.79 200	98.90 66	98.16 346	98.95 270

baseline1		95.96 307	95.44 312	97.52 292	98.51 313	93.99 318	98.39 133	96.09 367	98.21 143	98.40 237	97.76 317	86.88 342	99.63 290	95.42 283	89.27 406	98.95 270

CLD-MVS		97.49 234	97.16 247	98.48 213	99.07 209	97.03 219	94.71 371	99.21 202	94.46 332	98.06 258	97.16 345	97.57 126	99.48 340	94.46 304	99.78 120	98.95 270
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MSLP-MVS++		98.02 193	98.14 176	97.64 281	98.58 304	95.19 281	97.48 243	99.23 200	97.47 204	97.90 268	98.62 245	97.04 161	98.81 394	97.55 148	99.41 242	98.94 274

DELS-MVS		98.27 170	98.20 166	98.48 213	98.86 249	96.70 233	95.60 346	99.20 204	97.73 179	98.45 229	98.71 225	97.50 135	99.82 164	98.21 108	99.59 199	98.93 275
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		95.79 311	95.39 315	96.98 319	96.77 394	92.79 344	94.40 382	98.53 304	94.59 329	97.89 269	98.17 290	82.82 374	99.24 375	96.37 239	99.03 295	98.92 276

LS3D		98.63 120	98.38 143	99.36 63	97.25 383	99.38 8	99.12 57	99.32 160	99.21 62	98.44 230	98.88 197	97.31 145	99.80 185	96.58 220	99.34 252	98.92 276

CMPMVS	75.91 23	96.29 297	95.44 312	98.84 153	96.25 402	98.69 87	97.02 272	99.12 226	88.90 391	97.83 275	98.86 200	89.51 327	98.90 392	91.92 358	99.51 225	98.92 276
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet-Re		98.64 118	98.48 126	99.11 111	98.85 252	98.51 101	98.49 121	99.83 20	98.37 128	99.69 38	99.46 66	98.21 78	99.92 50	94.13 317	99.30 259	98.91 279

DPM-MVS		96.32 296	95.59 306	98.51 209	98.76 266	97.21 208	94.54 380	98.26 315	91.94 369	96.37 351	97.25 343	93.06 295	99.43 350	91.42 368	98.74 316	98.89 280

test_yl		96.69 282	96.29 291	97.90 257	98.28 331	95.24 278	97.29 257	97.36 339	98.21 143	98.17 246	97.86 311	86.27 346	99.55 318	94.87 293	98.32 336	98.89 280

DCV-MVSNet		96.69 282	96.29 291	97.90 257	98.28 331	95.24 278	97.29 257	97.36 339	98.21 143	98.17 246	97.86 311	86.27 346	99.55 318	94.87 293	98.32 336	98.89 280

CS-MVS-test		99.13 50	99.09 56	99.26 89	99.13 198	98.97 66	99.31 27	99.88 11	99.44 38	98.16 248	98.51 257	98.64 44	99.93 40	98.91 65	99.85 80	98.88 283

UnsupCasMVSNet_bld		97.30 248	96.92 258	98.45 216	99.28 159	96.78 232	96.20 318	99.27 187	95.42 310	98.28 242	98.30 281	93.16 291	99.71 248	94.99 290	97.37 370	98.87 284

Effi-MVS+		98.02 193	97.82 206	98.62 188	98.53 311	97.19 210	97.33 253	99.68 41	97.30 224	96.68 339	97.46 335	98.56 54	99.80 185	96.63 218	98.20 342	98.86 285

test_0402		98.76 94	98.71 89	98.93 143	99.56 89	98.14 129	98.45 128	99.34 153	99.28 56	98.95 156	98.91 186	98.34 69	99.79 200	95.63 277	99.91 62	98.86 285

PatchmatchNet		95.58 317	95.67 303	95.30 366	97.34 381	87.32 393	97.65 221	96.65 358	95.30 314	97.07 318	98.69 229	84.77 359	99.75 230	94.97 291	98.64 327	98.83 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_vis1_rt		97.75 217	97.72 213	97.83 262	98.81 261	96.35 244	97.30 256	99.69 36	94.61 328	97.87 271	98.05 300	96.26 204	98.32 399	98.74 76	98.18 343	98.82 288

CL-MVSNet_self_test		97.44 239	97.22 244	98.08 248	98.57 306	95.78 262	94.30 384	98.79 283	96.58 269	98.60 211	98.19 289	94.74 264	99.64 287	96.41 238	98.84 311	98.82 288

miper_ehance_all_eth		97.06 266	97.03 253	97.16 313	97.83 355	93.06 338	94.66 374	99.09 231	95.99 293	98.69 198	98.45 266	92.73 302	99.61 299	96.79 203	99.03 295	98.82 288

MIMVSNet		96.62 287	96.25 294	97.71 276	99.04 217	94.66 297	99.16 51	96.92 354	97.23 235	97.87 271	99.10 137	86.11 350	99.65 284	91.65 363	99.21 273	98.82 288

iter_conf05_11		98.62 122	98.57 112	98.76 169	98.69 284	97.65 181	98.90 78	99.52 84	98.15 154	98.72 196	99.20 112	95.57 234	99.84 136	98.58 86	99.83 91	98.81 292

hse-mvs2		97.46 236	97.07 251	98.64 183	98.73 270	97.33 199	97.45 246	97.64 336	99.11 73	98.58 215	97.98 304	88.65 335	99.79 200	98.11 114	97.39 369	98.81 292

GSMVS																98.81 292

sam_mvs1												84.74 360				98.81 292

SCA		96.41 295	96.66 278	95.67 357	98.24 334	88.35 388	95.85 339	96.88 355	96.11 286	97.67 285	98.67 233	93.10 293	99.85 119	94.16 313	99.22 271	98.81 292

Patchmatch-RL test		97.26 251	97.02 254	97.99 256	99.52 103	95.53 268	96.13 323	99.71 33	97.47 204	99.27 107	99.16 123	84.30 365	99.62 293	97.89 129	99.77 125	98.81 292

AUN-MVS		96.24 300	95.45 311	98.60 193	98.70 279	97.22 207	97.38 249	97.65 334	95.95 295	95.53 371	97.96 308	82.11 377	99.79 200	96.31 243	97.44 366	98.80 298

ITE_SJBPF				98.87 150	99.22 172	98.48 103		99.35 147	97.50 201	98.28 242	98.60 248	97.64 120	99.35 362	93.86 325	99.27 263	98.79 299

tpm		94.67 331	94.34 335	95.66 358	97.68 366	88.42 387	97.88 190	94.90 378	94.46 332	96.03 360	98.56 252	78.66 388	99.79 200	95.88 263	95.01 397	98.78 300

Patchmatch-test		96.55 288	96.34 289	97.17 311	98.35 327	93.06 338	98.40 132	97.79 329	97.33 220	98.41 233	98.67 233	83.68 369	99.69 256	95.16 288	99.31 256	98.77 301

EC-MVSNet		99.09 55	99.05 60	99.20 98	99.28 159	98.93 71	99.24 41	99.84 18	99.08 85	98.12 253	98.37 273	98.72 38	99.90 64	99.05 57	99.77 125	98.77 301

PMMVS		96.51 289	95.98 295	98.09 245	97.53 372	95.84 259	94.92 367	98.84 275	91.58 372	96.05 359	95.58 373	95.68 230	99.66 279	95.59 279	98.09 350	98.76 303

test_method		79.78 374	79.50 377	80.62 390	80.21 415	45.76 418	70.82 406	98.41 311	31.08 410	80.89 410	97.71 319	84.85 358	97.37 403	91.51 367	80.03 407	98.75 304

ab-mvs		98.41 150	98.36 146	98.59 194	99.19 181	97.23 205	99.32 23	98.81 280	97.66 183	98.62 207	99.40 78	96.82 175	99.80 185	95.88 263	99.51 225	98.75 304

CHOSEN 280x420		95.51 320	95.47 309	95.65 359	98.25 333	88.27 389	93.25 395	98.88 264	93.53 350	94.65 382	97.15 346	86.17 348	99.93 40	97.41 157	99.93 43	98.73 306

test_fmvsmvis_n_1920		99.26 32	99.49 12	98.54 206	99.66 63	96.97 221	98.00 175	99.85 15	99.24 59	99.92 8	99.50 59	99.39 11	99.95 23	99.89 3	99.98 12	98.71 307

MVS_Test		98.18 181	98.36 146	97.67 277	98.48 314	94.73 294	98.18 149	99.02 245	97.69 181	98.04 261	99.11 134	97.22 153	99.56 315	98.57 89	98.90 310	98.71 307

PVSNet	93.40 17	95.67 314	95.70 301	95.57 360	98.83 255	88.57 386	92.50 398	97.72 331	92.69 362	96.49 350	96.44 359	93.72 287	99.43 350	93.61 330	99.28 262	98.71 307

alignmvs		97.35 244	96.88 261	98.78 165	98.54 309	98.09 134	97.71 212	97.69 333	99.20 64	97.59 290	95.90 368	88.12 340	99.55 318	98.18 110	98.96 305	98.70 310

ADS-MVSNet2		95.43 321	94.98 325	96.76 332	98.14 340	91.74 360	97.92 184	97.76 330	90.23 382	96.51 347	98.91 186	85.61 353	99.85 119	92.88 345	96.90 379	98.69 311

ADS-MVSNet		95.24 324	94.93 328	96.18 347	98.14 340	90.10 382	97.92 184	97.32 342	90.23 382	96.51 347	98.91 186	85.61 353	99.74 235	92.88 345	96.90 379	98.69 311

MDTV_nov1_ep13_2view						74.92 414	97.69 214		90.06 387	97.75 281		85.78 352		93.52 333		98.69 311

MSDG		97.71 220	97.52 227	98.28 234	98.91 240	96.82 228	94.42 381	99.37 138	97.65 184	98.37 238	98.29 282	97.40 142	99.33 365	94.09 318	99.22 271	98.68 314

mvsany_test1		97.60 227	97.54 225	97.77 267	97.72 359	95.35 275	95.36 356	97.13 346	94.13 341	99.71 34	99.33 89	97.93 100	99.30 369	97.60 147	98.94 307	98.67 315

CS-MVS		99.13 50	99.10 55	99.24 94	99.06 213	99.15 47	99.36 19	99.88 11	99.36 48	98.21 245	98.46 265	98.68 42	99.93 40	99.03 59	99.85 80	98.64 316

Syy-MVS		96.04 303	95.56 308	97.49 295	97.10 387	94.48 301	96.18 320	96.58 360	95.65 302	94.77 379	92.29 405	91.27 316	99.36 359	98.17 112	98.05 354	98.63 317

myMVS_eth3d		91.92 370	90.45 372	96.30 340	97.10 387	90.90 375	96.18 320	96.58 360	95.65 302	94.77 379	92.29 405	53.88 413	99.36 359	89.59 384	98.05 354	98.63 317

miper_enhance_ethall		96.01 304	95.74 299	96.81 329	96.41 400	92.27 356	93.69 393	98.89 263	91.14 379	98.30 240	97.35 342	90.58 320	99.58 310	96.31 243	99.03 295	98.60 319

Effi-MVS+-dtu		98.26 172	97.90 199	99.35 69	98.02 346	99.49 5	98.02 171	99.16 219	98.29 137	97.64 286	97.99 303	96.44 196	99.95 23	96.66 217	98.93 308	98.60 319

new_pmnet		96.99 273	96.76 270	97.67 277	98.72 272	94.89 289	95.95 333	98.20 318	92.62 363	98.55 220	98.54 253	94.88 257	99.52 329	93.96 321	99.44 240	98.59 321

MVSMamba_pp		98.01 195	97.90 199	98.32 227	97.95 349	96.59 235	97.57 232	99.38 133	96.07 288	97.99 263	99.01 162	95.57 234	99.80 185	97.76 142	99.82 95	98.57 322

mamv4		98.09 188	98.01 187	98.31 230	98.02 346	96.58 238	97.53 236	99.41 126	97.57 192	97.89 269	98.96 176	95.45 242	99.80 185	97.48 154	99.78 120	98.57 322

testing91		93.32 352	92.27 355	96.47 337	97.54 370	91.25 370	96.17 322	96.76 357	97.18 239	93.65 394	93.50 399	65.11 408	99.63 290	93.04 342	97.45 365	98.53 324

EIA-MVS		98.00 196	97.74 210	98.80 159	98.72 272	98.09 134	98.05 166	99.60 52	97.39 215	96.63 341	95.55 374	97.68 114	99.80 185	96.73 211	99.27 263	98.52 325

PatchMatch-RL		97.24 254	96.78 269	98.61 191	99.03 220	97.83 164	96.36 308	99.06 234	93.49 352	97.36 311	97.78 315	95.75 228	99.49 337	93.44 336	98.77 315	98.52 325

sasdasda		98.34 159	98.26 160	98.58 195	98.46 317	97.82 167	98.96 72	99.46 108	99.19 68	97.46 302	95.46 379	98.59 50	99.46 345	98.08 117	98.71 320	98.46 327

ET-MVSNet_ETH3D		94.30 337	93.21 347	97.58 285	98.14 340	94.47 302	94.78 370	93.24 393	94.72 326	89.56 404	95.87 369	78.57 390	99.81 178	96.91 190	97.11 378	98.46 327

canonicalmvs		98.34 159	98.26 160	98.58 195	98.46 317	97.82 167	98.96 72	99.46 108	99.19 68	97.46 302	95.46 379	98.59 50	99.46 345	98.08 117	98.71 320	98.46 327

tt0805		98.69 106	98.62 104	98.90 149	99.75 34	99.30 17	99.15 53	96.97 350	98.86 103	98.87 177	97.62 326	98.63 46	98.96 388	99.41 36	98.29 339	98.45 330

TAPA-MVS	96.21 11	96.63 286	95.95 296	98.65 182	98.93 233	98.09 134	96.93 279	99.28 184	83.58 401	98.13 252	97.78 315	96.13 208	99.40 354	93.52 333	99.29 261	98.45 330
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MGCFI-Net		98.34 159	98.28 156	98.51 209	98.47 315	97.59 186	98.96 72	99.48 98	99.18 70	97.40 307	95.50 376	98.66 43	99.50 334	98.18 110	98.71 320	98.44 332

BH-untuned		96.83 278	96.75 271	97.08 314	98.74 269	93.33 335	96.71 291	98.26 315	96.72 263	98.44 230	97.37 340	95.20 247	99.47 343	91.89 359	97.43 367	98.44 332

WB-MVSnew		95.73 313	95.57 307	96.23 345	96.70 395	90.70 379	96.07 326	93.86 389	95.60 304	97.04 320	95.45 382	96.00 215	99.55 318	91.04 374	98.31 338	98.43 334

pmmvs3		95.03 327	94.40 333	96.93 321	97.70 363	92.53 349	95.08 363	97.71 332	88.57 392	97.71 282	98.08 298	79.39 385	99.82 164	96.19 250	99.11 289	98.43 334

DP-MVS Recon		97.33 246	96.92 258	98.57 198	99.09 205	97.99 147	96.79 285	99.35 147	93.18 354	97.71 282	98.07 299	95.00 253	99.31 367	93.97 320	99.13 285	98.42 336

testing99		93.04 357	91.98 363	96.23 345	97.53 372	90.70 379	96.35 309	95.94 370	96.87 255	93.41 395	93.43 400	63.84 410	99.59 304	93.24 340	97.19 375	98.40 337

ETVMVS		92.60 361	91.08 370	97.18 309	97.70 363	93.65 332	96.54 297	95.70 373	96.51 270	94.68 381	92.39 404	61.80 411	99.50 334	86.97 391	97.41 368	98.40 337

Fast-Effi-MVS+-dtu		98.27 170	98.09 179	98.81 157	98.43 321	98.11 131	97.61 226	99.50 89	98.64 112	97.39 309	97.52 331	98.12 87	99.95 23	96.90 195	98.71 320	98.38 339

LF4IMVS		97.90 201	97.69 214	98.52 208	99.17 189	97.66 180	97.19 267	99.47 106	96.31 280	97.85 274	98.20 288	96.71 185	99.52 329	94.62 299	99.72 150	98.38 339

testing11		93.08 356	92.02 360	96.26 343	97.56 368	90.83 377	96.32 311	95.70 373	96.47 274	92.66 398	93.73 396	64.36 409	99.59 304	93.77 328	97.57 361	98.37 341

Fast-Effi-MVS+		97.67 223	97.38 235	98.57 198	98.71 275	97.43 195	97.23 261	99.45 112	94.82 325	96.13 355	96.51 355	98.52 56	99.91 59	96.19 250	98.83 312	98.37 341

test0.0.03 1		94.51 332	93.69 341	96.99 318	96.05 403	93.61 333	94.97 366	93.49 390	96.17 283	97.57 293	94.88 389	82.30 375	99.01 387	93.60 331	94.17 401	98.37 341

UWE-MVS		92.38 364	91.76 367	94.21 375	97.16 385	84.65 402	95.42 354	88.45 406	95.96 294	96.17 354	95.84 371	66.36 404	99.71 248	91.87 360	98.64 327	98.28 344

FE-MVS		95.66 315	94.95 327	97.77 267	98.53 311	95.28 277	99.40 16	96.09 367	93.11 356	97.96 265	99.26 100	79.10 387	99.77 217	92.40 356	98.71 320	98.27 345

baseline2		93.73 346	92.83 352	96.42 338	97.70 363	91.28 369	96.84 284	89.77 404	93.96 346	92.44 399	95.93 367	79.14 386	99.77 217	92.94 343	96.76 383	98.21 346

thisisatest0515		94.12 341	93.16 348	96.97 320	98.60 299	92.90 342	93.77 392	90.61 401	94.10 342	96.91 327	95.87 369	74.99 395	99.80 185	94.52 302	99.12 288	98.20 347

EPMVS		93.72 347	93.27 346	95.09 369	96.04 404	87.76 391	98.13 154	85.01 410	94.69 327	96.92 325	98.64 241	78.47 392	99.31 367	95.04 289	96.46 385	98.20 347

dp		93.47 350	93.59 343	93.13 387	96.64 396	81.62 411	97.66 219	96.42 363	92.80 361	96.11 356	98.64 241	78.55 391	99.59 304	93.31 338	92.18 405	98.16 349

CNLPA		97.17 260	96.71 273	98.55 203	98.56 307	98.05 144	96.33 310	98.93 255	96.91 253	97.06 319	97.39 338	94.38 271	99.45 347	91.66 362	99.18 279	98.14 350

dmvs_re		95.98 306	95.39 315	97.74 273	98.86 249	97.45 193	98.37 135	95.69 375	97.95 163	96.56 344	95.95 366	90.70 319	97.68 402	88.32 387	96.13 390	98.11 351

HY-MVS	95.94 13	95.90 308	95.35 317	97.55 289	97.95 349	94.79 290	98.81 87	96.94 353	92.28 367	95.17 375	98.57 251	89.90 325	99.75 230	91.20 372	97.33 374	98.10 352

CostFormer		93.97 343	93.78 340	94.51 372	97.53 372	85.83 398	97.98 178	95.96 369	89.29 390	94.99 378	98.63 243	78.63 389	99.62 293	94.54 301	96.50 384	98.09 353

FA-MVS(test-final)		96.99 273	96.82 266	97.50 294	98.70 279	94.78 291	99.34 20	96.99 349	95.07 318	98.48 227	99.33 89	88.41 338	99.65 284	96.13 256	98.92 309	98.07 354

AdaColmap		97.14 262	96.71 273	98.46 215	98.34 328	97.80 171	96.95 276	98.93 255	95.58 305	96.92 325	97.66 322	95.87 225	99.53 325	90.97 375	99.14 283	98.04 355

KD-MVS_2432*1600		92.87 359	91.99 361	95.51 362	91.37 411	89.27 384	94.07 386	98.14 321	95.42 310	97.25 313	96.44 359	67.86 400	99.24 375	91.28 370	96.08 391	98.02 356

miper_refine_blended		92.87 359	91.99 361	95.51 362	91.37 411	89.27 384	94.07 386	98.14 321	95.42 310	97.25 313	96.44 359	67.86 400	99.24 375	91.28 370	96.08 391	98.02 356

TESTMET0.1,1		92.19 368	91.77 366	93.46 383	96.48 399	82.80 408	94.05 388	91.52 399	94.45 334	94.00 390	94.88 389	66.65 403	99.56 315	95.78 271	98.11 349	98.02 356

testing222		91.96 369	90.37 373	96.72 333	97.47 378	92.59 347	96.11 324	94.76 379	96.83 257	92.90 397	92.87 402	57.92 412	99.55 318	86.93 392	97.52 362	98.00 359

PCF-MVS	92.86 18	94.36 334	93.00 351	98.42 219	98.70 279	97.56 187	93.16 396	99.11 228	79.59 405	97.55 294	97.43 336	92.19 307	99.73 240	79.85 405	99.45 237	97.97 360
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OpenMVS	96.65 7	97.09 264	96.68 275	98.32 227	98.32 329	97.16 213	98.86 83	99.37 138	89.48 388	96.29 353	99.15 127	96.56 190	99.90 64	92.90 344	99.20 274	97.89 361

Gipuma		99.03 59	99.16 46	98.64 183	99.94 2	98.51 101	99.32 23	99.75 31	99.58 26	98.60 211	99.62 34	98.22 76	99.51 333	97.70 144	99.73 143	97.89 361
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PVSNet_0	89.98 21	91.15 372	90.30 375	93.70 381	97.72 359	84.34 406	90.24 402	97.42 337	90.20 385	93.79 392	93.09 401	90.90 318	98.89 393	86.57 394	72.76 409	97.87 363

test-LLR		93.90 344	93.85 338	94.04 376	96.53 397	84.62 403	94.05 388	92.39 395	96.17 283	94.12 387	95.07 383	82.30 375	99.67 268	95.87 266	98.18 343	97.82 364

test-mter		92.33 366	91.76 367	94.04 376	96.53 397	84.62 403	94.05 388	92.39 395	94.00 345	94.12 387	95.07 383	65.63 407	99.67 268	95.87 266	98.18 343	97.82 364

tpm2		93.09 355	92.58 354	94.62 371	97.56 368	86.53 395	97.66 219	95.79 372	86.15 397	94.07 389	98.23 286	75.95 393	99.53 325	90.91 377	96.86 382	97.81 366

CR-MVSNet		96.28 298	95.95 296	97.28 305	97.71 361	94.22 306	98.11 157	98.92 258	92.31 366	96.91 327	99.37 79	85.44 356	99.81 178	97.39 158	97.36 372	97.81 366

RPMNet		97.02 269	96.93 256	97.30 304	97.71 361	94.22 306	98.11 157	99.30 173	99.37 45	96.91 327	99.34 87	86.72 343	99.87 100	97.53 151	97.36 372	97.81 366

tpmrst		95.07 326	95.46 310	93.91 378	97.11 386	84.36 405	97.62 224	96.96 351	94.98 320	96.35 352	98.80 212	85.46 355	99.59 304	95.60 278	96.23 388	97.79 369

PAPM		91.88 371	90.34 374	96.51 335	98.06 345	92.56 348	92.44 399	97.17 344	86.35 396	90.38 403	96.01 364	86.61 344	99.21 378	70.65 409	95.43 395	97.75 370

FPMVS		93.44 351	92.23 356	97.08 314	99.25 166	97.86 161	95.61 345	97.16 345	92.90 359	93.76 393	98.65 238	75.94 394	95.66 406	79.30 406	97.49 363	97.73 371

MAR-MVS		96.47 293	95.70 301	98.79 162	97.92 352	99.12 57	98.28 140	98.60 301	92.16 368	95.54 370	96.17 363	94.77 263	99.52 329	89.62 383	98.23 340	97.72 372
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		98.03 192	97.86 204	98.56 202	98.69 284	98.07 140	97.51 240	99.50 89	98.10 156	97.50 299	95.51 375	98.41 62	99.88 83	96.27 246	99.24 268	97.71 373

thres600view7		94.45 333	93.83 339	96.29 341	99.06 213	91.53 362	97.99 177	94.24 386	98.34 130	97.44 305	95.01 385	79.84 381	99.67 268	84.33 397	98.23 340	97.66 374

thres400		94.14 340	93.44 344	96.24 344	98.93 233	91.44 364	97.60 227	94.29 384	97.94 164	97.10 316	94.31 394	79.67 383	99.62 293	83.05 399	98.08 351	97.66 374

IB-MVS	91.63 19	92.24 367	90.90 371	96.27 342	97.22 384	91.24 371	94.36 383	93.33 392	92.37 365	92.24 400	94.58 393	66.20 406	99.89 74	93.16 341	94.63 399	97.66 374
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		95.02 328	95.25 319	94.33 373	96.39 401	85.87 396	98.08 161	96.83 356	95.46 309	95.51 372	98.69 229	85.91 351	99.53 325	94.16 313	96.23 388	97.58 377

cascas		94.79 330	94.33 336	96.15 351	96.02 405	92.36 354	92.34 400	99.26 192	85.34 399	95.08 377	94.96 388	92.96 297	98.53 397	94.41 310	98.59 331	97.56 378

PatchT		96.65 285	96.35 288	97.54 290	97.40 379	95.32 276	97.98 178	96.64 359	99.33 50	96.89 331	99.42 73	84.32 364	99.81 178	97.69 146	97.49 363	97.48 379

TR-MVS		95.55 318	95.12 323	96.86 328	97.54 370	93.94 319	96.49 301	96.53 362	94.36 337	97.03 322	96.61 354	94.26 275	99.16 381	86.91 393	96.31 387	97.47 380

dmvs_testset		92.94 358	92.21 357	95.13 367	98.59 302	90.99 374	97.65 221	92.09 397	96.95 250	94.00 390	93.55 398	92.34 306	96.97 405	72.20 408	92.52 403	97.43 381

JIA-IIPM		95.52 319	95.03 324	97.00 317	96.85 392	94.03 315	96.93 279	95.82 371	99.20 64	94.63 383	99.71 17	83.09 371	99.60 300	94.42 307	94.64 398	97.36 382

BH-w/o		95.13 325	94.89 329	95.86 352	98.20 337	91.31 367	95.65 344	97.37 338	93.64 348	96.52 346	95.70 372	93.04 296	99.02 385	88.10 388	95.82 393	97.24 383

tpm cat1		93.29 353	93.13 350	93.75 380	97.39 380	84.74 401	97.39 248	97.65 334	83.39 402	94.16 386	98.41 268	82.86 373	99.39 356	91.56 366	95.35 396	97.14 384

xiu_mvs_v1_base_debu		97.86 207	98.17 170	96.92 322	98.98 226	93.91 321	96.45 302	99.17 216	97.85 172	98.41 233	97.14 347	98.47 57	99.92 50	98.02 121	99.05 291	96.92 385

xiu_mvs_v1_base		97.86 207	98.17 170	96.92 322	98.98 226	93.91 321	96.45 302	99.17 216	97.85 172	98.41 233	97.14 347	98.47 57	99.92 50	98.02 121	99.05 291	96.92 385

xiu_mvs_v1_base_debi		97.86 207	98.17 170	96.92 322	98.98 226	93.91 321	96.45 302	99.17 216	97.85 172	98.41 233	97.14 347	98.47 57	99.92 50	98.02 121	99.05 291	96.92 385

PMVS	91.26 20	97.86 207	97.94 195	97.65 279	99.71 46	97.94 156	98.52 113	98.68 295	98.99 92	97.52 297	99.35 83	97.41 141	98.18 400	91.59 365	99.67 174	96.82 388
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1314		95.74 312	95.60 305	96.17 348	97.53 372	92.75 346	98.07 163	98.31 314	91.22 377	94.25 385	96.68 353	95.53 236	99.03 384	91.64 364	97.18 376	96.74 389

MVS-HIRNet		94.32 335	95.62 304	90.42 389	98.46 317	75.36 413	96.29 313	89.13 405	95.25 315	95.38 373	99.75 11	92.88 298	99.19 379	94.07 319	99.39 244	96.72 390

OpenMVS_ROB	95.38 14	95.84 310	95.18 322	97.81 264	98.41 325	97.15 214	97.37 250	98.62 300	83.86 400	98.65 203	98.37 273	94.29 274	99.68 265	88.41 386	98.62 330	96.60 391

thres100view900		94.19 338	93.67 342	95.75 356	99.06 213	91.35 366	98.03 169	94.24 386	98.33 131	97.40 307	94.98 387	79.84 381	99.62 293	83.05 399	98.08 351	96.29 392

tfpn200view9		94.03 342	93.44 344	95.78 355	98.93 233	91.44 364	97.60 227	94.29 384	97.94 164	97.10 316	94.31 394	79.67 383	99.62 293	83.05 399	98.08 351	96.29 392

MVS		93.19 354	92.09 358	96.50 336	96.91 390	94.03 315	98.07 163	98.06 325	68.01 407	94.56 384	96.48 357	95.96 222	99.30 369	83.84 398	96.89 381	96.17 394

gg-mvs-nofinetune		92.37 365	91.20 369	95.85 353	95.80 406	92.38 353	99.31 27	81.84 412	99.75 6	91.83 401	99.74 13	68.29 399	99.02 385	87.15 390	97.12 377	96.16 395

xiu_mvs_v2_base		97.16 261	97.49 229	96.17 348	98.54 309	92.46 350	95.45 352	98.84 275	97.25 229	97.48 301	96.49 356	98.31 70	99.90 64	96.34 242	98.68 325	96.15 396

PS-MVSNAJ		97.08 265	97.39 234	96.16 350	98.56 307	92.46 350	95.24 359	98.85 274	97.25 229	97.49 300	95.99 365	98.07 88	99.90 64	96.37 239	98.67 326	96.12 397

E-PMN		94.17 339	94.37 334	93.58 382	96.86 391	85.71 399	90.11 404	97.07 347	98.17 150	97.82 277	97.19 344	84.62 361	98.94 389	89.77 382	97.68 360	96.09 398

EMVS		93.83 345	94.02 337	93.23 386	96.83 393	84.96 400	89.77 405	96.32 364	97.92 166	97.43 306	96.36 362	86.17 348	98.93 390	87.68 389	97.73 359	95.81 399

MVE	83.40 22	92.50 362	91.92 364	94.25 374	98.83 255	91.64 361	92.71 397	83.52 411	95.92 296	86.46 409	95.46 379	95.20 247	95.40 407	80.51 404	98.64 327	95.73 400
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres200		93.72 347	93.14 349	95.46 364	98.66 293	91.29 368	96.61 296	94.63 381	97.39 215	96.83 334	93.71 397	79.88 380	99.56 315	82.40 402	98.13 348	95.54 401

API-MVS		97.04 268	96.91 260	97.42 300	97.88 354	98.23 123	98.18 149	98.50 306	97.57 192	97.39 309	96.75 352	96.77 179	99.15 382	90.16 381	99.02 298	94.88 402

GG-mvs-BLEND				94.76 370	94.54 408	92.13 358	99.31 27	80.47 413		88.73 407	91.01 407	67.59 402	98.16 401	82.30 403	94.53 400	93.98 403

DeepMVS_CX				93.44 384	98.24 334	94.21 308		94.34 383	64.28 408	91.34 402	94.87 391	89.45 329	92.77 409	77.54 407	93.14 402	93.35 404

tmp_tt		78.77 375	78.73 378	78.90 391	58.45 416	74.76 415	94.20 385	78.26 414	39.16 409	86.71 408	92.82 403	80.50 379	75.19 411	86.16 395	92.29 404	86.74 405

dongtai		76.24 376	75.95 379	77.12 392	92.39 410	67.91 416	90.16 403	59.44 417	82.04 403	89.42 405	94.67 392	49.68 415	81.74 410	48.06 410	77.66 408	81.72 406

kuosan		69.30 377	68.95 380	70.34 393	87.68 414	65.00 417	91.11 401	59.90 416	69.02 406	74.46 411	88.89 408	48.58 416	68.03 412	28.61 411	72.33 410	77.99 407

wuyk23d		96.06 302	97.62 222	91.38 388	98.65 296	98.57 95	98.85 84	96.95 352	96.86 256	99.90 12	99.16 123	99.18 17	98.40 398	89.23 385	99.77 125	77.18 408

test123		17.04 380	20.11 383	7.82 394	10.25 418	4.91 419	94.80 369	4.47 419	4.93 412	10.00 414	24.28 411	9.69 417	3.64 413	10.14 412	12.43 412	14.92 409

testmvs		17.12 379	20.53 382	6.87 395	12.05 417	4.20 420	93.62 394	6.73 418	4.62 413	10.41 413	24.33 410	8.28 418	3.56 414	9.69 413	15.07 411	12.86 410

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

cdsmvs_eth3d_5k		24.66 378	32.88 381	0.00 396	0.00 419	0.00 421	0.00 407	99.10 229	0.00 414	0.00 415	97.58 327	99.21 16	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		8.17 381	10.90 384	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	98.07 88	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

ab-mvs-re		8.12 382	10.83 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	97.48 333	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						90.90 375								91.37 369

FOURS1					99.73 37	99.67 2	99.43 11	99.54 78	99.43 40	99.26 111

test_one_0601					99.39 139	99.20 34		99.31 165	98.49 125	98.66 202	99.02 153	97.64 120

eth-test2					0.00 419

eth-test					0.00 419

ZD-MVS					99.01 221	98.84 75		99.07 233	94.10 342	98.05 260	98.12 293	96.36 201	99.86 107	92.70 352	99.19 277

test_241102_ONE					99.49 115	99.17 39		99.31 165	97.98 160	99.66 43	98.90 189	98.36 65	99.48 340

9.14			97.78 207		99.07 209		97.53 236	99.32 160	95.53 307	98.54 222	98.70 228	97.58 125	99.76 223	94.32 312	99.46 235

save fliter					99.11 200	97.97 151	96.53 299	99.02 245	98.24 140

test0726					99.50 108	99.21 28	98.17 152	99.35 147	97.97 161	99.26 111	99.06 141	97.61 123

test_part2					99.36 147	99.10 60				99.05 140

sam_mvs												84.29 366

MTGPA								99.20 204

test_post1							97.59 229				20.48 413	83.07 372	99.66 279	94.16 313

test_post											21.25 412	83.86 368	99.70 252

patchmatchnet-post											98.77 217	84.37 363	99.85 119

MTMP							97.93 182	91.91 398

gm-plane-assit					94.83 407	81.97 410			88.07 394		94.99 386		99.60 300	91.76 361

TEST9					98.71 275	98.08 138	95.96 331	99.03 242	91.40 375	95.85 361	97.53 329	96.52 192	99.76 223

test_8					98.67 288	98.01 146	95.91 336	99.02 245	91.64 370	95.79 363	97.50 332	96.47 194	99.76 223

agg_prior					98.68 287	97.99 147		99.01 248		95.59 364			99.77 217

test_prior4						97.97 151	95.86 337

test_prior2							95.74 342		96.48 273	96.11 356	97.63 325	95.92 224		94.16 313	99.20 274

旧先验2							95.76 341		88.56 393	97.52 297			99.66 279	94.48 303

新几何2							95.93 334

原ACMM2							95.53 348

testdata2													99.79 200	92.80 349

segment_acmp												97.02 164

testdata1							95.44 353		96.32 279

plane_prior7					99.19 181	97.87 160

plane_prior6					98.99 225	97.70 179						94.90 254

plane_prior4											97.98 304

plane_prior3						97.78 172			97.41 213	97.79 278

plane_prior2							97.77 204		98.20 147

plane_prior1					99.05 216

plane_prior						97.65 181	97.07 271		96.72 263						99.36 248

n2								0.00 420

nn								0.00 420

door-mid								99.57 62

test11								98.87 266

door								99.41 126

HQP5-MVS						96.79 229

HQP-NCC					98.67 288		96.29 313		96.05 289	95.55 367

ACMP_Plane					98.67 288		96.29 313		96.05 289	95.55 367

BP-MVS														92.82 347

HQP3-MVS								99.04 240							99.26 266

HQP2-MVS												93.84 282

NP-MVS					98.84 253	97.39 197					96.84 350

MDTV_nov1_ep13			95.22 320		97.06 389	83.20 407	97.74 209	96.16 365	94.37 336	96.99 323	98.83 206	83.95 367	99.53 325	93.90 322	97.95 357

ACMMP++_ref															99.77 125

ACMMP++															99.68 168

Test By Simon												96.52 192

High-resolution multi-view benchmark